TY - CONF AU - Müller, Marcel AU - Behnke, Daniel AU - Bök, Patrick-Benjamin AU - Peuster, Manuel AU - Schneider, Stefan Balthasar AU - Karl, Holger ID - 15369 T2 - IEEE 17th International Conference on Industrial Informatics (IEEE-INDIN) TI - 5G as Key Technology for Networked Factories: Application of Vertical-specific Network Services for Enabling Flexible Smart Manufacturing ER - TY - CONF AB - More and more management and orchestration approaches for (software) networks are based on machine learning paradigms and solutions. These approaches depend not only on their program code to operate properly, but also require enough input data to train their internal models. However, such training data is barely available for the software networking domain and most presented solutions rely on their own, sometimes not even published, data sets. This makes it hard, or even infeasible, to reproduce and compare many of the existing solutions. As a result, it ultimately slows down the adoption of machine learning approaches in softwarised networks. To this end, we introduce the "softwarised network data zoo" (SNDZoo), an open collection of software networking data sets aiming to streamline and ease machine learning research in the software networking domain. We present a general methodology to collect, archive, and publish those data sets for use by other researches and, as an example, eight initial data sets, focusing on the performance of virtualised network functions. AU - Peuster, Manuel AU - Schneider, Stefan Balthasar AU - Karl, Holger ID - 15371 T2 - IEEE/IFIP 15th International Conference on Network and Service Management (CNSM) TI - The Softwarised Network Data Zoo ER - TY - CONF AU - Nuriddinov, Askhat AU - Tavernier, Wouter AU - Colle, Didier AU - Pickavet, Mario AU - Peuster, Manuel AU - Schneider, Stefan Balthasar ID - 15372 T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) TI - Reproducible Functional Tests for Multi-scale Network Services ER - TY - CONF AB - Offloading packet processing tasks to programmable switches and/or to programmable network interfaces, so called “SmartNICs”, is one of the key concepts to prepare softwarized networks for the high traffic demands of the future. However, implementing network functions that make use of those offload- ing technologies is still challenging and usually requires the availability of specialized hardware. It becomes even harder if heterogeneous services, making use of different offloading and network virtualization technologies, should be developed. In this paper, we introduce FOP4 (Function Offloading Pro- totyping with P4), a novel prototyping platform that allows to prototype heterogeneous software network scenarios, including container-based, P4-switch-based, and SmartNIC-based network functions. The presented work substantially extends our existing Containernet platform with the means to prototype offloading scenarios. Besides presenting the platform’s system design, we evaluate its scalability and show that it can run scenarios with more than 64 P4 switch or SmartNIC nodes on a single laptop. Finally, we presented a case study in which we use the presented platform to prototype an extended in-band network telemetry use case. AU - Moro, Daniele AU - Peuster, Manuel AU - Karl, Holger AU - Capone, Antonio ID - 15373 T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) TI - FOP4: Function Offloading Prototyping in Heterogeneous and Programmable Network Scenarios ER - TY - CONF AB - Emulation platforms supporting Virtual Network Functions (VNFs) allow developers to rapidly prototype network services. None of the available platforms, however, supports experimenting with programmable data planes to enable VNF offloading. In this demonstration, we show FOP4, a flexible platform that provides support for Docker-based VNFs, and VNF offloading, by means of P4-enabled switches. The platform provides interfaces to program the P4 devices and to deploy network functions. We demonstrate FOP4 with two complex example scenarios, demonstrating how developers can exploit data plane programmability to implement network functions. AU - Moro, Daniele AU - Peuster, Manuel AU - Karl, Holger AU - Capone, Antonio ID - 15374 T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) TI - Demonstrating FOP4: A Flexible Platform to Prototype NFV Offloading Scenarios ER - TY - CONF AU - Müller, Marcel AU - Behnke, Daniel AU - Bök, Patrick-Benjamin AU - Schneider, Stefan Balthasar AU - Peuster, Manuel AU - Karl, Holger ID - 15375 T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) TI - Putting NFV into Reality: Physical Smart Manufacturing Testbed ER - TY - CONF AU - Behnke, Daniel AU - Müller, Marcel AU - Bök, Patrick-Benjamin AU - Schneider, Stefan Balthasar AU - Peuster, Manuel AU - Karl, Holger ID - 15376 T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) TI - NFV-driven intrusion detection for smart manufacturing ER - TY - JOUR AB - In many cyber–physical systems, we encounter the problem of remote state estimation of geo- graphically distributed and remote physical processes. This paper studies the scheduling of sensor transmissions to estimate the states of multiple remote, dynamic processes. Information from the different sensors has to be transmitted to a central gateway over a wireless network for monitoring purposes, where typically fewer wireless channels are available than there are processes to be monitored. For effective estimation at the gateway, the sensors need to be scheduled appropriately, i.e., at each time instant one needs to decide which sensors have network access and which ones do not. To address this scheduling problem, we formulate an associated Markov decision process (MDP). This MDP is then solved using a Deep Q-Network, a recent deep reinforcement learning algorithm that is at once scalable and model-free. We compare our scheduling algorithm to popular scheduling algorithms such as round-robin and reduced-waiting-time, among others. Our algorithm is shown to significantly outperform these algorithms for many example scenario AU - Leong, Alex S. AU - Ramaswamy, Arunselvan AU - Quevedo, Daniel E. AU - Karl, Holger AU - Shi, Ling ID - 15741 JF - Automatica SN - 0005-1098 TI - Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems ER - TY - CONF AB - Given the recent development in embedded devices, wireless senor nodes are no longer limited to data collection but they can also do processing (e.g., smartphones). Accordingly, new types of applications take an advantage of the processing and flexibility provided by the wireless network. A common property between these applications is that the processing is not running on only one single node, but it is broken-down into smaller tasks that can run over multiple nodes, i.e., exploiting the in-network processing. We study a special variant of in-network processing, where the application is given by a graph; the processing tasks have predefined connections to be executed in a predefined sequence. The problem of embedding an application graph into a network is commonly known as Virtual Network Embedding (VNE). In this paper, we present a Genetic Algorithm (GA) solution to solve this wireless VNE problem, where we take into account the interference and multi-cast properties. We show that the GA has a good performance and fast execution compared to the optimization problem. AU - Afifi, Haitham AU - Horbach, Konrad AU - Karl, Holger ID - 13123 T2 - 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (WiMob 2019) TI - A Genetic Algorithm Framework for Solving Wireless Virtual Network Embedding ER - TY - THES AU - Dräxler, Sevil ID - 13124 TI - Scaling, placement, and routing for pliable virtualized composed services ER - TY - CONF AB - Building on 5G and network function virtualization (NFV), smart manufacturing has the potential to drastically increase productivity, reduce cost, and introduce novel, flexible manufacturing services. Current work mostly focuses on high-level scenarios or emulation-based prototype deployments. Extending our previous work, we showcase one of the first cloud-native 5G verticals focusing on the deployment of smart manufacturing use cases on production infrastructure. In particular, we use the 5GTANGO service platform to deploy our developed network services on Kubernetes. For this demo, we implemented a series of cloud-native virtualized network functions (VNFs) and created suitable service descriptors. Their light-weight, stateless deployment on Kubernetes enables quick instantiation, scalability, and robustness. AU - Schneider, Stefan Balthasar AU - Peuster, Manuel AU - Hannemann, Kai AU - Behnke, Daniel AU - Müller, Marcel AU - Bök, Patrick-Benjamin AU - Karl, Holger ID - 13292 KW - 5G KW - NFV KW - Smart Manufacturing KW - Cloud-Native KW - Kubernetes T2 - IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) Demo Track TI - "Producing Cloud-Native": Smart Manufacturing Use Cases on Kubernetes ER - TY - JOUR AU - Peuster, Manuel AU - Marchetti, Michael AU - García de Blas, Gerardo AU - Karl, Holger ID - 10325 JF - EURASIP Journal on Wireless Communications and Networking SN - 1687-1499 TI - Automated testing of NFV orchestrators against carrier-grade multi-PoP scenarios using emulation-based smoke testing ER - TY - CONF AU - Afifi, Haitham AU - Auroux, Sébastien AU - Karl, Holger ID - 2474 TI - MARVELO: Wireless Virtual Network Embedding for Overlay Graphs with Loops ER - TY - CONF AU - Shiferaw Heyi, Binyam AU - Karl, Holger ID - 2476 TI - Modelling Time-Limited Capacity of a Wireless Channel as aMarkov Reward Process ER - TY - CONF AB - Understanding the behavior of the components of service function chains (SFCs) in different load situations is important for efficient and automatic management and orches- tration of services. For this purpose and for practical research in network function virtualization in general, there is a great need for benchmarks and experimental data. In this paper, we describe our experiments for characterizing the relationship between resource demands of virtual network functions (VNFs) and the expected performance of the SFC, considering the individual performance of the VNFs as well as the interdependencies among VNFs within the SFC. We have designed our experiments focusing on video streaming, an important application in this context. We present examples of models for predicting the interdependence between resource demands and performance characteristics of SFCs using support vector regression and polynomial regression models. We also show practical evidence from our experiments that VNFs need to be benchmarked in their final chain setup, rather than individually, to capture important interdependencies that affect their performance. The data gathered from our experiments is publicly available. AU - Dräxler, Sevil AU - Peuster, Manuel AU - Illian, Marvin AU - Karl, Holger ID - 2480 T2 - 4th IEEE International Conference on Network Softwarization (NetSoft 2018) TI - Generating Resource and Performance Models for Service Function Chains: The Video Streaming Case ER - TY - CONF AB - Network function virtualization requires scaling and placement, deciding the number and the location of function instances. Current approaches are limited in flexibility and practical applicability. Specifically, we study dynamic, single-step, joint scaling and placement of network services with bidirectional flows traversing Physical or Virtual Network Functions (VNFs) and returning to their sources. We develop models to support stateful components and legacy network functions with fixed locations in these network services as well as the possibility of reusing VNFs across network services. We formalize the problem of jointly scaling and placing such network services as a mixed- integer linear program (MILP). We show that this problem is NP-complete and also present a heuristic algorithm to find good solutions in short time. In an extensive evaluation with realistic scenarios, we investigate the capabilities of the two approaches. AU - Dräxler, Sevil AU - Schneider, Stefan Balthasar AU - Karl, Holger ID - 2481 T2 - 4th IEEE International Conference on Network Softwarization (NetSoft 2018) TI - Scaling and Placing Bidirectional Services with Stateful Virtual and Physical Network Functions ER - TY - GEN AB - Understanding the behavior of distributed cloud service components in different load situations is important for efficient and automatic management and orchestration of these services. For this purpose and for practical research in distributed cloud computing in general, there is need for benchmarks and experimental data. In this paper, we describe our experiments for characterizing the relationship between resource demands of application components and the expected performance of applica- tions. We present initial results for predicting the interdependence between resource demands and performance characteristics using support vector regression and polynomial regression models. The data gathered from our experiments is publicly available. AU - Dräxler, Sevil AU - Peuster, Manuel AU - Illian, Marvin AU - Karl, Holger ID - 2483 TI - Towards Predicting Resource Demands and Performance of Distributed Cloud Services ER - TY - CONF AU - Auroux, Sébastien AU - Karl, Holger ID - 2472 TI - Distributed Placement of Virtualized Control Applications in Mobile Backhaul Networks ER - TY - CONF AU - Demirel, Burak AU - Ramaswamy, Arunselvan AU - Quevedo, Daniel AU - Karl, Holger ID - 3217 TI - DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling ER - TY - CONF AB - Dynamically steering flows through virtualized net- work function instances is a key enabler for elastic, on-demand deployments of virtualized network functions. This becomes par- ticular challenging when stateful functions are involved, necessi- tating state management. The problem with existing solutions is that they typically embrace state migration and flow rerouting jointly, imposing a huge set of requirements on the on-boarded VNFs, e.g., solution-specific state management interfaces. In this paper, we introduce the seamless handover proto- col (SHarP). It provides an easy-to-use, loss-less, and order- preserving flow rerouting mechanism that is not fixed to a single state management approach. This allows VNF vendors to implement or use the state management solution of their choice. SHarP supports these solutions with additional information when flows are migrated. Further, we show how SHarP significantly reduces the buffer usage at a central (SDN) controller, which is a typical bottleneck in existing solutions. Our experiments show that SHarP uses a constant amount of controller buffer, irrespective of the time taken to migrate the VNF state. AU - Peuster, Manuel AU - Küttner, Hannes AU - Karl, Holger ID - 3345 T2 - 4th IEEE International Conference on Network Softwarization (NetSoft 2018) TI - Let the state follow its flows: An SDN-based flow handover protocol to support state migration ER -