Advanced Network Slicing and Computational Offloading for Latency Limited Communications

Thesis event information

Date and time of the thesis defence

Place of the thesis defence

IT116, Linnanmaa

Topic of the dissertation

Advanced Network Slicing and Computational Offloading for Latency Limited Communications

Doctoral candidate

Master of Science (Technology) Ivana Kovacevic

Faculty and unit

University of Oulu Graduate School, Faculty of Information Technology and Electrical Engineering, Centre for Wireless Communications - Networks and Systems (CWC-NS)

Subject of study

Telecommunication engineering

Opponent

Professor Jyri Hämäläinen, Aalto University

Custos

Assistant professor Erkki Harjula, University of Oulu

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Advanced Network Slicing and Computational Offloading for Latency Limited Communications

A surge in diverse novel network services is being driven by the 4th Industrial revolution and by a number of new Internet of Things (IoT) use cases. With the emergence of 5G mobile broadband, Network Slicing (NS) has been recognized as a solution for service diversification. It enables network operators to create service-aware logical networks customized for different vertical industries, which have diverse requirements in terms of functionality, performance and resource separation. Provisioning of end-to-end delay-critical communication across multiple network domains is one of the key requirements for enabling future services. In addition, due to the decreasing size of end devices, a number of use cases depend on reliable and latency constrained computational offloading. The extension of the cloud computing concept into edge computing, located in the access network, is another key technological trend that enables reductions in latency, costs, and security risks.

This thesis proposes novel resource allocation mechanisms for network traffic with strict latency limitations. First, a multi-domain network slicing framework is designed, based on a novel multipath multihop delay model. This framework encompasses a novel hierarchical orchestration mechanism and a mechanism for dynamic slice resizing. Next, the problem of latency limited on-demand computational tasks offloading to Multi-access Edge Computing (MEC) and cloud servers is considered. The offloading decision and resource allocation problem is formulated as a joint optimization of communication and computation resources. Then, low complexity heuristic algorithms are proposed with the aim to minimize the total resource consumption of the system. Optimal solution-based, reinforcement learning-based resource allocation algorithms are proposed to efficiently handle latency limited tasks and tasks with extremely low latency requirements. Finally, an algorithm placement strategy for delay-critical continuous IoT data stream processing is proposed as well as a nanoservice orchestration framework for local edge computing. Presented numerical analysis results demonstrate the feasibility and high efficiency of the proposed solutions.
Last updated: 20.1.2023