A Novel Task Caching and Migration Strategy in Multi-Access Edge Computing Based on the Genetic Algorithm

Lujie Tang, Bing Tang, Linyao Kang and Li Zhang
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Lujie Tang: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
Bing Tang: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
Linyao Kang: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
Li Zhang: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China

Future Internet, 2019, vol. 11, issue 8, 1-14

Abstract: Multi-access edge computing (MEC) brings high-bandwidth and low-latency access to applications distributed at the edge of the network. Data transmission and exchange become faster, and the overhead of the task migration between mobile devices and edge cloud becomes smaller. In this paper, we adopt the fine-grained task migration model. At the same time, in order to further reduce the delay and energy consumption of task execution, the concept of the task cache is proposed, which involves caching the completed tasks and related data on the edge cloud. Then, we consider the limitations of the edge cloud cache capacity to study the task caching strategy and fine-grained task migration strategy on the edge cloud using the genetic algorithm (GA). Thus, we obtained the optimal mobile device task migration strategy, satisfying minimum energy consumption and the optimal cache on the edge cloud. The simulation results showed that the task caching strategy based on fine-grained migration can greatly reduce the energy consumption of mobile devices in the MEC environment.

Keywords: edge computing; task migration; task caching; genetic algorithm (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
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