@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}}, }