TY - CHAP AU - Karl, Holger AU - Maack, Marten AU - Meyer auf der Heide, Friedhelm AU - Pukrop, Simon AU - Redder, Adrian ED - Haake, Claus-Jochen ED - Meyer auf der Heide, Friedhelm ED - Platzner, Marco ED - Wachsmuth, Henning ED - Wehrheim, Heike ID - 45895 T2 - On-The-Fly Computing -- Individualized IT-services in dynamic markets TI - On-The-Fly Compute Centers II: Execution of Composed Services in Configurable Compute Centers VL - 412 ER - TY - CONF AB - Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive results. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field. To this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of wireless mobile networks. AU - Schneider, Stefan Balthasar AU - Werner, Stefan AU - Khalili, Ramin AU - Hecker, Artur AU - Karl, Holger ID - 30236 KW - wireless mobile networks KW - network management KW - continuous control KW - cognitive networks KW - autonomous coordination KW - reinforcement learning KW - gym environment KW - simulation KW - open source T2 - IEEE/IFIP Network Operations and Management Symposium (NOMS) TI - mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks ER - TY - CONF AB - The decentralized nature of multi-agent systems requires continuous data exchange to achieve global objectives. In such scenarios, Age of Information (AoI) has become an important metric of the freshness of exchanged data due to the error-proneness and delays of communication systems. Communication systems usually possess dependencies: the process describing the success or failure of communication is highly correlated when these attempts are ``close'' in some domain (e.g. in time, frequency, space or code as in wireless communication) and is, in general, non-stationary. To study AoI in such scenarios, we consider an abstract event-based AoI process $\Delta(n)$, expressing time since the last update: If, at time $n$, a monitoring node receives a status update from a source node (event $A(n-1)$ occurs), then $\Delta(n)$ is reset to one; otherwise, $\Delta(n)$ grows linearly in time. This AoI process can thus be viewed as a special random walk with resets. The event process $A(n)$ may be nonstationary and we merely assume that its temporal dependencies decay sufficiently, described by $\alpha$-mixing. We calculate moment bounds for the resulting AoI process as a function of the mixing rate of $A(n)$. Furthermore, we prove that the AoI process $\Delta(n)$ is itself $\alpha$-mixing from which we conclude a strong law of large numbers for $\Delta(n)$. These results are new, since AoI processes have not been studied so far in this general strongly mixing setting. This opens up future work on renewal processes with non-independent interarrival times. AU - Redder, Adrian AU - Ramaswamy, Arunselvan AU - Karl, Holger ID - 32811 T2 - Proceedings of the 58th Allerton Conference on Communication, Control, and Computing TI - Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law ER - TY - CONF AU - Redder, Adrian AU - Ramaswamy, Arunselvan AU - Karl, Holger ID - 30793 T2 - Proceedings of the 14th International Conference on Agents and Artificial Intelligence TI - Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication ER - TY - GEN AB - Iterative distributed optimization algorithms involve multiple agents that communicate with each other, over time, in order to minimize/maximize a global objective. In the presence of unreliable communication networks, the Age-of-Information (AoI), which measures the freshness of data received, may be large and hence hinder algorithmic convergence. In this paper, we study the convergence of general distributed gradient-based optimization algorithms in the presence of communication that neither happens periodically nor at stochastically independent points in time. We show that convergence is guaranteed provided the random variables associated with the AoI processes are stochastically dominated by a random variable with finite first moment. This improves on previous requirements of boundedness of more than the first moment. We then introduce stochastically strongly connected (SSC) networks, a new stochastic form of strong connectedness for time-varying networks. We show: If for any $p \ge0$ the processes that describe the success of communication between agents in a SSC network are $\alpha$-mixing with $n^{p-1}\alpha(n)$ summable, then the associated AoI processes are stochastically dominated by a random variable with finite $p$-th moment. In combination with our first contribution, this implies that distributed stochastic gradient descend converges in the presence of AoI, if $\alpha(n)$ is summable. AU - Redder, Adrian AU - Ramaswamy, Arunselvan AU - Karl, Holger ID - 30790 T2 - arXiv:2201.11343 TI - Distributed gradient-based optimization in the presence of dependent aperiodic communication ER - TY - GEN AB - We present sufficient conditions that ensure convergence of the multi-agent Deep Deterministic Policy Gradient (DDPG) algorithm. It is an example of one of the most popular paradigms of Deep Reinforcement Learning (DeepRL) for tackling continuous action spaces: the actor-critic paradigm. In the setting considered herein, each agent observes a part of the global state space in order to take local actions, for which it receives local rewards. For every agent, DDPG trains a local actor (policy) and a local critic (Q-function). The analysis shows that multi-agent DDPG using neural networks to approximate the local policies and critics converge to limits with the following properties: The critic limits minimize the average squared Bellman loss; the actor limits parameterize a policy that maximizes the local critic's approximation of $Q_i^*$, where $i$ is the agent index. The averaging is with respect to a probability distribution over the global state-action space. It captures the asymptotics of all local training processes. Finally, we extend the analysis to a fully decentralized setting where agents communicate over a wireless network prone to delays and losses; a typical scenario in, e.g., robotic applications. AU - Redder, Adrian AU - Ramaswamy, Arunselvan AU - Karl, Holger ID - 30791 T2 - arXiv:2201.00570 TI - Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms ER - TY - JOUR AU - Redder, Adrian AU - Ramaswamy, Arunselvan AU - Karl, Holger ID - 32854 IS - 13 JF - IFAC-PapersOnLine TI - Practical Network Conditions for the Convergence of Distributed Optimization VL - 55 ER - TY - CONF AB - Modern services often comprise several components, such as chained virtual network functions, microservices, or machine learning functions. Providing such services requires to decide how often to instantiate each component, where to place these instances in the network, how to chain them and route traffic through them. To overcome limitations of conventional, hardwired heuristics, deep reinforcement learning (DRL) approaches for self-learning network and service management have emerged recently. These model-free DRL approaches are more flexible but typically learn tabula rasa, i.e., disregard existing understanding of networks, services, and their coordination. Instead, we propose FutureCoord, a novel model-based AI approach that leverages existing understanding of networks and services for more efficient and effective coordination without time-intensive training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic model. This allows FutureCoord to estimate the impact of future incoming traffic and effectively optimize long-term effects, taking fluctuating demand and Quality of Service (QoS) requirements into account. Our extensive evaluation based on real-world network topologies, services, and traffic traces indicates that FutureCoord clearly outperforms state-of-the-art model-free and model-based approaches with up to 51% higher flow success ratios. AU - Werner, Stefan AU - Schneider, Stefan Balthasar AU - Karl, Holger ID - 29220 KW - network management KW - service management KW - AI KW - Monte Carlo Tree Search KW - model-based KW - QoS T2 - IEEE/IFIP Network Operations and Management Symposium (NOMS) TI - Use What You Know: Network and Service Coordination Beyond Certainty ER - TY - CONF AB - Using Service Function Chaining (SFC) in wireless networks became popular in many domains like networking and multimedia. It relies on allocating network resources to incoming SFCs requests, via a Virtual Network Embedding (VNE) algorithm, so that it optimizes the performance of the SFC. When the load of incoming requests -- competing for the limited network resources -- increases, it becomes challenging to decide which requests should be admitted and which one should be rejected. In this work, we propose a deep Reinforcement learning (RL) solution that can learn the admission policy for different dependencies, such as the service lifetime and the priority of incoming requests. We compare the deep RL solution to a first-come-first-serve baseline that admits a request whenever there are available resources. We show that deep RL outperforms the baseline and provides higher acceptance rate with low rejections even when there are enough resources. AU - Afifi, Haitham AU - Sauer, Fabian Jakob AU - Karl, Holger ID - 25278 KW - reinforcement learning KW - admission control KW - wireless sensor networks T2 - 2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS'21) TI - Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding ER - TY - CONF AB - Wireless Acoustic Sensor Networks (WASNs) have a wide range of audio signal processing applications. Due to the spatial diversity of the microphone and their relative position to the acoustic source, not all microphones are equally useful for subsequent audio signal processing tasks, nor do they all have the same wireless data transmission rates. Hence, a central task in WASNs is to balance a microphone’s estimated acoustic utility against its transmission delay, selecting a best-possible subset of microphones to record audio signals. In this work, we use reinforcement learning to decide if a microphone should be used or switched off to maximize the acoustic quality at low transmission delays, while minimizing switching frequency. In experiments with moving sources in a simulated acoustic environment, our method outperforms naive baseline comparisons AU - Afifi, Haitham AU - Guenther, Michael AU - Brendel, Andreas AU - Karl, Holger AU - Kellermann, Walter ID - 25281 KW - microphone utility KW - microphone selection KW - wireless acoustic sensor network KW - network delay KW - reinforcement learning T2 - 14. ITG Conference on Speech Communication (ITG 2021) TI - Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering Network and Acoustic Utilities ER - TY - CONF AU - Gunther, Michael AU - Afifi, Haitham AU - Brendel, Andreas AU - Karl, Holger AU - Kellermann, Walter ID - 25293 T2 - ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) TI - Network-Aware Optimal Microphone Channel Selection in Wireless Acoustic Sensor Networks ER - TY - CONF AB - Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling heuristics under varying network load. AU - Hasnain, Asif AU - Karl, Holger ID - 20125 KW - Flow scheduling KW - Deadlines KW - Reinforcement learning T2 - 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) TI - Learning Flow Scheduling ER - TY - CONF AB - Data-parallel applications are developed using different data programming models, e.g., MapReduce, partition/aggregate. These models represent diverse resource requirements of application in a datacenter network, which can be represented by the coflow abstraction. The conventional method of creating hand-crafted coflow heuristics for admission or scheduling for different workloads is practically infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based coflow admission scheme -- LCS -- that can learn an admission policy for a higher-level performance objective, i.e., maximize successful coflow admissions, without manual feature engineering. LCS is trained on a production trace, which has online coflow arrivals. The evaluation results show that LCS is able to learn a reasonable admission policy that admits more coflows than state-of-the-art Varys heuristic while meeting their deadlines. AU - Hasnain, Asif AU - Karl, Holger ID - 21005 KW - Coflow scheduling KW - Reinforcement learning KW - Deadlines T2 - IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) TI - Learning Coflow Admissions ER - TY - CONF AB - In this work we use autonomous vehicles to improve the performance of Wireless Sensor Networks (WSNs). In contrast to other autonomous vehicle applications, WSNs have two metrics for performance evaluation. First, quality of information (QoI) which is used to measure the quality of sensed data (e.g., measurement uncertainties or signal strength). Second, quality of service (QoS) which is used to measure the network's performance for data forwarding (e.g., delay and packet losses). As a use case, we consider wireless acoustic sensor networks, where a group of speakers move inside a room and there are autonomous vehicles installed with microphones for streaming the audio data. We formulate the problem as a Markov decision problem (MDP) and solve it using Deep-Q-Networks (DQN). Additionally, we compare the performance of DQN solution to two different real-world implementations: speakers holding/passing microphones and microphones being preinstalled in fixed positions. We show that the performance of autonomous vehicles in terms of QoI and QoS is better than the real-world implementation in some scenarios. Moreover, we study the impact of the vehicles speed on the learning process of the DQN solution and show how low speeds degrade the performance. Finally, we compare the DQN solution to a heuristic one and provide theoretical analysis of the performance with respect to dynamic WSNs. AU - Afifi, Haitham AU - Ramaswamy, Arunselvan AU - Karl, Holger ID - 21478 T2 - 2021 IEEE International Conference on Communications (ICC): IoT and Sensor Networks Symposium (IEEE ICC'21 - IoTSN Symposium) TI - Reinforcement Learning for Autonomous Vehicle Movements in Wireless Sensor Networks ER - TY - CONF AB - Two of the most important metrics when developing Wireless Sensor Networks (WSNs) applications are the Quality of Information (QoI) and Quality of Service (QoS). The former is used to specify the quality of the collected data by the sensors (e.g., measurements error or signal's intensity), while the latter defines the network's performance and availability (e.g., packet losses and latency). In this paper, we consider an example of wireless acoustic sensor networks, where we select a subset of microphones for two different objectives. First, we maximize the recording quality under QoS constraints. Second, we apply a trade-off between QoI and QoS. We formulate the problem as a constrained Markov Decision Problem (MDP) and solve it using reinforcement learning (RL). We compare the RL solution to a baseline model and show that in case of QoS-guarantee objective, the RL solution has an optimality gap up to 1\%. Meanwhile, the RL solution is better than the baseline with improvements up to 23\%, when using the trade-off objective. AU - Afifi, Haitham AU - Ramaswamy, Arunselvan AU - Karl, Holger ID - 21479 KW - reinforcement learning KW - wireless sensor networks KW - resource allocation KW - acoustic sensor networks T2 - 2021 IEEE 18th Annual Consumer Communications \& Networking Conference (CCNC) (CCNC 2021) TI - A Reinforcement Learning QoI/QoS-Aware Approach in Acoustic Sensor Networks ER - TY - CONF AB - Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks. To address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms). AU - Schneider, Stefan Balthasar AU - Qarawlus, Haydar AU - Karl, Holger ID - 21543 KW - network management KW - service management KW - coordination KW - reinforcement learning KW - distributed T2 - IEEE International Conference on Distributed Computing Systems (ICDCS) TI - Distributed Online Service Coordination Using Deep Reinforcement Learning ER - TY - CONF AB - In practical, large-scale networks, services are requested by users across the globe, e.g., for video streaming. Services consist of multiple interconnected components such as microservices in a service mesh. Coordinating these services requires scaling them according to continuously changing user demand, deploying instances at the edge close to their users, and routing traffic efficiently between users and connected instances. Network and service coordination is commonly addressed through centralized approaches, where a single coordinator knows everything and coordinates the entire network globally. While such centralized approaches can reach global optima, they do not scale to large, realistic networks. In contrast, distributed approaches scale well, but sacrifice solution quality due to their limited scope of knowledge and coordination decisions. To this end, we propose a hierarchical coordination approach that combines the good solution quality of centralized approaches with the scalability of distributed approaches. In doing so, we divide the network into multiple hierarchical domains and optimize coordination in a top-down manner. We compare our hierarchical with a centralized approach in an extensive evaluation on a real-world network topology. Our results indicate that hierarchical coordination can find close-to-optimal solutions in a fraction of the runtime of centralized approaches. AU - Schneider, Stefan Balthasar AU - Jürgens, Mirko AU - Karl, Holger ID - 20693 KW - network management KW - service management KW - coordination KW - hierarchical KW - scalability KW - nfv T2 - IFIP/IEEE International Symposium on Integrated Network Management (IM) TI - Divide and Conquer: Hierarchical Network and Service Coordination ER - TY - JOUR AB - Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available. AU - Schneider, Stefan Balthasar AU - Khalili, Ramin AU - Manzoor, Adnan AU - Qarawlus, Haydar AU - Schellenberg, Rafael AU - Karl, Holger AU - Hecker, Artur ID - 21808 JF - Transactions on Network and Service Management KW - network management KW - service management KW - coordination KW - reinforcement learning KW - self-learning KW - self-adaptation KW - multi-objective TI - Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning ER - TY - GEN AB - Macrodiversity is a key technique to increase the capacity of mobile networks. It can be realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple overlapping cells. Selecting which users to serve by how many and which cells is NP-hard but needs to happen continuously in real time as users move and channel state changes. Existing approaches often require strict assumptions about or perfect knowledge of the underlying radio system, its resource allocation scheme, or user movements, none of which is readily available in practice. Instead, we propose three novel self-learning and self-adapting approaches using model-free deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages central observations and control of all users to select cells almost optimally. DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and highly scalable coordination. All three approaches learn from experience and self-adapt to varying scenarios, reaching 2x higher Quality of Experience than other approaches. They have very few built-in assumptions and do not need prior system knowledge, making them more robust to change and better applicable in practice than existing approaches. AU - Schneider, Stefan Balthasar AU - Karl, Holger AU - Khalili, Ramin AU - Hecker, Artur ID - 33854 KW - mobility management KW - coordinated multipoint KW - CoMP KW - cell selection KW - resource management KW - reinforcement learning KW - multi agent KW - MARL KW - self-learning KW - self-adaptation KW - QoE TI - DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning ER - TY - CONF AB - Modern services consist of modular, interconnected components, e.g., microservices forming a service mesh. To dynamically adjust to ever-changing service demands, service components have to be instantiated on nodes across the network. Incoming flows requesting a service then need to be routed through the deployed instances while considering node and link capacities. Ultimately, the goal is to maximize the successfully served flows and Quality of Service (QoS) through online service coordination. Current approaches for service coordination are usually centralized, assuming up-to-date global knowledge and making global decisions for all nodes in the network. Such global knowledge and centralized decisions are not realistic in practical large-scale networks. To solve this problem, we propose two algorithms for fully distributed service coordination. The proposed algorithms can be executed individually at each node in parallel and require only very limited global knowledge. We compare and evaluate both algorithms with a state-of-the-art centralized approach in extensive simulations on a large-scale, real-world network topology. Our results indicate that the two algorithms can compete with centralized approaches in terms of solution quality but require less global knowledge and are magnitudes faster (more than 100x). AU - Schneider, Stefan Balthasar AU - Klenner, Lars Dietrich AU - Karl, Holger ID - 19607 KW - distributed management KW - service coordination KW - network coordination KW - nfv KW - softwarization KW - orchestration T2 - IEEE International Conference on Network and Service Management (CNSM) TI - Every Node for Itself: Fully Distributed Service Coordination ER - TY - CONF AB - Modern services comprise interconnected components, e.g., microservices in a service mesh, that can scale and run on multiple nodes across the network on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities and changing demands into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, it significantly improves flow throughput and overall network utility on real-world network topologies and traffic traces. It also learns to optimize different objectives, generalizes to scenarios with unseen, stochastic traffic patterns, and scales to large real-world networks. AU - Schneider, Stefan Balthasar AU - Manzoor, Adnan AU - Qarawlus, Haydar AU - Schellenberg, Rafael AU - Karl, Holger AU - Khalili, Ramin AU - Hecker, Artur ID - 19609 KW - self-driving networks KW - self-learning KW - network coordination KW - service coordination KW - reinforcement learning KW - deep learning KW - nfv T2 - IEEE International Conference on Network and Service Management (CNSM) TI - Self-Driving Network and Service Coordination Using Deep Reinforcement Learning ER - TY - CONF AB - Upcoming sensing applications (acoustic or video) will have high processing requirements not satisfiable by a single node or need input from multiple sources (e.g., speaker localization). Offloading these applications to cloud or mobile edge is an option, but when running in a wireless senor network (WSN), it might entail needlessly high data rate and latency. An alternative is to spread processing inside the WSN, which is particularly attractive if the application comprises individual components. This scenario is typical for applications like acoustic signal processing. Mapping components to nodes can be formulated as wireless version of the NP-hard Virtual Network Embedding (VNE) problem, for which various heuristics exist. We propose a Reinforcement Learning (RL) framework, which relies on Q-Learning and uses either Greedy Epsilon or Epsilon Decay for exploration. We compare both exploration methods to the result of an optimization approach and show empirically that the RL framework achieves good results in terms of network delay within few number of steps. AU - Afifi, Haitham AU - Karl, Holger ID - 20164 T2 - 2020 Thirteenth International Workshop on Selected Topics in Mobile and Wireless Computing (STWiMob'2020) TI - Reinforcement Learning for Virtual Network Embedding in Wireless Sensor Networks ER - TY - CONF AU - Razzaghi Kouchaksaraei, Hadi AU - Shivarpatna Venkatesh, Ashwin Prasad AU - Churi, Amey AU - Illian, Marvin AU - Karl, Holger ID - 16726 T2 - European Conference on Networks and Communications (EUCNC 2020) TI - Dynamic Provisioning of Network Services on Heterogeneous Resources ER - TY - CONF AB - Data-parallel applications run on cluster of servers in a datacenter and their communication triggers correlated resource demand on multiple links that can be abstracted as coflow. They often desire predictable network performance, which can be passed to network via coflow abstraction for application-aware network scheduling. In this paper, we propose a heuristic and an optimization algorithm for predictable network performance such that they guarantee coflows completion within their deadlines. The algorithms also ensure high network utilization, i.e., it's work-conserving, and avoids starvation of coflows. We evaluate both algorithms via trace-driven simulation and show that they admit 1.1x more coflows than the Varys scheme while meeting their deadlines. AU - Hasnain, Asif AU - Karl, Holger ID - 17082 KW - Coflow KW - Scheduling KW - Deadlines KW - Data centers T2 - 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID) TI - Coflow Scheduling with Performance Guarantees for Data Center Applications ER - TY - CONF AB - Network function virtualization (NFV) proposes to replace physical middleboxes with more flexible virtual network functions (VNFs). To dynamically adjust to everchanging traffic demands, VNFs have to be instantiated and their allocated resources have to be adjusted on demand. Deciding the amount of allocated resources is non-trivial. Existing optimization approaches often assume fixed resource requirements for each VNF instance. However, this can easily lead to either waste of resources or bad service quality if too many or too few resources are allocated. To solve this problem, we train machine learning models on real VNF data, containing measurements of performance and resource requirements. For each VNF, the trained models can then accurately predict the required resources to handle a certain traffic load. We integrate these machine learning models into an algorithm for joint VNF scaling and placement and evaluate their impact on resulting VNF placements. Our evaluation based on real-world data shows that using suitable machine learning models effectively avoids over- and underallocation of resources, leading to up to 12 times lower resource consumption and better service quality with up to 4.5 times lower total delay than using standard fixed resource allocation. AU - Schneider, Stefan Balthasar AU - Satheeschandran, Narayanan Puthenpurayil AU - Peuster, Manuel AU - Karl, Holger ID - 16219 T2 - IEEE Conference on Network Softwarization (NetSoft) TI - Machine Learning for Dynamic Resource Allocation in Network Function Virtualization ER - TY - CONF AU - Zafeiropoulos, A. AU - Fotopoulou, E. AU - Peuster, Manuel AU - Schneider, Stefan Balthasar AU - Gouvas, P. AU - Behnke, D. AU - Müller, M. AU - Bök, P. AU - Trakadas, P. AU - Karkazis, P. AU - Karl, Holger ID - 16222 T2 - IEEE Conference on Network Softwarization (NetSoft) TI - Benchmarking and Profiling 5G Verticals' Applications: An Industrial IoT Use Case ER - TY - JOUR AB - Currently, the coexistence of multiple users and devices challenges the network's ability to reliably connect them. This article proposes a novel communication architecture that satisfies the requirements of fifth-generation (5G) mobile network applications. In particular, this architecture extends and combines ultra-dense networking (UDN), multi-access edge computing (MEC), and virtual infrastructure manager (VIM) concepts to provide a flexible network of moving radio access (RA) nodes, flying or moving to areas where users and devices struggle for connectivity and data rate. Furthermore, advances in radio communications and non-orthogonal multiple access (NOMA), virtualization technologies and energy-awareness mechanisms are integrated towards a mobile UDN that not only allows RA nodes to follow the user but also enables the virtualized network functions (VNFs) to adapt to user mobility by migrating from one node to another. Performance evaluation shows that the underlying network improves connectivity of users and devices through the flexible deployment of moving RA nodes and the use of NOMA. AU - Nomikos, Nikolaos AU - Michailidis, Emmanouel T. AU - Trakadas, Panagiotis AU - Vouyioukas, Demosthenes AU - Karl, Holger AU - Martrat, Josep AU - Zahariadis, Theodore AU - Papadopoulos, Konstantinos AU - Voliotis, Stamatis ID - 16278 JF - Vehicular Communications SN - 2214-2096 TI - A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices ER - TY - JOUR AB - Assigning bands of the wireless spectrum as resources to users is a common problem in wireless networks. Typically, frequency bands were assumed to be available in a stable manner. Nevertheless, in recent scenarios where wireless networks may be deployed in unknown environments, spectrum competition is considered, making it uncertain whether a frequency band is available at all or at what quality. To fully exploit such resources with uncertain availability, the multi-armed bandit (MAB) method, a representative online learning technique, has been applied to design spectrum scheduling algorithms. This article surveys such proposals. We describe the following three aspects: how to model spectrum scheduling problems within the MAB framework, what the main thread is following which prevalent algorithms are designed, and how to evaluate algorithm performance and complexity. We also give some promising directions for future research in related fields. AU - Li, Feng AU - Yu, Dongxiao AU - Yang, Huan AU - Yu, Jiguo AU - Karl, Holger AU - Cheng, Xiuzhen ID - 16280 JF - IEEE Wireless Communications SN - 1536-1284 TI - Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey ER - TY - CONF AB - Softwarization facilitates the introduction of smart manufacturing applications in the industry. Manifold devices such as machine computers, Industrial IoT devices, tablets, smartphones and smart glasses are integrated into factory networks to enable shop floor digitalization and big data analysis. To handle the increasing number of devices and the resulting traffic, a flexible and scalable factory network is necessary which can be realized using softwarization technologies like Network Function Virtualization (NFV). However, the security risks increase with the increasing number of new devices, so that cyber security must also be considered in NFV-based networks. Therefore, extending our previous work, we showcase threat detection using a cloud-native NFV-driven intrusion detection system (IDS) that is integrated in our industrial-specific network services. As a result of the threat detection, the affected network service is put into quarantine via automatic network reconfiguration. We use the 5GTANGO service platform to deploy our developed network services on Kubernetes and to initiate the network reconfiguration. AU - Müller, Marcel AU - Behnke, Daniel AU - Bök, Patrick-Benjamin AU - Schneider, Stefan Balthasar AU - Peuster, Manuel AU - Karl, Holger ID - 16400 T2 - IEEE Conference on Network Softwarization (NetSoft) Demo Track TI - Cloud-Native Threat Detection and Containment for Smart Manufacturing ER - TY - JOUR AU - Karl, Holger AU - Kundisch, Dennis AU - Meyer auf der Heide, Friedhelm AU - Wehrheim, Heike ID - 13770 IS - 6 JF - Business & Information Systems Engineering TI - A Case for a New IT Ecosystem: On-The-Fly Computing VL - 62 ER - TY - CONF AB - For optimal placement and orchestration of network services, it is crucial that their structure and semantics are specified clearly and comprehensively and are available to an orchestrator. Existing specification approaches are either ambiguous or miss important aspects regarding the behavior of virtual network functions (VNFs) forming a service. We propose to formally and unambiguously specify the behavior of these functions and services using Queuing Petri Nets (QPNs). QPNs are an established method that allows to express queuing, synchronization, stochastically distributed processing delays, and changing traffic volume and characteristics at each VNF. With QPNs, multiple VNFs can be connected to complete network services in any structure, even specifying bidirectional network services containing loops. We discuss how management and orchestration systems can benefit from our clear and comprehensive specification approach, leading to better placement of VNFs and improved Quality of Service. Another benefit of formally specifying network services with QPNs are diverse analysis options, which allow valuable insights such as the distribution of end-to-end delay. We propose a tool-based workflow that supports the specification of network services and the automatic generation of corresponding simulation code to enable an in-depth analysis of their behavior and performance. AU - Schneider, Stefan Balthasar AU - Sharma, Arnab AU - Karl, Holger AU - Wehrheim, Heike ID - 3287 T2 - 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM) TI - Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets ER - TY - CONF AB - As 5G and network function virtualization (NFV) are maturing, it becomes crucial to demonstrate their feasibility and benefits by means of vertical scenarios. While 5GPPP has identified smart manufacturing as one of the most important vertical industries, there is still a lack of specific, practical use cases. Using the experience from a large-scale manufacturing company, Weidm{\"u}ller Group, we present a detailed use case that reflects the needs of real-world manufacturers. We also propose an architecture with specific network services and virtual network functions (VNFs) that realize the use case in practice. As a proof of concept, we implement the required services and deploy them on an emulation-based prototyping platform. Our experimental results indicate that a fully virtualized smart manufacturing use case is not only feasible but also reduces machine interconnection and configuration time and thus improves productivity by orders of magnitude. AU - Schneider, Stefan Balthasar AU - Peuster, Manuel AU - Behnke, Daniel AU - Marcel, Müller AU - Bök, Patrick-Benjamin AU - Karl, Holger ID - 9270 KW - 5g KW - vertical KW - smart manufacturing KW - nfv T2 - European Conference on Networks and Communications (EuCNC) TI - Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario ER - TY - JOUR AB - The ongoing softwarization of networks creates a big need for automated testing solutions to ensure service quality. This becomes even more important if agile environments with short time to market and high demands, in terms of service performance and availability, are considered. In this paper, we introduce a novel testing solution for virtualized, microservice-based network functions and services, which we base on TTCN-3, a well known testing language defined by the European standards institute (ETSI). We use TTCN-3 not only for functional testing but also answer the question whether TTCN-3 can be used for performance profiling tasks as well. Finally, we demonstrate the proposed concepts and solutions in a case study using our open-source prototype to test and profile a chained network service. AU - Peuster, Manuel AU - Dröge, Christian AU - Boos, Clemens AU - Karl, Holger ID - 8113 JF - ICT Express SN - 2405-9595 TI - Joint testing and profiling of microservice-based network services using TTCN-3 ER - TY - CONF AU - Dräxler, Sevil AU - Karl, Holger ID - 8240 T2 - 5th IEEE International Conference on Network Softwarization (NetSoft) 2019 TI - SPRING: Scaling, Placement, and Routing of Heterogeneous Services with Flexible Structures ER - TY - CONF AB - 5G together with software defined networking (SDN) and network function virtualisation (NFV) will enable a wide variety of vertical use cases. One of them is the smart man- ufacturing case which utilises 5G networks to interconnect production machines, machine parks, and factory sites to enable new possibilities in terms of flexibility, automation, and novel applications (industry 4.0). However, the availability of realistic and practical proof-of-concepts for those smart manufacturing scenarios is still limited. This demo fills this gap by not only showing a real-world smart manufacturing application entirely implemented using NFV concepts, but also a lightweight prototyping framework that simplifies the realisation of vertical NFV proof-of-concepts. Dur- ing the demo, we show how an NFV-based smart manufacturing scenario can be specified, on-boarded, and instantiated before we demonstrate how the presented NFV services simplify machine data collection, aggregation, and analysis. AU - Peuster, Manuel AU - Schneider, Stefan Balthasar AU - Behnke, Daniel AU - Müller, Marcel AU - Bök, Patrick-Benjamin AU - Karl, Holger ID - 8792 T2 - 5th IEEE International Conference on Network Softwarization (NetSoft 2019) TI - Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case ER - TY - JOUR AB - Softwarized networks are the key enabler for elastic, on-demand service deployments of virtualized network functions. They allow to dynamically steer traffic through the network when new network functions are instantiated, or old ones are terminated. These scenarios become in particular challenging when stateful functions are involved, necessitating state management solutions to migrate state between the functions. The problem with existing solutions is that they typically embrace state migration and flow rerouting jointly, imposing a huge set of requirements on the on-boarded virtualized network functions (VNFs), eg, solution-specific state management interfaces. To change this, we introduce the seamless handover protocol (SHarP). An easy-to-use, loss-less, and order-preserving flow rerouting mechanism that is not fixed to a single state management approach. Using SHarP, VNF vendors are empowered to implement or use the state management solution of their choice. SHarP supports these solutions with additional information when flows are migrated. In this paper, we present SHarP's design, its open source prototype implementation, and show how SHarP significantly reduces the buffer usage at a central (SDN) controller, which is a typical bottleneck in state-of-the-art solutions. Our experiments show that SHarP uses a constant amount of controller buffer, irrespective of the time taken to migrate the VNF state. AU - Peuster, Manuel AU - Küttner, Hannes AU - Karl, Holger ID - 8795 JF - International Journal of Network Management SN - 1055-7148 TI - A flow handover protocol to support state migration in softwarized networks ER - TY - CONF AB - Remarkable advantages of Containers (CNs) over Virtual Machines (VMs) such as lower overhead and faster startup has gained the attention of Communication Service Providers (CSPs) as using CNs for providing Virtual Network Functions (VNFs) can save costs while increasing the service agility. However, as it is not feasible to realise all types of VNFs in CNs, the coexistence of VMs and CNs is proposed. To put VMs and CNs together, an orchestration framework that can chain services across distributed and heterogeneous domains is required. To this end, we implemented a framework by extending and consolidating state-of-the-art tools and technologies originated from Network Function Virtualization (NFV), Software-defined Networking (SDN) and cloud computing environments. This framework chains services provisioned across Kubernetes and OpenStack domains. During the demo, we deploy a service consist of CN- and VM-based VNFs to demonstrate different features provided by our framework. AU - Razzaghi Kouchaksaraei, Hadi AU - Karl, Holger ID - 9809 KW - Network Function Virtualization KW - Software-defined Networking KW - Cloud Computing KW - service orchestration KW - OpenStack KW - Kubernetes T2 - 13th ACM International Conference on Distributed and Event-based Systems TI - Service Function Chaining Across OpenStack and Kubernetes Domains ER - TY - JOUR AU - Peuster, Manuel AU - Schneider, Stefan Balthasar AU - Zhao, Mengxuan AU - Xilouris, George AU - Trakadas, Panagiotis AU - Vicens, Felipe AU - Tavernier, Wouter AU - Soenen, Thomas AU - Vilalta, Ricard AU - Andreou, George AU - Kyriazis, Dimosthenis AU - Karl, Holger ID - 9824 JF - IEEE Communications Magazine SN - 0163-6804 TI - Introducing Automated Verification and Validation for Virtualized Network Functions and Services ER - TY - CONF AU - Afifi, Haitham AU - Karl, Holger ID - 6860 T2 - 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019) TI - Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network Embedding ER - TY - CONF AB - By distributing the computational load over the nodes of a Wireless Acoustic Sensor Network (WASN), the real-time capability of the TRINICON (TRIple-N-Independent component analysis for CONvolutive mixtures) framework for Blind Source Separation (BSS) can be ensured, even if the individual network nodes are not powerful enough to run TRINICON in real-time by themselves. To optimally utilize the limited computing power and data rate in WASNs, the MARVELO (Multicast-Aware Routing for Virtual network Embedding with Loops in Overlays) framework is expanded for use with TRINICON, while a feature-based selection scheme is proposed to exploit the most beneficial parts of the input signal for adapting the demixing system. The simulation results of realistic scenarios show only a minor degradation of the separation performance even in heavily resource-limited situations. AU - Guenther, Michael AU - Afifi, Haitham AU - Brendel, Andreas AU - Karl, Holger AU - Kellermann, Walter ID - 12880 T2 - 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (WASPAA 2019) TI - Sparse Adaptation of Distributed Blind Source Separation in Acoustic Sensor Networks ER - TY - CONF AB - Internet of Things (IoT) applications witness an exceptional evolution of traffic demands, while existing protocols, as seen in wireless sensor networks (WSNs), struggle to cope with these demands. Traditional protocols rely on finding a routing path between sensors generating data and sinks acting as gateway or databases. Meanwhile, the network will suffer from high collisions in case of high data rates. In this context, in-network processing solutions are used to leverage the wireless nodes' computations, by distributing processing tasks on the nodes along the routing path. Although in-network processing solutions are very popular in wired networks (e.g., data centers and wide area networks), there are many challenges to adopt these solutions in wireless networks, due to the interference problem. In this paper, we solve the problem of routing and task distribution jointly using a greedy Virtual Network Embedding (VNE) algorithm, and consider power control as well. Through simulations, we compare the proposed algorithm to optimal solutions and show that it achieves good results in terms of delay. Moreover, we discuss its sub-optimality by driving tight lower bounds and loose upper bounds. We also compare our solution with another wireless VNE solution to show the trade-off between delay and symbol error rate. AU - Afifi, Haitham AU - Karl, Holger ID - 12881 T2 - 2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) (WMNC'19) TI - An Approximate Power Control Algorithm for a Multi-Cast Wireless Virtual Network Embedding ER - TY - CONF AB - One of the major challenges in implementing wireless virtualization is the resource discovery. This is particularly important for the embedding-algorithms that are used to distribute the tasks to nodes. MARVELO is a prototype framework for executing different distributed algorithms on the top of a wireless (802.11) ad-hoc network. The aim of MARVELO is to select the nodes for running the algorithms and to define the routing between the nodes. Hence, it also supports monitoring functionalities to collect information about the available resources and to assist in profiling the algorithms. The objective of this demo is to show how MAVRLEO distributes tasks in an ad-hoc network, based on a feedback from our monitoring tool. Additionally, we explain the work-flow, composition and execution of the framework. AU - Afifi, Haitham AU - Karl, Holger AU - Eikenberg, Sebastian AU - Mueller, Arnold AU - Gansel, Lars AU - Makejkin, Alexander AU - Hannemann, Kai AU - Schellenberg, Rafael ID - 12882 KW - WSN KW - virtualization KW - VNE T2 - 2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo) TI - A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO ER - TY - CONF AU - Razzaghi Kouchaksaraei, Hadi AU - Karl, Holger ID - 12912 T2 - 15th International Conference on Network and Service Management (CNSM) TI - Quantitative Analysis of Dynamically Provisioned Heterogeneous Network Services ER - TY - CONF AU - Müller, Marcel AU - Behnke, Daniel AU - Bök, Patrick-Benjamin AU - Peuster, Manuel AU - Schneider, Stefan Balthasar AU - Karl, Holger ID - 15369 T2 - IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN) TI - 5G as Key Technology for Networked Factories: Application of Vertical-specific Network Services for Enabling Flexible Smart Manufacturing ER - TY - CONF AB - More and more management and orchestration approaches for (software) networks are based on machine learning paradigms and solutions. These approaches depend not only on their program code to operate properly, but also require enough input data to train their internal models. However, such training data is barely available for the software networking domain and most presented solutions rely on their own, sometimes not even published, data sets. This makes it hard, or even infeasible, to reproduce and compare many of the existing solutions. As a result, it ultimately slows down the adoption of machine learning approaches in softwarised networks. To this end, we introduce the "softwarised network data zoo" (SNDZoo), an open collection of software networking data sets aiming to streamline and ease machine learning research in the software networking domain. We present a general methodology to collect, archive, and publish those data sets for use by other researches and, as an example, eight initial data sets, focusing on the performance of virtualised network functions. AU - Peuster, Manuel AU - Schneider, Stefan Balthasar AU - Karl, Holger ID - 15371 T2 - IEEE/IFIP 15th International Conference on Network and Service Management (CNSM) TI - The Softwarised Network Data Zoo ER - TY - CONF AB - Offloading packet processing tasks to programmable switches and/or to programmable network interfaces, so called “SmartNICs”, is one of the key concepts to prepare softwarized networks for the high traffic demands of the future. However, implementing network functions that make use of those offload- ing technologies is still challenging and usually requires the availability of specialized hardware. It becomes even harder if heterogeneous services, making use of different offloading and network virtualization technologies, should be developed. In this paper, we introduce FOP4 (Function Offloading Pro- totyping with P4), a novel prototyping platform that allows to prototype heterogeneous software network scenarios, including container-based, P4-switch-based, and SmartNIC-based network functions. The presented work substantially extends our existing Containernet platform with the means to prototype offloading scenarios. Besides presenting the platform’s system design, we evaluate its scalability and show that it can run scenarios with more than 64 P4 switch or SmartNIC nodes on a single laptop. Finally, we presented a case study in which we use the presented platform to prototype an extended in-band network telemetry use case. AU - Moro, Daniele AU - Peuster, Manuel AU - Karl, Holger AU - Capone, Antonio ID - 15373 T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) TI - FOP4: Function Offloading Prototyping in Heterogeneous and Programmable Network Scenarios ER - TY - CONF AB - Emulation platforms supporting Virtual Network Functions (VNFs) allow developers to rapidly prototype network services. None of the available platforms, however, supports experimenting with programmable data planes to enable VNF offloading. In this demonstration, we show FOP4, a flexible platform that provides support for Docker-based VNFs, and VNF offloading, by means of P4-enabled switches. The platform provides interfaces to program the P4 devices and to deploy network functions. We demonstrate FOP4 with two complex example scenarios, demonstrating how developers can exploit data plane programmability to implement network functions. AU - Moro, Daniele AU - Peuster, Manuel AU - Karl, Holger AU - Capone, Antonio ID - 15374 T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) TI - Demonstrating FOP4: A Flexible Platform to Prototype NFV Offloading Scenarios ER - TY - CONF AU - Müller, Marcel AU - Behnke, Daniel AU - Bök, Patrick-Benjamin AU - Schneider, Stefan Balthasar AU - Peuster, Manuel AU - Karl, Holger ID - 15375 T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) TI - Putting NFV into Reality: Physical Smart Manufacturing Testbed ER - TY - CONF AU - Behnke, Daniel AU - Müller, Marcel AU - Bök, Patrick-Benjamin AU - Schneider, Stefan Balthasar AU - Peuster, Manuel AU - Karl, Holger ID - 15376 T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) TI - NFV-driven intrusion detection for smart manufacturing ER - TY - JOUR AB - In many cyber–physical systems, we encounter the problem of remote state estimation of geo- graphically distributed and remote physical processes. This paper studies the scheduling of sensor transmissions to estimate the states of multiple remote, dynamic processes. Information from the different sensors has to be transmitted to a central gateway over a wireless network for monitoring purposes, where typically fewer wireless channels are available than there are processes to be monitored. For effective estimation at the gateway, the sensors need to be scheduled appropriately, i.e., at each time instant one needs to decide which sensors have network access and which ones do not. To address this scheduling problem, we formulate an associated Markov decision process (MDP). This MDP is then solved using a Deep Q-Network, a recent deep reinforcement learning algorithm that is at once scalable and model-free. We compare our scheduling algorithm to popular scheduling algorithms such as round-robin and reduced-waiting-time, among others. Our algorithm is shown to significantly outperform these algorithms for many example scenario AU - Leong, Alex S. AU - Ramaswamy, Arunselvan AU - Quevedo, Daniel E. AU - Karl, Holger AU - Shi, Ling ID - 15741 JF - Automatica SN - 0005-1098 TI - Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems ER - TY - CONF AB - Given the recent development in embedded devices, wireless senor nodes are no longer limited to data collection but they can also do processing (e.g., smartphones). Accordingly, new types of applications take an advantage of the processing and flexibility provided by the wireless network. A common property between these applications is that the processing is not running on only one single node, but it is broken-down into smaller tasks that can run over multiple nodes, i.e., exploiting the in-network processing. We study a special variant of in-network processing, where the application is given by a graph; the processing tasks have predefined connections to be executed in a predefined sequence. The problem of embedding an application graph into a network is commonly known as Virtual Network Embedding (VNE). In this paper, we present a Genetic Algorithm (GA) solution to solve this wireless VNE problem, where we take into account the interference and multi-cast properties. We show that the GA has a good performance and fast execution compared to the optimization problem. AU - Afifi, Haitham AU - Horbach, Konrad AU - Karl, Holger ID - 13123 T2 - 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (WiMob 2019) TI - A Genetic Algorithm Framework for Solving Wireless Virtual Network Embedding ER - TY - CONF AB - Building on 5G and network function virtualization (NFV), smart manufacturing has the potential to drastically increase productivity, reduce cost, and introduce novel, flexible manufacturing services. Current work mostly focuses on high-level scenarios or emulation-based prototype deployments. Extending our previous work, we showcase one of the first cloud-native 5G verticals focusing on the deployment of smart manufacturing use cases on production infrastructure. In particular, we use the 5GTANGO service platform to deploy our developed network services on Kubernetes. For this demo, we implemented a series of cloud-native virtualized network functions (VNFs) and created suitable service descriptors. Their light-weight, stateless deployment on Kubernetes enables quick instantiation, scalability, and robustness. AU - Schneider, Stefan Balthasar AU - Peuster, Manuel AU - Hannemann, Kai AU - Behnke, Daniel AU - Müller, Marcel AU - Bök, Patrick-Benjamin AU - Karl, Holger ID - 13292 KW - 5G KW - NFV KW - Smart Manufacturing KW - Cloud-Native KW - Kubernetes T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Demo Track TI - "Producing Cloud-Native": Smart Manufacturing Use Cases on Kubernetes ER - TY - JOUR AU - Peuster, Manuel AU - Marchetti, Michael AU - García de Blas, Gerardo AU - Karl, Holger ID - 10325 JF - EURASIP Journal on Wireless Communications and Networking SN - 1687-1499 TI - Automated testing of NFV orchestrators against carrier-grade multi-PoP scenarios using emulation-based smoke testing ER - TY - CONF AU - Afifi, Haitham AU - Auroux, Sébastien AU - Karl, Holger ID - 2474 TI - MARVELO: Wireless Virtual Network Embedding for Overlay Graphs with Loops ER - TY - CONF AU - Shiferaw Heyi, Binyam AU - Karl, Holger ID - 2476 TI - Modelling Time-Limited Capacity of a Wireless Channel as aMarkov Reward Process ER - TY - CONF AB - Understanding the behavior of the components of service function chains (SFCs) in different load situations is important for efficient and automatic management and orches- tration of services. For this purpose and for practical research in network function virtualization in general, there is a great need for benchmarks and experimental data. In this paper, we describe our experiments for characterizing the relationship between resource demands of virtual network functions (VNFs) and the expected performance of the SFC, considering the individual performance of the VNFs as well as the interdependencies among VNFs within the SFC. We have designed our experiments focusing on video streaming, an important application in this context. We present examples of models for predicting the interdependence between resource demands and performance characteristics of SFCs using support vector regression and polynomial regression models. We also show practical evidence from our experiments that VNFs need to be benchmarked in their final chain setup, rather than individually, to capture important interdependencies that affect their performance. The data gathered from our experiments is publicly available. AU - Dräxler, Sevil AU - Peuster, Manuel AU - Illian, Marvin AU - Karl, Holger ID - 2480 T2 - 4th IEEE International Conference on Network Softwarization (NetSoft 2018) TI - Generating Resource and Performance Models for Service Function Chains: The Video Streaming Case ER - TY - CONF AB - Network function virtualization requires scaling and placement, deciding the number and the location of function instances. Current approaches are limited in flexibility and practical applicability. Specifically, we study dynamic, single-step, joint scaling and placement of network services with bidirectional flows traversing Physical or Virtual Network Functions (VNFs) and returning to their sources. We develop models to support stateful components and legacy network functions with fixed locations in these network services as well as the possibility of reusing VNFs across network services. We formalize the problem of jointly scaling and placing such network services as a mixed- integer linear program (MILP). We show that this problem is NP-complete and also present a heuristic algorithm to find good solutions in short time. In an extensive evaluation with realistic scenarios, we investigate the capabilities of the two approaches. AU - Dräxler, Sevil AU - Schneider, Stefan Balthasar AU - Karl, Holger ID - 2481 T2 - 4th IEEE International Conference on Network Softwarization (NetSoft 2018) TI - Scaling and Placing Bidirectional Services with Stateful Virtual and Physical Network Functions ER - TY - CONF AU - Dräxler, Sevil AU - Karl, Holger AU - Razzaghi Kouchaksaraei, Hadi AU - Machwe, Azahar AU - Dent-Young, Crispin AU - Katsalis, Kostas AU - Samdanis, Konstantinos ID - 2482 T2 - 27th European Conference on Networks and Communications (EUCNC 2018) TI - 5G OS: Control and Orchestration of Services on Multi-Domain Heterogeneous 5G Infrastructures ER - TY - GEN AB - Understanding the behavior of distributed cloud service components in different load situations is important for efficient and automatic management and orchestration of these services. For this purpose and for practical research in distributed cloud computing in general, there is need for benchmarks and experimental data. In this paper, we describe our experiments for characterizing the relationship between resource demands of application components and the expected performance of applica- tions. We present initial results for predicting the interdependence between resource demands and performance characteristics using support vector regression and polynomial regression models. The data gathered from our experiments is publicly available. AU - Dräxler, Sevil AU - Peuster, Manuel AU - Illian, Marvin AU - Karl, Holger ID - 2483 TI - Towards Predicting Resource Demands and Performance of Distributed Cloud Services ER - TY - CONF AU - Auroux, Sébastien AU - Karl, Holger ID - 2472 TI - Distributed Placement of Virtualized Control Applications in Mobile Backhaul Networks ER - TY - CONF AB - Supporting the vast variety of network services’ management and orchestration requirements is one of the main challenges that Network Function Virtualization (NFV) is dealing with. While general management requirements such as Virtual Network Function (VNF) resource requirements can be specified by the service developers using service descriptors, specific management operations like VNF-specific configuration cannot be performed by these descriptors. On the other hand, it is inefficient and also very challenging for Management and Orchestration (MANO) frameworks to provide all specific-management operations for every individual network service and their constituent VNFs. To mitigate this issue, we propose the use of service-specific programs called Specific Managers (SMs) that can customize management and orchestration of network services and also extend the capability of MANO frameworks to support per-service management and orchestration. The results of our evaluation show that the higher flexibility and programmability enabled by SMs improve the performance of the service performance and also utilises the service provider resources more efficiently. AU - Razzaghi Kouchaksaraei, Hadi AU - Dräxler, Sevil AU - Peuster, Manuel AU - Karl, Holger ID - 2666 T2 - 2018 European Conference on Networks and Communications (EuCNC) TI - Programmable and Flexible Management and Orchestration of Virtualized Network Functions ER - TY - CONF AB - Developing cloud applications using a microservice architecture allows their functional blocks to be distributed and deployed on multiple Cloud infrastructures. This enables service providers to mix and match Cloud-based microservices and Virtual Network Functions (VNFs) that are provided by Network Function Virtualization (NFV). Provisioning complex services containing VNFs and Cloud-based microservices across NFV and cloud infrastructures can enhance service quality, reduce latency, and optimise cost. This can be provided by an orchestration system that can handle cross-ecosystem dependencies. To this end, we implemented Pishahang that is a framework for jointly managing and orchestrating virtual network functions and Cloud-based microservices. During the demo, we deploy several complex services to demonstrate features provided by Pishahang to support management and orchestration of complex services. AU - Razzaghi Kouchaksaraei, Hadi AU - Dierich, Tobias AU - Karl, Holger ID - 2667 TI - Pishahang: Joint Orchestration of Network Function Chains and Distributed Cloud Applications ER - TY - CONF AU - Demirel, Burak AU - Ramaswamy, Arunselvan AU - Quevedo, Daniel AU - Karl, Holger ID - 3217 TI - DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling ER - TY - CONF AB - Dynamically steering flows through virtualized net- work function instances is a key enabler for elastic, on-demand deployments of virtualized network functions. This becomes par- ticular challenging when stateful functions are involved, necessi- tating state management. The problem with existing solutions is that they typically embrace state migration and flow rerouting jointly, imposing a huge set of requirements on the on-boarded VNFs, e.g., solution-specific state management interfaces. In this paper, we introduce the seamless handover proto- col (SHarP). It provides an easy-to-use, loss-less, and order- preserving flow rerouting mechanism that is not fixed to a single state management approach. This allows VNF vendors to implement or use the state management solution of their choice. SHarP supports these solutions with additional information when flows are migrated. Further, we show how SHarP significantly reduces the buffer usage at a central (SDN) controller, which is a typical bottleneck in existing solutions. Our experiments show that SHarP uses a constant amount of controller buffer, irrespective of the time taken to migrate the VNF state. AU - Peuster, Manuel AU - Küttner, Hannes AU - Karl, Holger ID - 3345 T2 - 4th IEEE International Conference on Network Softwarization (NetSoft 2018) TI - Let the state follow its flows: An SDN-based flow handover protocol to support state migration ER - TY - CONF AB - Developing a virtualized network service does not only involve the implementation and configuration of the network functions it is composed of but also its integration and test with management solutions that will control the service in its production environment. These integration tasks require testbeds that offer the needed network function virtualization infrastructure~(NFVI), like OpenStack, introducing a lot of management and maintenance overheads. Such testbed setups become even more complicated when the multi point-of-presence~(PoP) case, with multiple infrastructure installations, is considered. In this demo, we showcase an emulation platform that executes containerized network services in user-defined multi-PoP topologies. The platform does not only allow network service developers to locally test their services but also to connect real-world management and orchestration solutions to the emulated PoPs. During our interactive demonstration we focus on the integration between the emulated infrastructure and state-of-the-art orchestration solutions like SONATA or OSM. AU - Peuster, Manuel AU - Kampmeyer, Johannes AU - Karl, Holger ID - 3346 T2 - 4th IEEE International Conference on Network Softwarization (NetSoft 2018) TI - Containernet 2.0: A Rapid Prototyping Platform for Hybrid Service Function Chains ER - TY - CONF AB - Management and orchestration~(MANO) systems are the key components of future large-scale NFV environments. They will manage resources of hundreds or even thousands of NFV infrastructure installations, so called points of presence~(PoP). Such scenarios need to be automatically tested during the development phase of a MANO system. This task becomes very challenging because large-scale NFV testbeds are hard to maintain, too expensive, or simply not available. In this paper, we present a multi-PoP NFV infrastructure emulation platform that enables automated, large-scale testing of MANO stacks. We show that our platform can easily emulate hundreds of PoPs on a single physical machine and reduces the setup time of a test PoP by a factor of 232x compared to a DevStack-based test PoP installation. Further, we present a case study in which we test ETSI's Open Source MANO~(OSM) against our proposed system to gain insights about OSM's behaviour in large-scale NFV deployments. AU - Peuster, Manuel AU - Marchetti, Michael AU - Garcia de Blas, Gerado AU - Karl, Holger ID - 3347 T2 - European Conference on Networks and Communications (EuCNC) TI - Emulation-based Smoke Testing of NFV Orchestrators in Large Multi-PoP Environments ER - TY - JOUR AB - To adapt to continuously changing workloads in networks, components of the running network services may need to be replicated (scaling the network service) and allocated to physical resources (placement) dynamically, also necessitating dynamic re-routing of flows between service components. In this paper, we propose JASPER, a fully automated approach to jointly optimizing scaling, placement, and routing for complex network services, consisting of multiple (virtualized) components. JASPER handles multiple network services that share the same substrate network; services can be dynamically added or removed and dynamic workload changes are handled. Our approach lets service designers specify their services on a high level of abstraction using service templates. JASPER automatically makes scaling, placement and routing decisions, enabling quick reaction to changes. We formalize the problem, analyze its complexity, and develop two algorithms to solve it. Extensive empirical results show the applicability and effectiveness of the proposed approach. AU - Dräxler, Sevil AU - Karl, Holger AU - Mann, Zoltan Adam ID - 3152 JF - IEEE Transactions on Network and Service Management TI - JASPER: Joint Optimization of Scaling, Placement, and Routing of Virtual Network Services ER - TY - CONF AU - Peuster, Manuel AU - Karl, Holger ID - 6016 T2 - IEEE/IFIP 14th International Conference on Network and Service Management (CNSM) TI - Understand your chains and keep your deadlines: Introducing time-constrained profiling for NFV ER - TY - CONF AU - Peuster, Manuel AU - Schneider, Stefan Balthasar AU - Christ, Frederic AU - Karl, Holger ID - 6483 T2 - IEEE Conference on Network Function Virtualisation and Software Defined Networks (NFV-SDN) 5GNetApp TI - A Prototyping Platform to Validate and Verify Network Service Header-based Service Chains ER - TY - GEN AU - Rosa, Raphael Vicente AU - Rothenberg, Christian Esteve AU - Peuster, Manuel AU - Karl, Holger ID - 6485 TI - Methodology for VNF Benchmarking Automation ER - TY - CONF AB - Dynamic allocation of resources is a key feature in network function virtualization (NFV), enabling flexible adjustment of slices and contained network services to ever-changing service demands. Considering resource allocation across the entire network, many authors have proposed approaches to optimize the placement and chaining of virtual network function (VNF) instances and the allocation of resources to these VNF instances. In doing so, various optimization objectives are conceivable, e.g., minimizing certain required resources or the end-to-end delay of the placed services. In this paper, we investigate the relationship between four typical optimization objectives when coordinating the placement and resource allocation of chained VNF instances. We observe an interesting trade-off between minimizing the overhead of starting/stopping VNF instances and all other objectives when adapting to changed service demands. AU - Schneider, Stefan Balthasar AU - Dräxler, Sevil AU - Karl, Holger ID - 6970 T2 - IEEE Global Communications Conference (GLOBECOM 2018) TI - Trade-offs in Dynamic Resource Allocation in Network Function Virtualization ER - TY - CONF AB - In recent years, a variety of different approaches have been proposed to tackle the problem of scaling and placing network services, consisting of interconnected virtual network functions (VNFs). This paper presents a placement abstraction layer (PAL) that provides a clear and simple northbound interface for using such algorithms while hiding their internal functionality and implementation. Through its southbound interface, PAL can connect to different back ends that evaluate the calculated placements, e.g., using simulations, emulations, or testbed approaches. As an example for such evaluation back ends, we introduce a novel placement emulation framework (PEF) that allows executing calculated placements using real, containerbased VNFs on real-world network topologies. In a case study, we show how PAL and PEF facilitate reusing and evaluating placement algorithms as well as validating their underlying models and performance claims. AU - Schneider, Stefan Balthasar AU - Peuster, Manuel AU - Karl, Holger ID - 6972 T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN 2018) TI - A Generic Emulation Framework for Reusing and Evaluating VNF Placement Algorithms ER - TY - CONF AB - A key challenge of network function virtualization (NFV) is the complexity of developing and deploying new network services. Currently, development requires many manual steps that are time-consuming and error-prone (e.g., for creating service descriptors). Furthermore, existing management and orchestration (MANO) platforms only offer limited support of standardized descriptor models or package formats, limiting the re-usability of network services. To this end, we introduce a fully integrated, open-source NFV service development kit (SDK) with multi-MANO platform support. Our SDK simplifies many NFV service development steps by offering initial generation of descriptors, advanced project management, as well as fully automated packaging and submission for on-boarding. To achieve multi-platform support, we present a package format that extends ETSI’s VNF package format. In this demonstration, we present the end-to-end workflow to develop an NFV service that is then packaged for multiple platforms, i.e., 5GTANGO and OSM. AU - Schneider, Stefan Balthasar AU - Peuster, Manuel AU - Tavernier, Wouter AU - Karl, Holger ID - 6974 T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN 2018) TI - A Fully Integrated Multi-Platform NFV SDK ER - TY - CONF AB - Recent studies show the increasing popularity of distributed cloud applications, which are composed of multiple microservices. Besides their known benefits, microservice architecture also enables to mix and match cloud applications and Network Function Virtualization (NFV) services (service chains), which are composed of Virtual Network Functions (VNFs). Provisioning complex services containing VNFs and microservices in a combined NFV/cloud platform can enhance service quality and optimise cost. Such a platform can be based on the multi-cloud concept. However, current multi-cloud solutions do not support NFV requirements, making them inadequate to support complex services. In this paper, we investigate these challenges and propose a solution for jointly managing and orchestrating microservices and virtual network functions. AU - Razzaghi Kouchaksaraei, Hadi AU - Karl, Holger ID - 1041 SN - 978-1-61208-607-1 T2 - The Ninth International Conference on Cloud Computing, GRIDs, and Virtualization CLOUD COMPUTING TI - Joint Orchestration of Cloud-Based Microservices and Virtual Network Functions ER - TY - CONF AB - Signal processing in WASNs is based on a software framework for hosting the algorithms as well as on a set of wireless connected devices representing the hardware. Each of the nodes contributes memory, processing power, communication bandwidth and some sensor information for the tasks to be solved on the network. In this paper we present our MARVELO framework for distributed signal processing. It is intended for transforming existing centralized implementations into distributed versions. To this end, the software only needs a block-oriented implementation, which MARVELO picks-up and distributes on the network. Additionally, our sensor node hardware and the audio interfaces responsible for multi-channel recordings are presented. AU - Afifi, Haitham AU - Schmalenstroeer, Joerg AU - Ullmann, Joerg AU - Haeb-Umbach, Reinhold AU - Karl, Holger ID - 6859 T2 - Speech Communication; 13th ITG-Symposium TI - MARVELO - A Framework for Signal Processing in Wireless Acoustic Sensor Networks ER - TY - JOUR AB - Network function virtualization and software-defined networking allow services consisting of virtual network functions to be designed and implemented with great flexibility by facilitating automatic deployments, migrations, and reconfigurations for services and their components. For extended flexibility, we go beyond seeing services as a fixed chain of functions. We define the service structure in a flexible way that enables changing the order of functions in case the functionality of the service is not influenced by this, and propose a YANG data model for expressing this flexibility. Flexible structures allow the network orchestration system to choose the optimal composition of service components that for example gives the best results for placement of services in the network. When number of flexible services and number of components in each service increase, combinatorial explosion limits the practical use of this flexibility. In this paper, we describe a selection heuristic that gives a Pareto set of the possible compositions of a service as well as possible combinations of different services, with respect to different optimization objectives. Moreover, we present a heuristic algorithm for placement of a combination of services, which aims at placing service components along shortest paths that have enough capacity for accommodating the services. By applying these solutions, we show that allowing flexibility in the service structure is feasible. AU - Dräxler, Sevil AU - Karl, Holger ID - 58 IS - 2 JF - International Journal of Network Management TI - Specification, Composition, and Placement of Network Services with Flexible Structures ER - TY - JOUR AB - A current trend in networking and cloud computing is to provide compute resources at widely distributed sites; this is exemplified by developments such as Network Function Virtualisation. This paves the way for wide-area service deployments with improved service quality: e.g. user-perceived response times can be reduced by offering services at nearby sites. But always assigning users to the nearest site can be a bad decision if this site is already highly utilised. This paper formalises two related decisions of allocating compute resources at different sites and assigning users to them with the goal of minimising the response times while the total number of resources to be allocated is limited – a non-linear capacitated Facility Location Problem with integrated queuing systems. To efficiently handle its non-linearity, we introduce five linear problem linearisations and adapt the currently best heuristic for a similar scenario to our scenario. All six approaches are compared in experiments for solution quality and solving time. Surprisingly, our best optimisation formulation outperforms the heuristic in both time and quality. Additionally, we evaluate the influence of distributions of available compute resources in the network on the response time: The time was halved for some configurations. The presented formulation techniques for our problem linearisations are applicable to a broader optimisation domain. AU - Keller, Matthias AU - Karl, Holger ID - 64 IS - 1 JF - IEEE Transactions on Network and Service Management TI - Response-Time-Optimised Service Deployment: MILP Formulations of Piece-wise Linear Functions Approximating Non-linear Bivariate Mixed-integer Functions ER - TY - CONF AU - Schwabe, Arne AU - Rojas, Elisa AU - Karl, Holger ID - 708 T2 - 2017 {IEEE} Conference on Network Softwarization, NetSoft 2017, Bologna, Italy, July 3-7, 2017 TI - Minimizing downtimes: Using dynamic reconfiguration and state management in SDN ER - TY - CONF AB - In conventional large-scale networks, creation and management of network services are costly and complex tasks that often consume a lot of resources, including time and manpower. Network softwarization and network function virtualization have been introduced to tackle these problems, aiming at decreasing costs and complexity of implementing new services, maintaining the implemented services, and managing available resources in service provisioning platforms and underlying infrastructures. To experience the full potential of these approaches, innovative development support tools and service provisioning environments are needed. To answer these needs, we introduce the architecture of the open-source SONATA system, a service programming, orchestration, and management framework. We present a development toolchain for virtualized network services, fully integrated with a service platform and orchestration system. We introduce the modular and flexible architecture of our system and discuss its main components and features, such as function- and service-specific managers that allow fine-grained service management, slicing support to facilitate multi-tenancy, recursiveness for improved scalability, and full-featured DevOps support. AU - Dräxler, Sevil AU - Karl, Holger AU - Peuster, Manuel AU - Razzaghi Kouchaksaraei, Hadi AU - Bredel, Michael AU - Lessmann, Johannes AU - Soenen, Thomas AU - Tavernier, Wouter AU - Mendel-Brin, Sharon AU - Xilouris, George ID - 717 SN - 9781509015252 T2 - 2017 IEEE International Conference on Communications Workshops (ICC Workshops) TI - SONATA: Service programming and orchestration for virtualized software networks ER - TY - CONF AB - Developing a virtualized network service does not only involve the implementation and configuration of the network functions it is composed of but also its integration and test with management solutions that will control the service in its production environment. These integration tasks require testbeds that offer the needed network function virtualization infrastructure~(NFVI), like OpenStack, introducing a lot of management and maintenance overheads. Such testbed setups become even more complicated when the multi point-of-presence~(PoP) case, with multiple infrastructure installations, is considered. In this demo, we showcase an emulation platform that executes containerized network services in user-defined multi-PoP topologies. The platform does not only allow network service developers to locally test their services but also to connect real-world management and orchestration solutions to the emulated PoPs. During our interactive demonstration we focus on the integration between the emulated infrastructure and state-of-the-art orchestration solutions like SONATA or OSM. AU - Peuster, Manuel AU - Dräxler, Sevil AU - Razzaghi Kouchaksaraei, Hadi AU - van Rossem, Steven AU - Tavernier, Wouter AU - Karl, Holger ID - 723 T2 - IEEE Conference on Network Softwarization, NetSoft 2017, Bologna, Italy, July 3-7, 2017 TI - A flexible multi-pop infrastructure emulator for carrier-grade MANO systems ER - TY - CONF AB - Management of complex network services requires flexible and efficient service provisioning as well as optimized handling of continuous changes in the workload of the service.To adapt to changes in the demand, service components need to be replicated (scaling) and allocated to physical resources (placement) dynamically. In this paper, we propose a fullyautomated approach to the joint optimization problem of scaling and placement, enabling quick reaction to changes. We formalize the problem, analyze its complexity, and develop two algorithms to solve it. Extensive empirical results show the applicability andeffectiveness of the proposed approach. AU - Dräxler, Sevil AU - Karl, Holger AU - Mann, Zoltan Adam ID - 87 T2 - Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2017) TI - Joint Optimization of Scaling and Placement of Virtual Network Services ER - TY - CONF AB - Benchmarking and profiling virtual network functions (VNFs) generates input knowledge for resource management decisions taken by management and orchestration systems. Such VNFs are usually not executed in isolation but are often deployed as part of a service function chain (SFC) that connects single functions into complex structures. To manage such chains, isolated performance profiles of single functions have to be combined to get insights into the overall behavior of an SFC. This becomes particularly challenging in highly agile DevOps environments in which profiling processes need to be fully automated and detailed insights about a chain's internal structures are not always available. In this paper, we introduce a fully automatable, flexible, and platform-agnostic profiling system that allows to profile entire SFCs at once. This obviates manual modeling procedures to combine profiling results from single VNFs to reflect SFC performance. We use a case study with different SFC configurations to show that it is hard to model the resulting SFC performance based on single-VNF measurements and that performance interactions between real, non-trivial functions that are deployed in a chain exist. AU - Peuster, Manuel AU - Karl, Holger ID - 981 T2 - IEEE Conference on Network Function Virtualisation and Software Defined Networks (NFV-SDN) TI - Profile Your Chains, Not Functions. Automated Network Service Profiling in DevOps Environments ER - TY - CONF AU - Auroux, Sébastien AU - Scholz, S. AU - Karl, Holger ID - 983 T2 - Proc. European Wireless TI - Assessing Genetic Algorithms for Placing Flow Processing-aware Control Applications ER - TY - JOUR AB - The Service Programming and Orchestration for Virtualised Software Networks (SONATA) project targets both the flexible programmability of software networks and the optimisation of their deployments by means of integrating Development and Operations in order to accelerate industry adoption of software networks and reduce time-to-market for networked services. SONATA supports network function chaining and orchestration, making service platforms modular and easier to customise to the needs of different service providers, and introduces a specialised Development and Operations model for supporting developers. AU - Karl, Holger AU - Dräxler, Sevil AU - Peuster, Manuel AU - Galis, Alex AU - Bredel, Michael AU - Ramos, Aurora AU - Martrat, Josep AU - Siddiqui, Muhammad Shuaib AU - van Rossem, Steven AU - Tavernier, Wouter AU - Xilouris, George ID - 714 IS - 9 JF - Transactions on Emerging Telecommunications Technologies SN - 2161-3915 TI - DevOps for network function virtualisation: an architectural approach VL - 27 ER - TY - JOUR AU - Wette, Philip AU - Karl, Holger ID - 726 JF - Computer Communications TI - DCT²Gen: A traffic generator for data centers ER - TY - CONF AU - Schwabe, Arne AU - A. Aranda-Gutierrez, Pedro AU - Karl, Holger ID - 728 T2 - Proceedings of the 2016 Applied Networking Research Workshop, {ANRW} 2016, Berlin, Germany, July 16, 2016 TI - Composition of SDN applications: Options/challenges for real implementations ER - TY - CONF AU - Doriguzzi Corin, Roberto AU - A. Aranda-Gutierrez, Pedro AU - Rojas, Elisa AU - Karl, Holger AU - Salvadori, Elio ID - 729 T2 - 12th International Conference on Network and Service Management, {CNSM} 2016, Montreal, QC, Canada, October 31 - Nov. 4, 2016 TI - Reusability of software-defined networking applications: {A} runtime, multi-controller approach ER - TY - CONF AB - Allocating resources to virtualized network functions and services to meet service level agreements is a challenging task for NFV management and orchestration systems. This becomes even more challenging when agile development methodologies, like DevOps, are applied. In such scenarios, management and orchestration systems are continuously facing new versions of functions and services which makes it hard to decide how much resources have to be allocated to them to provide the expected service performance. One solution for this problem is to support resource allocation decisions with performance behavior information obtained by profiling techniques applied to such network functions and services. In this position paper, we analyze and discuss the components needed to generate such performance behavior information within the NFV DevOps workflow. We also outline research questions that identify open issues and missing pieces for a fully integrated NFV profiling solution. Further, we introduce a novel profiling mechanism that is able to profile virtualized network functions and entire network service chains under different resource constraints before they are deployed on production infrastructure. AU - Peuster, Manuel AU - Karl, Holger ID - 730 T2 - Fifth European Workshop on Software-Defined Networks, EWSDN 2016, Den Haag, The Netherlands, October 10-11, 2016 TI - Understand Your Chains: Towards Performance Profile-Based Network Service Management ER - TY - CONF AB - Traditional cellular networks are forced to remain active regardless of the actual amount of traffic that is currently produced/requested, with a clear waste of energy. Two-layer mobile networks with separated signalling and data layers have been recently proposed for energy savings in future implementations. These networks are able to switch off unneeded data cells completely while maintaining full coverage with their signalling cells, thus saving energy. In this demonstration, we showcase a testbed that uses Wi-Fi access points to emulate small cells of the data layer and a publicly available cellular connection as the signalling layer. We use off-the-shelf Android smartphones with an ad-hoc networking management module and a MultiPath TCP-enabled kernel to manage the Wi-Fi and cellular interfaces simultaneously. The testbed is used to demonstrate the general feasibility of this layered architecture and to facilitate experiments with network-wide resource optimization. AU - Peuster, Manuel AU - Karl, Holger AU - Enrico Redondi, Alessandro AU - Capone, Antonio ID - 731 T2 - IEEE Conference on Computer Communications Workshops, INFOCOM Workshops 2016, San Francisco, CA, USA, April 10-14, 2016 TI - Demonstrating on-demand cell switching with a two-layer mobile network testbed ER - TY - CONF AB - Elastic deployments of virtualized network functions~(VNF) can automatically scale the amount of used resources in relation to their workload. This is often done by starting new VNF instances or stopping old ones. A problem of these scale operations is that most network functions are stateful and their internal state is not automatically migrated when traffic is redistributed in the deployment. As a result, mechanisms are needed to exchange or migrate internal network function state between VNF instances. This paper presents a state management framework that creates a logically distributed state store on top of elastically deployed virtual network functions. We also introduce a novel programming model that provides both a local and a global view of the state to each VNF instance. We discuss the integration of our framework into existing network function virtualization architectures and compare the performance of our prototype to a centralized and a distributed state store solution. AU - Peuster, Manuel AU - Karl, Holger ID - 732 T2 - IEEE NetSoft Conference and Workshops, NetSoft 2016, Seoul, South Korea, June 6-10, 2016 TI - E-State: Distributed state management in elastic network function deployments ER - TY - CONF AU - Auroux, Sébastien AU - Parruca, Donald AU - Karl, Holger ID - 735 T2 - 27th IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, {PIMRC} 2016, Valencia, Spain, September 4-8, 2016 TI - Joint real-time scheduling and interference coordination for wireless factory automation ER - TY - CONF AB - Virtualized network services consisting of multiple individual network functions are already today deployed across multiple sites, so called multi-PoP (points of presence) environments. This allows to improve service performance by optimizing its placement in the network. But prototyping and testing of these complex distributed software systems becomes extremely challenging. The reason is that not only the network service as such has to be tested but also its integration with management and orchestration systems. Existing solutions, like simulators, basic network emulators, or local cloud testbeds, do not support all aspects of these tasks. To this end, we introduce MeDICINE, a novel NFV prototyping platform that is able to execute production-ready network functions, provided as software containers, in an emulated multi-PoP environment. These network functions can be controlled by any third-party management and orchestration system that connects to our platform through standard interfaces. Based on this, a developer can use our platform to prototype and test complex network services in a realistic environment running on his laptop. AU - Peuster, Manuel AU - Karl, Holger AU - van Rossem, Steven ID - 738 T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) TI - MeDICINE: Rapid Prototyping of Production-Ready Network Services in Multi-PoP Environments ER - TY - CONF AB - Network function virtualization and software-defined networking allow services consisting of virtual network functions to be designed and implemented with great flexibility by facilitating automatic deployments, migrations, and reconfigurations for services and their components. For extended flexibility, we go beyond seeing services as a fixed chain of functions. We present a YANG model for describing the service structure in deployment requests in a flexible way that enables changing the order of functions in case the order of traversing them does not affect the functionality of the service. Upon receiving such requests, the network orchestration system can choose the optimal composition of service components that gives the best results for placement of services in the network. This introduces new complexities to the placement problem by greatly increasing the number of possible ways a service can be composed. In this paper, we describe a heuristic solution that selects a Pareto set of the possible compositions of a service as well as possible combinations of different services, with respect to different resource requirements of the services. Our evaluations show that the selected combinations consist of representative samples of possible structures and requirements and therefore, can result in optimal or close-to-optimal placement results. AU - Dräxler, Sevil AU - Karl, Holger ID - 166 T2 - Proceedings of the 2nd International IEEE Conference on Network Softwarization (NetSoft) TI - Placement of Services with Flexible Structures Specified by a YANG Data Model ER - TY - JOUR AU - Herlich, Matthias AU - Bredenbals, Nico AU - Karl, Holger ID - 1373 JF - Sustainable Computing: Informatics and Systems SN - 2210-5379 TI - Delayed (de-)activation in servers with a sleep mode VL - 10 ER - TY - CONF AB - Video streaming is in high demand by mobile users. In cellular networks, however, the unreliable wireless channel leads to two major problems. Poor channel states degrade video quality and interrupt the playback when a user cannot sufficiently fill its local playout buffer: buffer underruns occur. In contrast, good channel conditions cause common greedy buffering schemes to buffer too much data. Such over-buffering wastes expensive wireless channel capacity. Assuming that we can anticipate future data rates, we plan the quality and download time of video segments ahead. This anticipatory download scheduling avoids buffer underruns by downloading a large number of segments before a drop in available data rate occurs, without wasting wireless capacity by excessive buffering.We developed a practical anticipatory scheduling algorithm for segmented video streaming protocols (e.g., HLS or MPEG DASH). Simulation results and testbed measurements show that our solution essentially eliminates playback interruptions without significantly decreasing video quality. AU - Dräxler, Martin AU - Blobel, Johannes AU - Dreimann, Philipp AU - Valentin, Stefan AU - Karl, Holger ID - 252 T2 - Proceedings of the 2nd International Conference on Networked Systems (NetSys) TI - SmarterPhones: Anticipatory Download Scheduling for Wireless Video Streaming ER - TY - CONF AB - The size of modern data centers is constantly increasing. As it is not economic to interconnect all machines in the data center using a full-bisection-bandwidth network, techniques have to be developed to increase the efficiency of data-center networks. The Software-Defined Network paradigm opened the door for centralized traffic engineering (TE) in such environments. Up to now, there were already a number of TE proposals for SDN-controlled data centers that all work very well. However, these techniques either use a high amount of flow table entries or a high flow installation rate that overwhelms available switching hardware, or they require custom or very expensive end-of-line equipment to be usable in practice. We present HybridTE, a TE technique that uses (uncertain) information about large flows. Using this extra information, our technique has very low hardware requirements while maintaining better performance than existing TE techniques. This enables us to build very low-cost, high performance data-center networks. AU - Wette, Philip AU - Karl, Holger ID - 287 T2 - Proceedings of the 4th European Workshop on Software Defined Networks (EWSDN 2015) TI - HybridTE: Traffic Engineering for Very Low-Cost Software-Defined Data-Center Networks ER - TY - CONF AB - Multi-rooted trees are becoming the norm for modern data-center networks. In these networks, scalable flow routing is challenging owing to vast number of flows. Current approaches either employ a central controller that can have scalability issues or a scalable decentralized algorithm only considering local information. In this paper we present a new decentralized approach to least-congested path routing in software-defined data center networks that has neither of these issues: By duplicating the initial (or SYN) packet of a flow and estimating the data rate of multiple flows in parallel, we exploit TCP’s habit to fill buffers to find the least congested path. We show that our algorithm significantly improves flow completion time without the need for a central controller or specialized hardware. AU - Schwabe, Arne AU - Karl, Holger ID - 247 T2 - Proceedings of the 4th European Workshop on Software Defined Networks (EWSDN 2015) TI - SynRace: Decentralized Load-Adaptive Multi-path Routing without Collecting Statistics ER - TY - CONF AU - A. Aranda-Gutierrez, Pedro AU - Karl, Holger AU - Rojas, Elisa AU - Leckey, Alec ID - 739 T2 - 2015 European Conference on Networks and Communications, EuCNC 2015, Paris, France, June 29 - July 2, 2015 TI - On Network Application representation and controller independence in {SDN ER - TY - CONF AU - Blanckenstein, Johannes AU - Nardin, Cristina AU - Klaue, Jirka AU - Karl, Holger ID - 742 T2 - {IEEE} International Conference on Communication, {ICC} 2015, London, United Kingdom, June 8-12, 2015, Workshop Proceedings TI - Error characterization of multi-access point WSNs in an aircraft cabin ER - TY - CONF AU - Schwabe, Arne AU - Karl, Holger ID - 743 T2 - 2015 IEEE International Conference on Communications, ICC 2015, London, United Kingdom, June 8-12, 2015 TI - Topology model to generate realistic latency for simulations ER -