TY - CONF AB - Modern services comprise interconnected components, e.g., microservices in a service mesh, that can scale and run on multiple nodes across the network on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities and changing demands into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, it significantly improves flow throughput and overall network utility on real-world network topologies and traffic traces. It also learns to optimize different objectives, generalizes to scenarios with unseen, stochastic traffic patterns, and scales to large real-world networks. AU - Schneider, Stefan Balthasar AU - Manzoor, Adnan AU - Qarawlus, Haydar AU - Schellenberg, Rafael AU - Karl, Holger AU - Khalili, Ramin AU - Hecker, Artur ID - 19609 KW - self-driving networks KW - self-learning KW - network coordination KW - service coordination KW - reinforcement learning KW - deep learning KW - nfv T2 - IEEE International Conference on Network and Service Management (CNSM) TI - Self-Driving Network and Service Coordination Using Deep Reinforcement Learning ER - TY - CONF AB - Data-parallel applications run on cluster of servers in a datacenter and their communication triggers correlated resource demand on multiple links that can be abstracted as coflow. They often desire predictable network performance, which can be passed to network via coflow abstraction for application-aware network scheduling. In this paper, we propose a heuristic and an optimization algorithm for predictable network performance such that they guarantee coflows completion within their deadlines. The algorithms also ensure high network utilization, i.e., it's work-conserving, and avoids starvation of coflows. We evaluate both algorithms via trace-driven simulation and show that they admit 1.1x more coflows than the Varys scheme while meeting their deadlines. AU - Hasnain, Asif AU - Karl, Holger ID - 17082 KW - Coflow KW - Scheduling KW - Deadlines KW - Data centers T2 - 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID) TI - Coflow Scheduling with Performance Guarantees for Data Center Applications ER - TY - CONF AB - Network function virtualization (NFV) proposes to replace physical middleboxes with more flexible virtual network functions (VNFs). To dynamically adjust to everchanging traffic demands, VNFs have to be instantiated and their allocated resources have to be adjusted on demand. Deciding the amount of allocated resources is non-trivial. Existing optimization approaches often assume fixed resource requirements for each VNF instance. However, this can easily lead to either waste of resources or bad service quality if too many or too few resources are allocated. To solve this problem, we train machine learning models on real VNF data, containing measurements of performance and resource requirements. For each VNF, the trained models can then accurately predict the required resources to handle a certain traffic load. We integrate these machine learning models into an algorithm for joint VNF scaling and placement and evaluate their impact on resulting VNF placements. Our evaluation based on real-world data shows that using suitable machine learning models effectively avoids over- and underallocation of resources, leading to up to 12 times lower resource consumption and better service quality with up to 4.5 times lower total delay than using standard fixed resource allocation. AU - Schneider, Stefan Balthasar AU - Satheeschandran, Narayanan Puthenpurayil AU - Peuster, Manuel AU - Karl, Holger ID - 16219 T2 - IEEE Conference on Network Softwarization (NetSoft) TI - Machine Learning for Dynamic Resource Allocation in Network Function Virtualization ER - TY - CONF AU - Zafeiropoulos, A. AU - Fotopoulou, E. AU - Peuster, Manuel AU - Schneider, Stefan Balthasar AU - Gouvas, P. AU - Behnke, D. AU - Müller, M. AU - Bök, P. AU - Trakadas, P. AU - Karkazis, P. AU - Karl, Holger ID - 16222 T2 - IEEE Conference on Network Softwarization (NetSoft) TI - Benchmarking and Profiling 5G Verticals' Applications: An Industrial IoT Use Case ER - TY - JOUR AB - Currently, the coexistence of multiple users and devices challenges the network's ability to reliably connect them. This article proposes a novel communication architecture that satisfies the requirements of fifth-generation (5G) mobile network applications. In particular, this architecture extends and combines ultra-dense networking (UDN), multi-access edge computing (MEC), and virtual infrastructure manager (VIM) concepts to provide a flexible network of moving radio access (RA) nodes, flying or moving to areas where users and devices struggle for connectivity and data rate. Furthermore, advances in radio communications and non-orthogonal multiple access (NOMA), virtualization technologies and energy-awareness mechanisms are integrated towards a mobile UDN that not only allows RA nodes to follow the user but also enables the virtualized network functions (VNFs) to adapt to user mobility by migrating from one node to another. Performance evaluation shows that the underlying network improves connectivity of users and devices through the flexible deployment of moving RA nodes and the use of NOMA. AU - Nomikos, Nikolaos AU - Michailidis, Emmanouel T. AU - Trakadas, Panagiotis AU - Vouyioukas, Demosthenes AU - Karl, Holger AU - Martrat, Josep AU - Zahariadis, Theodore AU - Papadopoulos, Konstantinos AU - Voliotis, Stamatis ID - 16278 JF - Vehicular Communications SN - 2214-2096 TI - A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices ER - TY - JOUR AB - Assigning bands of the wireless spectrum as resources to users is a common problem in wireless networks. Typically, frequency bands were assumed to be available in a stable manner. Nevertheless, in recent scenarios where wireless networks may be deployed in unknown environments, spectrum competition is considered, making it uncertain whether a frequency band is available at all or at what quality. To fully exploit such resources with uncertain availability, the multi-armed bandit (MAB) method, a representative online learning technique, has been applied to design spectrum scheduling algorithms. This article surveys such proposals. We describe the following three aspects: how to model spectrum scheduling problems within the MAB framework, what the main thread is following which prevalent algorithms are designed, and how to evaluate algorithm performance and complexity. We also give some promising directions for future research in related fields. AU - Li, Feng AU - Yu, Dongxiao AU - Yang, Huan AU - Yu, Jiguo AU - Karl, Holger AU - Cheng, Xiuzhen ID - 16280 JF - IEEE Wireless Communications SN - 1536-1284 TI - Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey ER - TY - CONF AB - Softwarization facilitates the introduction of smart manufacturing applications in the industry. Manifold devices such as machine computers, Industrial IoT devices, tablets, smartphones and smart glasses are integrated into factory networks to enable shop floor digitalization and big data analysis. To handle the increasing number of devices and the resulting traffic, a flexible and scalable factory network is necessary which can be realized using softwarization technologies like Network Function Virtualization (NFV). However, the security risks increase with the increasing number of new devices, so that cyber security must also be considered in NFV-based networks. Therefore, extending our previous work, we showcase threat detection using a cloud-native NFV-driven intrusion detection system (IDS) that is integrated in our industrial-specific network services. As a result of the threat detection, the affected network service is put into quarantine via automatic network reconfiguration. We use the 5GTANGO service platform to deploy our developed network services on Kubernetes and to initiate the network reconfiguration. AU - Müller, Marcel AU - Behnke, Daniel AU - Bök, Patrick-Benjamin AU - Schneider, Stefan Balthasar AU - Peuster, Manuel AU - Karl, Holger ID - 16400 T2 - IEEE Conference on Network Softwarization (NetSoft) Demo Track TI - Cloud-Native Threat Detection and Containment for Smart Manufacturing ER - TY - JOUR AU - Karl, Holger AU - Kundisch, Dennis AU - Meyer auf der Heide, Friedhelm AU - Wehrheim, Heike ID - 13770 IS - 6 JF - Business & Information Systems Engineering TI - A Case for a New IT Ecosystem: On-The-Fly Computing VL - 62 ER - TY - CONF AB - For optimal placement and orchestration of network services, it is crucial that their structure and semantics are specified clearly and comprehensively and are available to an orchestrator. Existing specification approaches are either ambiguous or miss important aspects regarding the behavior of virtual network functions (VNFs) forming a service. We propose to formally and unambiguously specify the behavior of these functions and services using Queuing Petri Nets (QPNs). QPNs are an established method that allows to express queuing, synchronization, stochastically distributed processing delays, and changing traffic volume and characteristics at each VNF. With QPNs, multiple VNFs can be connected to complete network services in any structure, even specifying bidirectional network services containing loops. We discuss how management and orchestration systems can benefit from our clear and comprehensive specification approach, leading to better placement of VNFs and improved Quality of Service. Another benefit of formally specifying network services with QPNs are diverse analysis options, which allow valuable insights such as the distribution of end-to-end delay. We propose a tool-based workflow that supports the specification of network services and the automatic generation of corresponding simulation code to enable an in-depth analysis of their behavior and performance. AU - Schneider, Stefan Balthasar AU - Sharma, Arnab AU - Karl, Holger AU - Wehrheim, Heike ID - 3287 T2 - 2019 IFIP/IEEE International Symposium on Integrated Network Management (IM) TI - Specifying and Analyzing Virtual Network Services Using Queuing Petri Nets ER - TY - CONF AB - As 5G and network function virtualization (NFV) are maturing, it becomes crucial to demonstrate their feasibility and benefits by means of vertical scenarios. While 5GPPP has identified smart manufacturing as one of the most important vertical industries, there is still a lack of specific, practical use cases. Using the experience from a large-scale manufacturing company, Weidm{\"u}ller Group, we present a detailed use case that reflects the needs of real-world manufacturers. We also propose an architecture with specific network services and virtual network functions (VNFs) that realize the use case in practice. As a proof of concept, we implement the required services and deploy them on an emulation-based prototyping platform. Our experimental results indicate that a fully virtualized smart manufacturing use case is not only feasible but also reduces machine interconnection and configuration time and thus improves productivity by orders of magnitude. AU - Schneider, Stefan Balthasar AU - Peuster, Manuel AU - Behnke, Daniel AU - Marcel, Müller AU - Bök, Patrick-Benjamin AU - Karl, Holger ID - 9270 KW - 5g KW - vertical KW - smart manufacturing KW - nfv T2 - European Conference on Networks and Communications (EuCNC) TI - Putting 5G into Production: Realizing a Smart Manufacturing Vertical Scenario ER - TY - JOUR AB - The ongoing softwarization of networks creates a big need for automated testing solutions to ensure service quality. This becomes even more important if agile environments with short time to market and high demands, in terms of service performance and availability, are considered. In this paper, we introduce a novel testing solution for virtualized, microservice-based network functions and services, which we base on TTCN-3, a well known testing language defined by the European standards institute (ETSI). We use TTCN-3 not only for functional testing but also answer the question whether TTCN-3 can be used for performance profiling tasks as well. Finally, we demonstrate the proposed concepts and solutions in a case study using our open-source prototype to test and profile a chained network service. AU - Peuster, Manuel AU - Dröge, Christian AU - Boos, Clemens AU - Karl, Holger ID - 8113 JF - ICT Express SN - 2405-9595 TI - Joint testing and profiling of microservice-based network services using TTCN-3 ER - TY - CONF AU - Dräxler, Sevil AU - Karl, Holger ID - 8240 T2 - 5th IEEE International Conference on Network Softwarization (NetSoft) 2019 TI - SPRING: Scaling, Placement, and Routing of Heterogeneous Services with Flexible Structures ER - TY - CONF AB - 5G together with software defined networking (SDN) and network function virtualisation (NFV) will enable a wide variety of vertical use cases. One of them is the smart man- ufacturing case which utilises 5G networks to interconnect production machines, machine parks, and factory sites to enable new possibilities in terms of flexibility, automation, and novel applications (industry 4.0). However, the availability of realistic and practical proof-of-concepts for those smart manufacturing scenarios is still limited. This demo fills this gap by not only showing a real-world smart manufacturing application entirely implemented using NFV concepts, but also a lightweight prototyping framework that simplifies the realisation of vertical NFV proof-of-concepts. Dur- ing the demo, we show how an NFV-based smart manufacturing scenario can be specified, on-boarded, and instantiated before we demonstrate how the presented NFV services simplify machine data collection, aggregation, and analysis. AU - Peuster, Manuel AU - Schneider, Stefan Balthasar AU - Behnke, Daniel AU - Müller, Marcel AU - Bök, Patrick-Benjamin AU - Karl, Holger ID - 8792 T2 - 5th IEEE International Conference on Network Softwarization (NetSoft 2019) TI - Prototyping and Demonstrating 5G Verticals: The Smart Manufacturing Case ER - TY - JOUR AB - Softwarized networks are the key enabler for elastic, on-demand service deployments of virtualized network functions. They allow to dynamically steer traffic through the network when new network functions are instantiated, or old ones are terminated. These scenarios become in particular challenging when stateful functions are involved, necessitating state management solutions to migrate state between the functions. The problem with existing solutions is that they typically embrace state migration and flow rerouting jointly, imposing a huge set of requirements on the on-boarded virtualized network functions (VNFs), eg, solution-specific state management interfaces. To change this, we introduce the seamless handover protocol (SHarP). An easy-to-use, loss-less, and order-preserving flow rerouting mechanism that is not fixed to a single state management approach. Using SHarP, VNF vendors are empowered to implement or use the state management solution of their choice. SHarP supports these solutions with additional information when flows are migrated. In this paper, we present SHarP's design, its open source prototype implementation, and show how SHarP significantly reduces the buffer usage at a central (SDN) controller, which is a typical bottleneck in state-of-the-art solutions. Our experiments show that SHarP uses a constant amount of controller buffer, irrespective of the time taken to migrate the VNF state. AU - Peuster, Manuel AU - Küttner, Hannes AU - Karl, Holger ID - 8795 JF - International Journal of Network Management SN - 1055-7148 TI - A flow handover protocol to support state migration in softwarized networks ER - TY - JOUR AU - Soenen, Thomas AU - Tavernier, Wouter AU - Peuster, Manuel AU - Vicens, Felipe AU - Xilouris, George AU - Kolometsos, Stavros AU - Kourtis, Michail-Alexandros AU - Colle, Didier ID - 9823 JF - IEEE Communications Magazine SN - 0163-6804 TI - Empowering Network Service Developers: Enhanced NFV DevOps and Programmable MANO ER - TY - JOUR AU - Peuster, Manuel AU - Schneider, Stefan Balthasar AU - Zhao, Mengxuan AU - Xilouris, George AU - Trakadas, Panagiotis AU - Vicens, Felipe AU - Tavernier, Wouter AU - Soenen, Thomas AU - Vilalta, Ricard AU - Andreou, George AU - Kyriazis, Dimosthenis AU - Karl, Holger ID - 9824 JF - IEEE Communications Magazine SN - 0163-6804 TI - Introducing Automated Verification and Validation for Virtualized Network Functions and Services ER - TY - CONF AU - Afifi, Haitham AU - Karl, Holger ID - 6860 T2 - 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019) TI - Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network Embedding ER - TY - CONF AB - By distributing the computational load over the nodes of a Wireless Acoustic Sensor Network (WASN), the real-time capability of the TRINICON (TRIple-N-Independent component analysis for CONvolutive mixtures) framework for Blind Source Separation (BSS) can be ensured, even if the individual network nodes are not powerful enough to run TRINICON in real-time by themselves. To optimally utilize the limited computing power and data rate in WASNs, the MARVELO (Multicast-Aware Routing for Virtual network Embedding with Loops in Overlays) framework is expanded for use with TRINICON, while a feature-based selection scheme is proposed to exploit the most beneficial parts of the input signal for adapting the demixing system. The simulation results of realistic scenarios show only a minor degradation of the separation performance even in heavily resource-limited situations. AU - Guenther, Michael AU - Afifi, Haitham AU - Brendel, Andreas AU - Karl, Holger AU - Kellermann, Walter ID - 12880 T2 - 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (WASPAA 2019) TI - Sparse Adaptation of Distributed Blind Source Separation in Acoustic Sensor Networks ER - TY - CONF AB - Internet of Things (IoT) applications witness an exceptional evolution of traffic demands, while existing protocols, as seen in wireless sensor networks (WSNs), struggle to cope with these demands. Traditional protocols rely on finding a routing path between sensors generating data and sinks acting as gateway or databases. Meanwhile, the network will suffer from high collisions in case of high data rates. In this context, in-network processing solutions are used to leverage the wireless nodes' computations, by distributing processing tasks on the nodes along the routing path. Although in-network processing solutions are very popular in wired networks (e.g., data centers and wide area networks), there are many challenges to adopt these solutions in wireless networks, due to the interference problem. In this paper, we solve the problem of routing and task distribution jointly using a greedy Virtual Network Embedding (VNE) algorithm, and consider power control as well. Through simulations, we compare the proposed algorithm to optimal solutions and show that it achieves good results in terms of delay. Moreover, we discuss its sub-optimality by driving tight lower bounds and loose upper bounds. We also compare our solution with another wireless VNE solution to show the trade-off between delay and symbol error rate. AU - Afifi, Haitham AU - Karl, Holger ID - 12881 T2 - 2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) (WMNC'19) TI - An Approximate Power Control Algorithm for a Multi-Cast Wireless Virtual Network Embedding ER - TY - CONF AB - One of the major challenges in implementing wireless virtualization is the resource discovery. This is particularly important for the embedding-algorithms that are used to distribute the tasks to nodes. MARVELO is a prototype framework for executing different distributed algorithms on the top of a wireless (802.11) ad-hoc network. The aim of MARVELO is to select the nodes for running the algorithms and to define the routing between the nodes. Hence, it also supports monitoring functionalities to collect information about the available resources and to assist in profiling the algorithms. The objective of this demo is to show how MAVRLEO distributes tasks in an ad-hoc network, based on a feedback from our monitoring tool. Additionally, we explain the work-flow, composition and execution of the framework. AU - Afifi, Haitham AU - Karl, Holger AU - Eikenberg, Sebastian AU - Mueller, Arnold AU - Gansel, Lars AU - Makejkin, Alexander AU - Hannemann, Kai AU - Schellenberg, Rafael ID - 12882 KW - WSN KW - virtualization KW - VNE T2 - 2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo) TI - A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO ER -