Speaker:

Qimu Xiao (University of Electronic Science and Technology of China)

Time:

• 16:20-17:20 (Time in Beijing)
• March 24, 2023 (Friday)

Venue:

518, Research Building 4

Abstract:

A widely studied typical mobile edge computing (MEC) system network consists of a cloud server, some edge servers, and some user equipment, which promises a satisfactory user experience by offloading computing tasks to the servers. With the continuous innovation of distributed technology and the wide spread of the concept of collaborative development, network interconnection and resource sharing in multiple MEC system networks have become a trend. However, since the resources in multiple MEC systems are usually unbalanced, how to efficiently complete task scheduling and resource allocation for performance optimization becomes a challenge. In addition, the online network environment is also an urgent problem to be solved. In this paper, we study the joint cross-cell offloading and resource allocation problem in multi-cell MEC networks with the objective of optimizing the user’s quality of experience (QoE). We first propose an exact solution for the problem by formulating it as a mixed integer nonlinear programming (MINLP). Secondly, we devise an efficient distributed global optimal solution search DGOSS algorithm to solve the optimal task scheduling and resource allocation for the offline QoE optimization problem. We thirdly develop a decentralized online learning with dynamic threshold exploration DTE-DOL algorithm with a sub-linear bounded regret under dynamic computing task generation, dynamic server quota, and uncertain server-side information assumptions, by adopting the multi-user Multi-Armed Bandit (MAB) technique and distributed auction technique. We finally evaluated the performance of the proposed algorithms compared to state-of-the-art benchmarks. Results show that the proposed DGOSS and DTE-DOL algorithms outperform offline and online benchmarks by reducing the QoE around 5% and 18.75%, respectively..