A Potential Game Theoretic Approach to Computation Offloading Strategy Optimization in End-Edge-Cloud Computing

• Published in: IEEE transactions on parallel and distributed systems Vol. 33; no. 6; pp. 1503 - 1519
• Main Authors: Ding, Yan, Li, Kenli, Liu, Chubo, Li, Keqin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; Computation offloading; Computer architecture; Costs; Delays; Edge computing; end-edge-cloud computing (EECC); Game theory; hierarchical EECC; horizontal EECC; Optimization; Performance enhancement; potential game; Quality of experience; Servers; Strategy; Task analysis; Visibility
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2021.3112604

Integrating user ends (UEs), edge servers (ESs), and the cloud into end-edge-cloud computing (EECC) can enhance the utilization of resources and improve quality of experience (QoE). However, the performance of EECC is significantly affected by its architecture. In this article, we classify EECC into two computing architectures types according to the visibility and accessibility of the cloud to UEs, i.e., hierarchical end-edge-cloud computing (Hi-EECC) and horizontal end-edge-cloud computing (Ho-EECC). In Hi-EECC, UEs can offload their tasks only to ESs. When the resources of ESs are exhausted, the ESs request the cloud to provide resources to UEs. In Ho-EECC, UEs can offload their tasks directly to ESs and the cloud. In this article, we construct a potential game for the EECC environment, in which each UE selfishly minimizes its payoff, study the computation offloading strategy optimization problems, and develop two potential game-based algorithms in Hi-EECC and Ho-EECC. Extensive experiments with real-world data are conducted to demonstrate the performance of the proposed algorithms. Moreover, the scalability and applicability of the two computing architectures are comprehensively analyzed. The conclusions of our work can provide useful suggestions for choosing specific computing architectures under different application environments to improve the performance of EECC and QoE.