CNSM 2019 special issue: Embracing the new wave of artificial intelligence

In recent years, we observe a growing trend in the area of artificial intelligence (AI) for network and service management. Approaches such as statistical analysis, data mining and machine learning have become promising to harness the immense stream of operational data and to improve the management of networks and system. Thus, CNSM 2019 was focused on what matters most to managing networks and services. Many researchers from the field of network and service management have explored how AI can help us not only sense but also shape the future of management as AI technologies have disrupted numerous industries. To manage the configuration, performance, resilience, availability and security of the networks and services, traditional measures such as log/event analysis, intrusion detection/prevention and monitoring and deployment have taken a new dimension. New techniques and mechanisms from AI, machine learning and data mining are explored for designing, developing and operating networks and systems. In summary, there are a lot of research challenges in this emerging field of embracing the new wave of AI. The purpose of this special issue is to explore and highlight the promising capabilities of AI in managing the operational data on the networks and services. Among the top research papers presented at the 2019 International Conference on Network and Service Management, four of them are selected and the extended versions of those papers went under extensive reviews and discussions. These four papers were finally selected for publication in this special issue. The authors of these papers were given the time to update their papers based on the review comments and suggestions provided. The selected papers address topics that play a central role in embracing the new wave of AI for network and service management and presenting novel theoretical and/or experimentation results. The first paper, “On Accounting for Screen Resolution in Adaptive Video Streaming: QoE Driven Bandwidth Sharing Framework,” by Belmoukadam et al. analyses the problem of bandwidth allocation for multiple video streaming sessions over a shared link and proposes an optimization framework to maximize the overall quality of experience (QoE) taking into consideration terminal display capabilities. The approach relies on a Lagrangian relaxation heuristic and Karush–Kuhn–Tucker (KKT) conditions to efficiently solve the optimization problem, showing an increase of up to 20% of the overall QoE compared with an allocation with a TCP look-alike strategy implementing max–min fairness. The second paper, “Towards Distributed Emergency Flow Prioritization in SDN Networks,” by Moeyersons et al. proposes a microservices-based framework, which is able to run both as a centralized and distributed system. Moreover, the proposed framework guarantees the required bandwidth for the emergency flows and maximizes the best-effort flows over the remaining bandwidth based on their priority. The offline linear model part of the framework is evaluated through simulation, the distributed model is evaluated through emulation while the online approach is validated through physical experiments with SDN switches. Finally, as a proof of concept, a prototype with Zodiac switches validates the feasibility of the centralized framework. The third paper, “OpenBNG: Central Office Network Functions on Programmable Data Plane Hardware,” by Kundel et al. explores to offer residential network access with programmable packet processing architectures. The authors present the design and open-source implementation of a Broadband Network Gateway data plane using P4 programming language. Moreover, they introduce a concept of hybrid openBNG design, realizing the required hierarchical quality of service functionality in a subsequent FPGA. Evaluation results show the desired performance characteristics towards highest performance NFV network access. Finally, the fourth paper, “De-anonymizing Ethereum Blockchain Smart Contracts through Code Attribution,” by Linoy et al. is on analysing the pseudo-anonymity of blockchain technologies. The paper proposes to leverage a stylometry approach to investigate the extent to which a deployed contract's source code can contribute to the affiliation of the deployers' account addresses. Authors have prepared a dataset of real-world contract data; design and implement feature selection, extraction techniques and data refinement heuristics; and examine their effect on attribution accuracy. They further evaluate the proposed system to test the classification of real-world scammer data. We expect future work in the area of embracing the new wave of AI for network and service management to further explore the topics addressed by the selected papers. The guest editors of this special issue wish to acknowledge the excellent work that has been performed by the authors, who submitted papers, and by the reviewers, who have spent a considerable amount of their time providing high-quality reviews. We would also like to extend our thanks to the editorial board of the International Journal of Network Management, in particular to editor-in-chief James Won-Ki Hong and associate editor-in-chief Jérôme François and Lisandro Zambenedetti Granville, for the great opportunity and their support in editing this special issue. Last but not the least, we thank the editorial team of Wiley, for the support offered to the authors. This is to confirm that there are no data available for this editorial paper. Dr Nur Zincir-Heywood is a Full Professor of Computer Science at Dalhousie University, Canada. She is a co-editor of the book Recent Advances in Computational Intelligence in Defense and Security (Springer). Her research interests include machine learning and data mining techniques for network management and cybersecurity, topics on which she has published over 200 fully reviewed papers. She is a recipient of several best paper awards as well as the supervisor for the recipient of the IFIP/IEEE IM 2013 Best Ph.D. Dissertation Award in Network Management. In 2017, she received the Women Leaders in the Digital Economy Award. She has served as the General Co-Chair of the IFIP/IEEE CNSM conference in 2020 and as the TPC Co-Chair in 2019. Dr Rémi Badonnel is an Associate Professor of Computer Science at TELECOM Nancy, the Engineering School of Computer Science of the University of Lorraine, France, where he is heading the Internet Systems and Security specialization. He is a Permanent Research Staff Member with the RESIST Team at LORIA–INRIA Nancy Grand Est, France. Previously, he worked on change management methods and algorithms for data centres at the IBM T.J. Watson Research Center, USA, and investigated autonomous smart systems at the Oslo Metropolitan University, Norway. His research interests are mainly focused on network and service management, smart and autonomic environments, security and defence techniques, orchestration and chaining of services, cloud infrastructures, and Internet of things. He has served as TPC co-chair for the IFIP/IEEE IM symposium in 2015 and for the IFIP/IEEE CNSM conference in 2019. He has been elected as chair of the IFIP TC6 WG 6.6 dedicated to the management of networks and distributed systems in 2019.