Research Article
A Novel Probabilistic-Performance-Aware and Evolutionary Game-Theoretic Approach to Task Offloading in the Hybrid Cloud-Edge Environment
@INPROCEEDINGS{10.1007/978-3-030-67537-0_16, author={Ying Lei and Wanbo Zheng and Yong Ma and Yunni Xia and Qing Xia}, title={A Novel Probabilistic-Performance-Aware and Evolutionary Game-Theoretic Approach to Task Offloading in the Hybrid Cloud-Edge Environment}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2021}, month={1}, keywords={Task offloading Mobile edge computing Evolutionary game theory Probabilistic QoS}, doi={10.1007/978-3-030-67537-0_16} }- Ying Lei
Wanbo Zheng
Yong Ma
Yunni Xia
Qing Xia
Year: 2021
A Novel Probabilistic-Performance-Aware and Evolutionary Game-Theoretic Approach to Task Offloading in the Hybrid Cloud-Edge Environment
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-67537-0_16
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 centralized cloud server 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 optimization 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.
