IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency - Ghafouri, Saeid; Razavi, Kamran; Salmani, Mehran; Sanaee, Alireza; Lorido Botran, Tania; Wang, Lin; Doyle, Joseph; Jamshidi, Pooyan (Journal of Systems Research (JSys))

Practical Network Conditions for the Convergence of Distributed Optimization - Redder, Adrian; Ramaswamy, Arunselvan; Karl, Holger (IFAC-PapersOnLine)

Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning - Schneider, Stefan Balthasar; Khalili, Ramin; Manzoor, Adnan; Qarawlus, Haydar; Schellenberg, Rafael; Karl, Holger; Hecker, Artur (Transactions on Network and Service Management)
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.
Keywords: network management; service management; coordination; reinforcement learning; self-learning; self-adaptation; multi-objective

A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices - Nomikos, Nikolaos; Michailidis, Emmanouel T.; Trakadas, Panagiotis; Vouyioukas, Demosthenes; Karl, Holger; Martrat, Josep; Zahariadis, Theodore; Papadopoulos, Konstantinos; Voliotis, Stamatis (Vehicular Communications)
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.

Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey - Li, Feng; Yu, Dongxiao; Yang, Huan; Yu, Jiguo; Karl, Holger; Cheng, Xiuzhen (IEEE Wireless Communications)
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.

A Case for a New IT Ecosystem: On-The-Fly Computing - Karl, Holger; Kundisch, Dennis; Meyer auf der Heide, Friedhelm; Wehrheim, Heike (Business & Information Systems Engineering)

Joint testing and profiling of microservice-based network services using TTCN-3 - Peuster, Manuel; Dröge, Christian; Boos, Clemens; Karl, Holger (ICT Express)
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.

A flow handover protocol to support state migration in softwarized networks - Peuster, Manuel; Küttner, Hannes; Karl, Holger (International Journal of Network Management)
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.

Empowering Network Service Developers: Enhanced NFV DevOps and Programmable MANO - Soenen, Thomas; Tavernier, Wouter; Peuster, Manuel; Vicens, Felipe; Xilouris, George; Kolometsos, Stavros; Kourtis, Michail-Alexandros; Colle, Didier (IEEE Communications Magazine)

Introducing Automated Verification and Validation for Virtualized Network Functions and Services - Peuster, Manuel; Schneider, Stefan Balthasar; Zhao, Mengxuan; Xilouris, George; Trakadas, Panagiotis; Vicens, Felipe; Tavernier, Wouter; Soenen, Thomas; Vilalta, Ricard; Andreou, George; Kyriazis, Dimosthenis; Karl, Holger (IEEE Communications Magazine)