@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}}, }