@inproceedings{50066, author = {{Dou, Feng and Wang, Lin and Chen, Shutong and Liu, Fangming}}, booktitle = {{Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)}}, location = {{Vancouver, Canada}}, publisher = {{IEEE}}, title = {{{X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics}}}, year = {{2024}}, } @inproceedings{50065, author = {{Blöcher, Marcel and Nedderhut, Nils and Chuprikov, Pavel and Khalili, Ramin and Eugster, Patrick and Wang, Lin}}, booktitle = {{Proceedings of the IEEE International Conference on Computer Communications (INFOCOM)}}, location = {{Vancouver, Canada}}, publisher = {{IEEE}}, title = {{{Train Once Apply Anywhere: Effective Scheduling for Network Function Chains Running on FUMES}}}, year = {{2024}}, } @inproceedings{50807, author = {{Hu, Haichuan and Liu, Fangming and Pei, Qiangyu and Yuan, Yongjie and Xu, Zichen and Wang, Lin}}, booktitle = {{Proceedings of the ACM Web Conference (WWW)}}, location = {{Singapore}}, publisher = {{ACM}}, title = {{{𝜆Grapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing}}}, year = {{2024}}, } @phdthesis{29672, author = {{Schneider, Stefan Balthasar}}, title = {{{Network and Service Coordination: Conventional and Machine Learning Approaches"}}}, doi = {{10.17619/UNIPB/1-1276 }}, year = {{2022}}, } @inproceedings{30236, abstract = {{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.}}, author = {{Schneider, Stefan Balthasar and Werner, Stefan and Khalili, Ramin and Hecker, Artur and Karl, Holger}}, booktitle = {{IEEE/IFIP Network Operations and Management Symposium (NOMS)}}, keywords = {{wireless mobile networks, network management, continuous control, cognitive networks, autonomous coordination, reinforcement learning, gym environment, simulation, open source}}, location = {{Budapest}}, publisher = {{IEEE}}, title = {{{mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks}}}, year = {{2022}}, } @inproceedings{32811, abstract = {{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.}}, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{Proceedings of the 58th Allerton Conference on Communication, Control, and Computing}}, title = {{{Age of Information Process under Strongly Mixing Communication -- Moment Bound, Mixing Rate and Strong Law}}}, year = {{2022}}, } @inproceedings{30793, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{Proceedings of the 14th International Conference on Agents and Artificial Intelligence}}, publisher = {{SCITEPRESS - Science and Technology Publications}}, title = {{{Multi-agent Policy Gradient Algorithms for Cyber-physical Systems with Lossy Communication}}}, doi = {{10.5220/0010845400003116}}, year = {{2022}}, } @unpublished{30790, abstract = {{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.}}, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{arXiv:2201.11343}}, title = {{{Distributed gradient-based optimization in the presence of dependent aperiodic communication}}}, year = {{2022}}, } @unpublished{30791, abstract = {{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.}}, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, booktitle = {{arXiv:2201.00570}}, title = {{{Asymptotic Convergence of Deep Multi-Agent Actor-Critic Algorithms}}}, year = {{2022}}, } @article{32854, author = {{Redder, Adrian and Ramaswamy, Arunselvan and Karl, Holger}}, journal = {{IFAC-PapersOnLine}}, number = {{13}}, pages = {{133–138}}, publisher = {{Elsevier}}, title = {{{Practical Network Conditions for the Convergence of Distributed Optimization}}}, volume = {{55}}, year = {{2022}}, } @inproceedings{29220, abstract = {{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.}}, author = {{Werner, Stefan and Schneider, Stefan Balthasar and Karl, Holger}}, booktitle = {{IEEE/IFIP Network Operations and Management Symposium (NOMS)}}, keywords = {{network management, service management, AI, Monte Carlo Tree Search, model-based, QoS}}, location = {{Budapest}}, publisher = {{IEEE}}, title = {{{Use What You Know: Network and Service Coordination Beyond Certainty}}}, year = {{2022}}, } @inproceedings{20125, abstract = {{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.}}, author = {{Hasnain, Asif and Karl, Holger}}, booktitle = {{2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)}}, keywords = {{Flow scheduling, Deadlines, Reinforcement learning}}, location = {{Las Vegas, USA}}, publisher = {{IEEE Computer Society}}, title = {{{Learning Flow Scheduling}}}, doi = {{https://doi.org/10.1109/CCNC49032.2021.9369514}}, year = {{2021}}, } @phdthesis{27503, author = {{Hasnain, Asif}}, title = {{{Automating Network Resource Allocation for Coflows with Deadlines}}}, doi = {{10.17619/UNIPB/1-1241 }}, year = {{2021}}, } @inproceedings{21005, abstract = {{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.}}, author = {{Hasnain, Asif and Karl, Holger}}, booktitle = {{IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)}}, keywords = {{Coflow scheduling, Reinforcement learning, Deadlines}}, location = {{Vancouver BC Canada}}, publisher = {{IEEE Communications Society}}, title = {{{Learning Coflow Admissions}}}, doi = {{10.1109/INFOCOMWKSHPS51825.2021.9484599}}, year = {{2021}}, } @inproceedings{21543, abstract = {{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).}}, author = {{Schneider, Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}}, booktitle = {{IEEE International Conference on Distributed Computing Systems (ICDCS)}}, keywords = {{network management, service management, coordination, reinforcement learning, distributed}}, location = {{Washington, DC, USA}}, publisher = {{IEEE}}, title = {{{Distributed Online Service Coordination Using Deep Reinforcement Learning}}}, year = {{2021}}, } @inproceedings{20693, abstract = {{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.}}, author = {{Schneider, Stefan Balthasar and Jürgens, Mirko and Karl, Holger}}, booktitle = {{IFIP/IEEE International Symposium on Integrated Network Management (IM)}}, keywords = {{network management, service management, coordination, hierarchical, scalability, nfv}}, location = {{Bordeaux, France}}, publisher = {{IFIP/IEEE}}, title = {{{Divide and Conquer: Hierarchical Network and Service Coordination}}}, year = {{2021}}, } @article{21808, abstract = {{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.}}, author = {{Schneider, Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Hecker, Artur}}, journal = {{Transactions on Network and Service Management}}, keywords = {{network management, service management, coordination, reinforcement learning, self-learning, self-adaptation, multi-objective}}, publisher = {{IEEE}}, title = {{{Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning}}}, doi = {{10.1109/TNSM.2021.3076503}}, year = {{2021}}, } @techreport{33854, abstract = {{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.}}, author = {{Schneider, Stefan Balthasar and Karl, Holger and Khalili, Ramin and Hecker, Artur}}, keywords = {{mobility management, coordinated multipoint, CoMP, cell selection, resource management, reinforcement learning, multi agent, MARL, self-learning, self-adaptation, QoE}}, title = {{{DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning}}}, year = {{2021}}, } @techreport{35889, abstract = {{Network and service coordination is important to provide modern services consisting of multiple interconnected components, e.g., in 5G, network function virtualization (NFV), or cloud and edge computing. In this paper, I outline my dissertation research, which proposes six approaches to automate such network and service coordination. All approaches dynamically react to the current demand and optimize coordination for high service quality and low costs. The approaches range from centralized to distributed methods and from conventional heuristic algorithms and mixed-integer linear programs to machine learning approaches using supervised and reinforcement learning. I briefly discuss their main ideas and advantages over other state-of-the-art approaches and compare strengths and weaknesses.}}, author = {{Schneider, Stefan Balthasar}}, keywords = {{nfv, coordination, machine learning, reinforcement learning, phd, digest}}, title = {{{Conventional and Machine Learning Approaches for Network and Service Coordination}}}, year = {{2021}}, } @inproceedings{19607, abstract = {{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).}}, author = {{Schneider, Stefan Balthasar and Klenner, Lars Dietrich and Karl, Holger}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{distributed management, service coordination, network coordination, nfv, softwarization, orchestration}}, publisher = {{IEEE}}, title = {{{Every Node for Itself: Fully Distributed Service Coordination}}}, year = {{2020}}, } @inproceedings{19609, abstract = {{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.}}, author = {{Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{self-driving networks, self-learning, network coordination, service coordination, reinforcement learning, deep learning, nfv}}, publisher = {{IEEE}}, title = {{{Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}}}, year = {{2020}}, } @inproceedings{17082, abstract = {{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.}}, author = {{Hasnain, Asif and Karl, Holger}}, booktitle = {{2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID)}}, keywords = {{Coflow, Scheduling, Deadlines, Data centers}}, location = {{Melbourne, Australia}}, publisher = {{IEEE Computer Society}}, title = {{{Coflow Scheduling with Performance Guarantees for Data Center Applications}}}, doi = {{https://doi.org/10.1109/CCGrid49817.2020.00010}}, year = {{2020}}, } @inproceedings{16219, abstract = {{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.}}, author = {{Schneider, Stefan Balthasar and Satheeschandran, Narayanan Puthenpurayil and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft)}}, location = {{Ghent, Belgium}}, publisher = {{IEEE}}, title = {{{Machine Learning for Dynamic Resource Allocation in Network Function Virtualization}}}, year = {{2020}}, } @inproceedings{16222, author = {{Zafeiropoulos, A. and Fotopoulou, E. and Peuster, Manuel and Schneider, Stefan Balthasar and Gouvas, P. and Behnke, D. and Müller, M. and Bök, P. and Trakadas, P. and Karkazis, P. and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft)}}, title = {{{Benchmarking and Profiling 5G Verticals' Applications: An Industrial IoT Use Case}}}, year = {{2020}}, } @article{16278, abstract = {{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.}}, author = {{Nomikos, Nikolaos and Michailidis, Emmanouel T. and Trakadas, Panagiotis and Vouyioukas, Demosthenes and Karl, Holger and Martrat, Josep and Zahariadis, Theodore and Papadopoulos, Konstantinos and Voliotis, Stamatis}}, issn = {{2214-2096}}, journal = {{Vehicular Communications}}, title = {{{A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices}}}, doi = {{10.1016/j.vehcom.2020.100250}}, year = {{2020}}, } @article{16280, abstract = {{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.}}, author = {{Li, Feng and Yu, Dongxiao and Yang, Huan and Yu, Jiguo and Karl, Holger and Cheng, Xiuzhen}}, issn = {{1536-1284}}, journal = {{IEEE Wireless Communications}}, pages = {{24--30}}, title = {{{Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey}}}, doi = {{10.1109/mwc.001.1900280}}, year = {{2020}}, } @inproceedings{16400, abstract = {{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.}}, author = {{Müller, Marcel and Behnke, Daniel and Bök, Patrick-Benjamin and Schneider, Stefan Balthasar and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization (NetSoft) Demo Track}}, location = {{Ghent, Belgium}}, publisher = {{IEEE}}, title = {{{Cloud-Native Threat Detection and Containment for Smart Manufacturing}}}, year = {{2020}}, } @article{13770, author = {{Karl, Holger and Kundisch, Dennis and Meyer auf der Heide, Friedhelm and Wehrheim, Heike}}, journal = {{Business & Information Systems Engineering}}, number = {{6}}, pages = {{467--481}}, publisher = {{Springer}}, title = {{{A Case for a New IT Ecosystem: On-The-Fly Computing}}}, doi = {{10.1007/s12599-019-00627-x}}, volume = {{62}}, year = {{2020}}, } @inproceedings{3287, abstract = {{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.}}, author = {{Schneider, Stefan Balthasar and Sharma, Arnab and Karl, Holger and Wehrheim, Heike}}, booktitle = {{2019 IFIP/IEEE International Symposium on Integrated Network Management (IM)}}, location = {{Washington, DC, USA}}, pages = {{116----124}}, publisher = {{IFIP}}, title = {{{Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets}}}, year = {{2019}}, } @inproceedings{9270, abstract = {{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.}}, author = {{Schneider, Stefan Balthasar and Peuster, Manuel and Behnke, Daniel and Marcel, Müller and Bök, Patrick-Benjamin and Karl, Holger}}, booktitle = {{European Conference on Networks and Communications (EuCNC)}}, keywords = {{5g, vertical, smart manufacturing, nfv}}, publisher = {{IEEE}}, title = {{{Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario}}}, doi = {{10.1109/eucnc.2019.8802016}}, year = {{2019}}, } @article{8113, abstract = {{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.}}, author = {{Peuster, Manuel and Dröge, Christian and Boos, Clemens and Karl, Holger}}, issn = {{2405-9595}}, journal = {{ICT Express}}, publisher = {{Elsevier BV}}, title = {{{Joint testing and profiling of microservice-based network services using TTCN-3}}}, doi = {{10.1016/j.icte.2019.02.001}}, year = {{2019}}, } @inproceedings{8240, author = {{Dräxler, Sevil and Karl, Holger}}, booktitle = {{5th IEEE International Conference on Network Softwarization (NetSoft) 2019}}, location = {{Paris}}, title = {{{SPRING: Scaling, Placement, and Routing of Heterogeneous Services with Flexible Structures}}}, year = {{2019}}, } @inproceedings{8792, abstract = {{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.}}, author = {{Peuster, Manuel and Schneider, Stefan Balthasar and Behnke, Daniel and Müller, Marcel and Bök, Patrick-Benjamin and Karl, Holger}}, booktitle = {{5th IEEE International Conference on Network Softwarization (NetSoft 2019)}}, location = {{Paris}}, title = {{{Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case}}}, doi = {{10.1109/NETSOFT.2019.8806685}}, year = {{2019}}, } @article{8795, abstract = {{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.}}, author = {{Peuster, Manuel and Küttner, Hannes and Karl, Holger}}, issn = {{1055-7148}}, journal = {{International Journal of Network Management}}, title = {{{A flow handover protocol to support state migration in softwarized networks}}}, doi = {{10.1002/nem.2067}}, year = {{2019}}, } @article{9823, author = {{Soenen, Thomas and Tavernier, Wouter and Peuster, Manuel and Vicens, Felipe and Xilouris, George and Kolometsos, Stavros and Kourtis, Michail-Alexandros and Colle, Didier}}, issn = {{0163-6804}}, journal = {{IEEE Communications Magazine}}, pages = {{89--95}}, title = {{{Empowering Network Service Developers: Enhanced NFV DevOps and Programmable MANO}}}, doi = {{10.1109/mcom.2019.1800810}}, year = {{2019}}, } @article{9824, author = {{Peuster, Manuel and Schneider, Stefan Balthasar and Zhao, Mengxuan and Xilouris, George and Trakadas, Panagiotis and Vicens, Felipe and Tavernier, Wouter and Soenen, Thomas and Vilalta, Ricard and Andreou, George and Kyriazis, Dimosthenis and Karl, Holger}}, issn = {{0163-6804}}, journal = {{IEEE Communications Magazine}}, pages = {{96--102}}, title = {{{Introducing Automated Verification and Validation for Virtualized Network Functions and Services}}}, doi = {{10.1109/mcom.2019.1800873}}, year = {{2019}}, } @inproceedings{6860, author = {{Afifi, Haitham and Karl, Holger}}, booktitle = {{2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019)}}, publisher = {{IEEE}}, title = {{{Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network Embedding}}}, year = {{2019}}, } @inproceedings{12880, abstract = {{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.}}, author = {{Guenther, Michael and Afifi, Haitham and Brendel, Andreas and Karl, Holger and Kellermann, Walter}}, booktitle = {{2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (WASPAA 2019)}}, title = {{{Sparse Adaptation of Distributed Blind Source Separation in Acoustic Sensor Networks}}}, year = {{2019}}, } @inproceedings{12881, abstract = {{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.}}, author = {{Afifi, Haitham and Karl, Holger}}, booktitle = {{2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) (WMNC'19)}}, title = {{{An Approximate Power Control Algorithm for a Multi-Cast Wireless Virtual Network Embedding}}}, year = {{2019}}, } @inproceedings{12882, abstract = {{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.}}, author = {{Afifi, Haitham and Karl, Holger and Eikenberg, Sebastian and Mueller, Arnold and Gansel, Lars and Makejkin, Alexander and Hannemann, Kai and Schellenberg, Rafael}}, booktitle = {{2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo)}}, keywords = {{WSN, virtualization, VNE}}, title = {{{A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO}}}, year = {{2019}}, } @inproceedings{15369, author = {{Müller, Marcel and Behnke, Daniel and Bök, Patrick-Benjamin and Peuster, Manuel and Schneider, Stefan Balthasar and Karl, Holger}}, booktitle = {{IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN)}}, publisher = {{IEEE}}, title = {{{5G as Key Technology for Networked Factories: Application of Vertical-specific Network Services for Enabling Flexible Smart Manufacturing}}}, year = {{2019}}, } @inproceedings{15371, abstract = {{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. }}, author = {{Peuster, Manuel and Schneider, Stefan Balthasar and Karl, Holger}}, booktitle = {{IEEE/IFIP 15th International Conference on Network and Service Management (CNSM)}}, publisher = {{IEEE/IFIP}}, title = {{{The Softwarised Network Data Zoo}}}, year = {{2019}}, } @inproceedings{15372, author = {{Nuriddinov, Askhat and Tavernier, Wouter and Colle, Didier and Pickavet, Mario and Peuster, Manuel and Schneider, Stefan Balthasar}}, booktitle = {{ IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)}}, publisher = {{IEEE}}, title = {{{Reproducible Functional Tests for Multi-scale Network Services}}}, year = {{2019}}, } @inproceedings{15373, abstract = {{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.}}, author = {{Moro, Daniele and Peuster, Manuel and Karl, Holger and Capone, Antonio}}, booktitle = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)}}, publisher = {{IEEE}}, title = {{{FOP4: Function Offloading Prototyping in Heterogeneous and Programmable Network Scenarios}}}, year = {{2019}}, } @inproceedings{15374, abstract = {{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.}}, author = {{Moro, Daniele and Peuster, Manuel and Karl, Holger and Capone, Antonio}}, booktitle = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)}}, publisher = {{IEEE}}, title = {{{Demonstrating FOP4: A Flexible Platform to Prototype NFV Offloading Scenarios}}}, year = {{2019}}, } @inproceedings{15375, author = {{Müller, Marcel and Behnke, Daniel and Bök, Patrick-Benjamin and Schneider, Stefan Balthasar and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)}}, publisher = {{IEEE}}, title = {{{Putting NFV into Reality: Physical Smart Manufacturing Testbed}}}, year = {{2019}}, } @inproceedings{15376, author = {{Behnke, Daniel and Müller, Marcel and Bök, Patrick-Benjamin and Schneider, Stefan Balthasar and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)}}, publisher = {{IEEE}}, title = {{{NFV-driven intrusion detection for smart manufacturing}}}, year = {{2019}}, } @article{15741, abstract = {{ 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}}, author = {{Leong, Alex S. and Ramaswamy, Arunselvan and Quevedo, Daniel E. and Karl, Holger and Shi, Ling}}, issn = {{0005-1098}}, journal = {{Automatica}}, title = {{{Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems}}}, doi = {{10.1016/j.automatica.2019.108759}}, year = {{2019}}, } @inproceedings{13123, abstract = {{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.}}, author = {{Afifi, Haitham and Horbach, Konrad and Karl, Holger}}, booktitle = {{2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (WiMob 2019)}}, title = {{{A Genetic Algorithm Framework for Solving Wireless Virtual Network Embedding}}}, year = {{2019}}, } @phdthesis{13124, author = {{Dräxler, Sevil}}, publisher = {{Universität Paderborn}}, title = {{{Scaling, placement, and routing for pliable virtualized composed services}}}, year = {{2019}}, } @inproceedings{13292, abstract = {{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.}}, author = {{Schneider, Stefan Balthasar and Peuster, Manuel and Hannemann, Kai and Behnke, Daniel and Müller, Marcel and Bök, Patrick-Benjamin and Karl, Holger}}, booktitle = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Demo Track}}, keywords = {{5G, NFV, Smart Manufacturing, Cloud-Native, Kubernetes}}, location = {{Dallas, TX, USA}}, publisher = {{IEEE}}, title = {{{"Producing Cloud-Native": Smart Manufacturing Use Cases on Kubernetes}}}, year = {{2019}}, } @article{10325, author = {{Peuster, Manuel and Marchetti, Michael and García de Blas, Gerardo and Karl, Holger}}, issn = {{1687-1499}}, journal = {{EURASIP Journal on Wireless Communications and Networking}}, publisher = {{Springer}}, title = {{{Automated testing of NFV orchestrators against carrier-grade multi-PoP scenarios using emulation-based smoke testing}}}, doi = {{10.1186/s13638-019-1493-2}}, year = {{2019}}, } @inproceedings{2474, author = {{Afifi, Haitham and Auroux, Sébastien and Karl, Holger}}, publisher = {{Proc. of IEEE Wireless Communications and Networking Conference (WCNC)}}, title = {{{MARVELO: Wireless Virtual Network Embedding for Overlay Graphs with Loops}}}, year = {{2018}}, } @inproceedings{2476, author = {{Shiferaw Heyi, Binyam and Karl, Holger}}, publisher = {{Proc. of IEEE Wireless Communications and Networking Conference (WCNC)}}, title = {{{Modelling Time-Limited Capacity of a Wireless Channel as aMarkov Reward Process}}}, year = {{2018}}, } @inproceedings{2480, abstract = {{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.}}, author = {{Dräxler, Sevil and Peuster, Manuel and Illian, Marvin and Karl, Holger}}, booktitle = {{4th IEEE International Conference on Network Softwarization (NetSoft 2018)}}, location = {{Montreal}}, pages = {{318----322}}, publisher = {{IEEE}}, title = {{{Generating Resource and Performance Models for Service Function Chains: The Video Streaming Case}}}, doi = {{10.1109/NETSOFT.2018.8460029}}, year = {{2018}}, } @inproceedings{2481, abstract = {{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.}}, author = {{Dräxler, Sevil and Schneider, Stefan Balthasar and Karl, Holger}}, booktitle = {{4th IEEE International Conference on Network Softwarization (NetSoft 2018)}}, location = {{Montreal}}, pages = {{123----131}}, publisher = {{IEEE}}, title = {{{ Scaling and Placing Bidirectional Services with Stateful Virtual and Physical Network Functions}}}, year = {{2018}}, } @techreport{2483, abstract = {{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.}}, author = {{Dräxler, Sevil and Peuster, Manuel and Illian, Marvin and Karl, Holger}}, title = {{{Towards Predicting Resource Demands and Performance of Distributed Cloud Services}}}, year = {{2018}}, } @inproceedings{2472, author = {{Auroux, Sébastien and Karl, Holger}}, publisher = {{Proc. of IEEE Wireless Communications and Networking Conference (WCNC)}}, title = {{{Distributed Placement of Virtualized Control Applications in Mobile Backhaul Networks}}}, doi = {{ 10.1109/WCNC.2018.8377335}}, year = {{2018}}, } @inproceedings{3217, author = {{Demirel, Burak and Ramaswamy, Arunselvan and Quevedo, Daniel and Karl, Holger}}, title = {{{DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling}}}, doi = {{10.1109/LCSYS.2018.2847721}}, year = {{2018}}, } @inproceedings{3345, abstract = {{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.}}, author = {{Peuster, Manuel and Küttner, Hannes and Karl, Holger}}, booktitle = {{4th IEEE International Conference on Network Softwarization (NetSoft 2018)}}, location = {{Montreal}}, title = {{{ Let the state follow its flows: An SDN-based flow handover protocol to support state migration}}}, doi = {{10.1109/NETSOFT.2018.8460007}}, year = {{2018}}, } @inproceedings{3346, abstract = {{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.}}, author = {{Peuster, Manuel and Kampmeyer, Johannes and Karl, Holger}}, booktitle = {{4th IEEE International Conference on Network Softwarization (NetSoft 2018)}}, location = {{Montreal}}, title = {{{Containernet 2.0: A Rapid Prototyping Platform for Hybrid Service Function Chains}}}, doi = {{10.1109/NETSOFT.2018.8459905}}, year = {{2018}}, } @inproceedings{3347, abstract = {{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.}}, author = {{Peuster, Manuel and Marchetti, Michael and Garcia de Blas, Gerado and Karl, Holger}}, booktitle = {{European Conference on Networks and Communications (EuCNC)}}, location = {{Ljubljana}}, title = {{{Emulation-based Smoke Testing of NFV Orchestrators in Large Multi-PoP Environments}}}, doi = {{10.1109/EuCNC.2018.8442701}}, year = {{2018}}, } @article{3152, abstract = {{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.}}, author = {{Dräxler, Sevil and Karl, Holger and Mann, Zoltan Adam}}, journal = {{IEEE Transactions on Network and Service Management}}, publisher = {{IEEE}}, title = {{{JASPER: Joint Optimization of Scaling, Placement, and Routing of Virtual Network Services}}}, doi = {{10.1109/TNSM.2018.2846572}}, year = {{2018}}, } @inproceedings{6016, author = {{Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE/IFIP 14th International Conference on Network and Service Management (CNSM)}}, location = {{Rome}}, publisher = {{IEEE/IFIP}}, title = {{{Understand your chains and keep your deadlines: Introducing time-constrained profiling for NFV}}}, year = {{2018}}, } @inproceedings{6483, author = {{Peuster, Manuel and Schneider, Stefan Balthasar and Christ, Frederic and Karl, Holger}}, booktitle = {{IEEE Conference on Network Function Virtualisation and Software Defined Networks (NFV-SDN) 5GNetApp}}, location = {{Verona}}, publisher = {{IEEE}}, title = {{{A Prototyping Platform to Validate and Verify Network Service Header-based Service Chains}}}, year = {{2018}}, } @techreport{6485, author = {{Rosa, Raphael Vicente and Rothenberg, Christian Esteve and Peuster, Manuel and Karl, Holger}}, publisher = {{IETF}}, title = {{{Methodology for VNF Benchmarking Automation}}}, year = {{2018}}, } @inproceedings{6970, abstract = {{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.}}, author = {{Schneider, Stefan Balthasar and Dräxler, Sevil and Karl, Holger}}, booktitle = {{IEEE Global Communications Conference (GLOBECOM 2018)}}, location = {{Abu Dhabi, UAE}}, publisher = {{IEEE}}, title = {{{Trade-offs in Dynamic Resource Allocation in Network Function Virtualization}}}, year = {{2018}}, } @inproceedings{6972, abstract = {{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.}}, author = {{Schneider, Stefan Balthasar and Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN 2018)}}, location = {{Verona, Italy}}, publisher = {{IEEE}}, title = {{{A Generic Emulation Framework for Reusing and Evaluating VNF Placement Algorithms}}}, doi = {{10.1109/NFV-SDN.2018.8725795}}, year = {{2018}}, } @inproceedings{6974, abstract = {{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.}}, author = {{Schneider, Stefan Balthasar and Peuster, Manuel and Tavernier, Wouter and Karl, Holger}}, booktitle = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN 2018)}}, location = {{Verona, Italy}}, publisher = {{IEEE}}, title = {{{A Fully Integrated Multi-Platform NFV SDK}}}, doi = {{10.1109/NFV-SDN.2018.8725794}}, year = {{2018}}, } @phdthesis{1208, author = {{Schwabe, Arne}}, publisher = {{Universität Paderborn}}, title = {{{Data-Centre Traffic Optimisation using Software-Defined Networks}}}, doi = {{10.17619/UNIPB/1-287}}, year = {{2018}}, } @inproceedings{6859, abstract = {{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.}}, author = {{Afifi, Haitham and Schmalenstroeer, Joerg and Ullmann, Joerg and Haeb-Umbach, Reinhold and Karl, Holger}}, booktitle = {{Speech Communication; 13th ITG-Symposium}}, pages = {{1--5}}, title = {{{MARVELO - A Framework for Signal Processing in Wireless Acoustic Sensor Networks}}}, year = {{2018}}, } @misc{3291, abstract = {{The microservice architecture uses independently running microservices as build- ing blocks for applications. These microservices are clearly bounded for each other and expose their functionality through, for instance, RESTful application inter- faces. Particularly the clear boundaries between microservices enable the reuse of microservice throughout different projects. Because of the increasing use of microservices, the composition of multiple microservices in service composition becomes a more important task. A challenging area in developing service compo- sitions is that it involves two distinct layers with few junctions. On the one hand, describes a service composition a business process, which involves multiple com- ponents. On the other hand, involves the implementation of a service composition topics like service discovery and message exchange protocols since the microser- vices involved in a service composition are located within a network environment. In this Bachelor’s Thesis, I describe a descriptions language to abstractly describe the business logic of a service composition. Furthermore, I describe a genera- tion process, which compiles this abstract description to a working microservice realizing the specified service composition. In addition to that, I provide an im- plementation of the generation process, as a proof of concept, and test it within a Kubernetes-based cluster environment.}}, author = {{Schürmann, Andreas}}, publisher = {{Universität Paderborn}}, title = {{{Microservice-based Execution Environment for Service Compositions}}}, year = {{2017}}, } @inproceedings{2741, author = {{Ali Ashraf, Shehzad and Wang, Y.-P. Eric and Eldessoki, Sameh and Holfeld, Bernd and Parruca, Donald and Serror, Martin and Gross, James}}, publisher = {{Proceedings of 23th European Wireless Conference 2017, 17- 19.05.2017}}, title = {{{From Radio Design to System Evaluations for Ultra-Reliable and Low-Latency Communication }}}, year = {{2017}}, } @article{58, abstract = {{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.}}, author = {{Dräxler, Sevil and Karl, Holger}}, journal = {{International Journal of Network Management}}, number = {{2}}, pages = {{1----16}}, publisher = {{Wiley Online Library}}, title = {{{Specification, Composition, and Placement of Network Services with Flexible Structures}}}, doi = {{10.1002/nem.1963}}, year = {{2017}}, } @article{64, abstract = {{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.}}, author = {{Keller, Matthias and Karl, Holger}}, journal = {{IEEE Transactions on Network and Service Management}}, number = {{1}}, pages = {{121----135}}, publisher = {{IEEE}}, title = {{{Response-Time-Optimised Service Deployment: MILP Formulations of Piece-wise Linear Functions Approximating Non-linear Bivariate Mixed-integer Functions}}}, doi = {{10.1109/TNSM.2016.2611590}}, year = {{2017}}, } @inproceedings{708, author = {{Schwabe, Arne and Rojas, Elisa and Karl, Holger}}, booktitle = {{2017 {IEEE} Conference on Network Softwarization, NetSoft 2017, Bologna, Italy, July 3-7, 2017}}, location = {{Bologna}}, pages = {{1----5}}, title = {{{Minimizing downtimes: Using dynamic reconfiguration and state management in SDN}}}, doi = {{10.1109/NETSOFT.2017.8004209}}, year = {{2017}}, } @inproceedings{717, abstract = {{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.}}, author = {{Dräxler, Sevil and Karl, Holger and Peuster, Manuel and Razzaghi Kouchaksaraei, Hadi and Bredel, Michael and Lessmann, Johannes and Soenen, Thomas and Tavernier, Wouter and Mendel-Brin, Sharon and Xilouris, George}}, booktitle = {{2017 IEEE International Conference on Communications Workshops (ICC Workshops)}}, isbn = {{9781509015252}}, location = {{Paris, France}}, publisher = {{IEEE}}, title = {{{SONATA: Service programming and orchestration for virtualized software networks}}}, doi = {{10.1109/iccw.2017.7962785}}, year = {{2017}}, } @inproceedings{723, abstract = {{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.}}, author = {{Peuster, Manuel and Dräxler, Sevil and Razzaghi Kouchaksaraei, Hadi and van Rossem, Steven and Tavernier, Wouter and Karl, Holger}}, booktitle = {{IEEE Conference on Network Softwarization, NetSoft 2017, Bologna, Italy, July 3-7, 2017}}, location = {{Bologna}}, pages = {{1----3}}, title = {{{A flexible multi-pop infrastructure emulator for carrier-grade MANO systems}}}, doi = {{10.1109/NETSOFT.2017.8004250}}, year = {{2017}}, } @inproceedings{87, abstract = {{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.}}, author = {{Dräxler, Sevil and Karl, Holger and Mann, Zoltan Adam}}, booktitle = {{Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2017)}}, title = {{{Joint Optimization of Scaling and Placement of Virtual Network Services}}}, doi = {{10.1109/CCGRID.2017.25}}, year = {{2017}}, } @inproceedings{981, abstract = {{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. }}, author = {{Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Function Virtualisation and Software Defined Networks (NFV-SDN)}}, location = {{Berlin}}, title = {{{Profile Your Chains, Not Functions. Automated Network Service Profiling in DevOps Environments}}}, doi = {{10.1109/NFV-SDN.2017.8169826}}, year = {{2017}}, } @inproceedings{983, author = {{Auroux, Sébastien and Scholz, S. and Karl, Holger}}, booktitle = {{Proc. European Wireless}}, title = {{{Assessing Genetic Algorithms for Placing Flow Processing-aware Control Applications}}}, year = {{2017}}, } @inproceedings{1618, author = {{Zhao, Mengxuan and Le Gall, Franck and Cousin, Philippe and Vilalta, Ricard and Munoz, Raul and Castro, Sonia and Peuster, Manuel and Schneider, Stefan Balthasar and Siapera, Maria and Kapassa, Evgenia and Kyriazis, Dimosthenis and Hasselmeyer, Peer and Xilouris, George and Tranoris, Christos and Denazis, Spyros and Martrat, Josep}}, booktitle = {{2017 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)}}, isbn = {{9781538632857}}, publisher = {{IEEE}}, title = {{{Verification and validation framework for 5G network services and apps}}}, doi = {{10.1109/nfv-sdn.2017.8169878}}, year = {{2017}}, } @inproceedings{1620, author = {{Aktas, Ismet and Ansari, Junaid and Auroux, Sebastien and Parruca, Donald and Perez Guirao, Maria Dolores and Holfeld, Bernd}}, publisher = {{Proceedings of 23th European Wireless Conference}}, title = {{{A Coordination Architecture for Wireless Industrial Automation}}}, year = {{2017}}, } @phdthesis{220, author = {{Keller, Matthias}}, publisher = {{Universität Paderborn}}, title = {{{Application Deployment at Distributed Clouds}}}, year = {{2016}}, } @article{714, abstract = {{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.}}, author = {{Karl, Holger and Dräxler, Sevil and Peuster, Manuel and Galis, Alex and Bredel, Michael and Ramos, Aurora and Martrat, Josep and Siddiqui, Muhammad Shuaib and van Rossem, Steven and Tavernier, Wouter and Xilouris, George}}, issn = {{2161-3915}}, journal = {{Transactions on Emerging Telecommunications Technologies}}, number = {{9}}, pages = {{1206--1215}}, publisher = {{Wiley-Blackwell}}, title = {{{DevOps for network function virtualisation: an architectural approach}}}, doi = {{10.1002/ett.3084}}, volume = {{27}}, year = {{2016}}, } @article{726, author = {{Wette, Philip and Karl, Holger}}, journal = {{Computer Communications}}, pages = {{45----58}}, title = {{{DCT²Gen: A traffic generator for data centers}}}, doi = {{10.1016/j.comcom.2015.12.001}}, year = {{2016}}, } @inproceedings{728, author = {{Schwabe, Arne and A. Aranda-Gutierrez, Pedro and Karl, Holger}}, booktitle = {{Proceedings of the 2016 Applied Networking Research Workshop, {ANRW} 2016, Berlin, Germany, July 16, 2016}}, pages = {{26----31}}, title = {{{Composition of SDN applications: Options/challenges for real implementations}}}, doi = {{10.1145/2959424.2959436}}, year = {{2016}}, } @inproceedings{729, author = {{Doriguzzi Corin, Roberto and A. Aranda-Gutierrez, Pedro and Rojas, Elisa and Karl, Holger and Salvadori, Elio}}, booktitle = {{12th International Conference on Network and Service Management, {CNSM} 2016, Montreal, QC, Canada, October 31 - Nov. 4, 2016}}, pages = {{209----215}}, title = {{{Reusability of software-defined networking applications: {A} runtime, multi-controller approach}}}, doi = {{10.1109/CNSM.2016.7818419}}, year = {{2016}}, } @inproceedings{730, abstract = {{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.}}, author = {{Peuster, Manuel and Karl, Holger}}, booktitle = {{Fifth European Workshop on Software-Defined Networks, EWSDN 2016, Den Haag, The Netherlands, October 10-11, 2016}}, location = {{Den Haag}}, pages = {{7----12}}, title = {{{Understand Your Chains: Towards Performance Profile-Based Network Service Management}}}, doi = {{10.1109/EWSDN.2016.9}}, year = {{2016}}, } @inproceedings{731, abstract = {{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. }}, author = {{Peuster, Manuel and Karl, Holger and Enrico Redondi, Alessandro and Capone, Antonio}}, booktitle = {{IEEE Conference on Computer Communications Workshops, INFOCOM Workshops 2016, San Francisco, CA, USA, April 10-14, 2016}}, location = {{San Francisco}}, pages = {{1015----1016}}, title = {{{Demonstrating on-demand cell switching with a two-layer mobile network testbed}}}, doi = {{10.1109/INFCOMW.2016.7562232}}, year = {{2016}}, } @inproceedings{732, abstract = {{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.}}, author = {{Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE NetSoft Conference and Workshops, NetSoft 2016, Seoul, South Korea, June 6-10, 2016}}, location = {{Seoul}}, pages = {{6----10}}, title = {{{E-State: Distributed state management in elastic network function deployments}}}, doi = {{10.1109/NETSOFT.2016.7502432}}, year = {{2016}}, } @inproceedings{735, author = {{Auroux, Sébastien and Parruca, Donald and Karl, Holger}}, booktitle = {{27th IEEE Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, {PIMRC} 2016, Valencia, Spain, September 4-8, 2016}}, pages = {{1----6}}, title = {{{Joint real-time scheduling and interference coordination for wireless factory automation}}}, doi = {{10.1109/PIMRC.2016.7794927}}, year = {{2016}}, } @inproceedings{738, abstract = {{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. }}, author = {{Peuster, Manuel and Karl, Holger and van Rossem, Steven}}, booktitle = {{IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN)}}, location = {{Palo Alto}}, title = {{{MeDICINE: Rapid Prototyping of Production-Ready Network Services in Multi-PoP Environments}}}, doi = {{10.1109/NFV-SDN.2016.7919490}}, year = {{2016}}, } @inproceedings{985, author = {{v. Rossem, S. and Tavernier, W. and Peuster, Manuel and Colle, D. and Pickavet, M. and Demeester, P.}}, booktitle = {{Proc. IEEE Conference on Network Function Virtualization and Software Defined Network (NFV-SDN), Demo Track}}, title = {{{Monitoring and debugging using an SDK for NFV-powered telecom applications}}}, year = {{2016}}, } @inproceedings{166, abstract = {{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.}}, author = {{Dräxler, Sevil and Karl, Holger}}, booktitle = {{Proceedings of the 2nd International IEEE Conference on Network Softwarization (NetSoft)}}, pages = {{184----192}}, title = {{{Placement of Services with Flexible Structures Specified by a YANG Data Model}}}, doi = {{10.1109/NETSOFT.2016.7502412}}, year = {{2016}}, } @inproceedings{1627, author = {{Gutierrez, P. A. Aranda and Rojas, E. and Schwabe, A. and Stritzke, C. and Doriguzzi-Corin, R. and Leckey, A. and Petralia, G. and Marsico, A. and Phemius, K. and Tamurejo, S.}}, booktitle = {{2016 IEEE NetSoft Conference and Workshops (NetSoft)}}, isbn = {{9781467394864}}, publisher = {{IEEE}}, title = {{{NetIDE: All-in-one framework for next generation, composed SDN applications}}}, doi = {{10.1109/netsoft.2016.7502408}}, year = {{2016}}, } @inproceedings{1630, author = {{Marsico, Antonio and Doriguzzi-Corin, Roberto and Gerola, Matteo and Siracusa, Domenico and Schwabe, Arne}}, booktitle = {{NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium}}, isbn = {{9781509002238}}, publisher = {{IEEE}}, title = {{{A non-disruptive automated approach to update SDN applications at runtime}}}, doi = {{10.1109/noms.2016.7502946}}, year = {{2016}}, } @inproceedings{1632, author = {{Doriguzzi-Corin, Roberto and Siracusa, Domenico and Salvador, Elio and Schwabe, Arne}}, booktitle = {{NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium}}, isbn = {{9781509002238}}, publisher = {{IEEE}}, title = {{{Empowering network operating systems with memory management techniques}}}, doi = {{10.1109/noms.2016.7502889}}, year = {{2016}}, } @article{1373, author = {{Herlich, Matthias and Bredenbals, Nico and Karl, Holger}}, issn = {{2210-5379}}, journal = {{Sustainable Computing: Informatics and Systems}}, pages = {{48--55}}, publisher = {{Elsevier BV}}, title = {{{Delayed (de-)activation in servers with a sleep mode}}}, doi = {{10.1016/j.suscom.2016.04.002}}, volume = {{10}}, year = {{2016}}, } @inproceedings{252, abstract = {{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.}}, author = {{Dräxler, Martin and Blobel, Johannes and Dreimann, Philipp and Valentin, Stefan and Karl, Holger}}, booktitle = {{Proceedings of the 2nd International Conference on Networked Systems (NetSys)}}, pages = {{1----8}}, title = {{{SmarterPhones: Anticipatory Download Scheduling for Wireless Video Streaming}}}, doi = {{10.1109/NetSys.2015.7089073}}, year = {{2015}}, }