Joint Optimization for Task Offloading in Edge Computing: An Evolutionary Game Approach
AbstractThe 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
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Dong, C.; Wen, W. Joint Optimization for Task Offloading in Edge Computing: An Evolutionary Game Approach. Sensors 2019, 19, 740.
Dong C, Wen W. Joint Optimization for Task Offloading in Edge Computing: An Evolutionary Game Approach. Sensors. 2019; 19(3):740.Chicago/Turabian Style
Dong, Chongwu; Wen, Wushao. 2019. "Joint Optimization for Task Offloading in Edge Computing: An Evolutionary Game Approach." Sensors 19, no. 3: 740.
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