Title: A method for mobile-aware multi-user offloading in hybrid cloud-edge environment

Authors: Yu Wang; Yawen Li; Yunni Xia; Jianqi Li

Addresses: Software Theory and Technology Chongqing Key Lab, Chongqing University, Chongqing, 400000, China ' Software Theory and Technology Chongqing Key Lab, Chongqing University, Chongqing, 400000, China ' Software Theory and Technology Chongqing Key Lab, Chongqing University, Chongqing, 400000, China ' Global Energy Interconnection Research Institute Co. Ltd., Beingjing, 102209, China

Abstract: The mobile edge computing (MEC) paradigm provides a promising solution to solve the resource-insufficiency problem in mobile terminals by offloading computation-intensive and delay-sensitive tasks to nearby edge nodes. However, pure edge resources can be limited and insufficient for computational-intensive applications raised by multiple users, which calls for a hybrid architecture with a centralised cloud service and multiple edge nodes and smart resource management strategies in such hybrid environment. The problem is however challenging due to the distributed nature and intrinsic dynamicness of the environment. Existing researches in this direction mainly see that edge servers are with constant performance and consider the offloading decision-making as a static optimisation problem. In this paper, instead, we consider that geographically distributed edge servers are with time-varying performance and introduce a dynamic offloading strategy based on a probabilistic evolutionary game-theoretic framework. To validate our proposed framework, we conduct experimental case studies based on a real-world dataset of cloud edge resource locations and show that our proposed approach outperforms traditional ones in terms of multiple metrics.

Keywords: task offloading; mobile edge computing; MEC; evolutionary game theory; probabilistic QoS.

DOI: 10.1504/IJIITC.2022.129115

International Journal of Intelligent Internet of Things Computing, 2022 Vol.1 No.4, pp.300 - 317

Received: 26 Jan 2022
Accepted: 23 Apr 2022
Published online: 20 Feb 2023 *

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