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Joint Optimization for Task Offloading in Edge Computing: An Evolutionary Game Approach

School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 740;
Received: 29 December 2018 / Revised: 4 February 2019 / Accepted: 5 February 2019 / Published: 12 February 2019
(This article belongs to the Special Issue Recent Advances in Fog/Edge Computing in Internet of Things)
PDF [4320 KB, uploaded 12 February 2019]


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, limited computation resources in edge nodes may not be sufficient to serve excessive offloading tasks exceeding the computation capacities of edge nodes. Therefore, multiple edge clouds with a complementary central cloud coordinated to serve users is the efficient architecture to satisfy users’ Quality-of-Service (QoS) requirements while trying to minimize some network service providers’ cost. We study a dynamic, decentralized resource-allocation strategy based on evolutionary game theory to deal with task offloading to multiple heterogeneous edge nodes and central clouds among multi-users. In our strategy, the resource competition among multi-users is modeled by the process of replicator dynamics. During the process, our strategy can achieve one evolutionary equilibrium, meeting users’ QoS requirements under resource constraints of edge nodes. The stability and fairness of this strategy is also proved by mathematical analysis. Illustrative studies show the effectiveness of our proposed strategy, outperforming other alternative methods. View Full-Text
Keywords: task offloading; mobile edge computing; evolutionary game theory task offloading; mobile edge computing; evolutionary game theory

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Dong, C.; Wen, W. Joint Optimization for Task Offloading in Edge Computing: An Evolutionary Game Approach. Sensors 2019, 19, 740.

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