The global evaluation strategy for many‐objective partial collaborative computation offloading problem. (22nd November 2022)

• Record Type: Journal Article
• Authors: Xue, Zhaoyu; Guo, Wanwan; Cui, Zhihua; Zhang, Wensheng
• Is Part Of: Concurrency and computation. Volume 35:Number 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-22
• Subjects: computation offloading -- edge computing -- many‐objective evolutionary algorithm; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7474
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: With the number of services expanding in the Internet of Things (IoT), the limited resources of user terminals are insufficient to satisfy the computation needs of all running services. Therefore, we design a collaborative computation‐offloading model (CCOM) composed of multiple servers and task offloading modes to solve the problem by offloading tasks from resource‐constrained terminals to other computing entities, which achieves the following four objectives: model execution time minimization, task execution time minimization, energy consumption minimization, and most efficient device workload. And the global evaluation strategy based on angle and distance is proposed to address the individual selection problems caused by the overly localized Pareto dominance relationship and objective conflicts in the many‐objective evolutionary algorithms. In simulations, the strategy achieves the best Inverted Generation Distance ( IGD ) performance on 22 benchmark test problems based on Wilcoxon's rank sum statistical test and improves performance in the four objectives above by 18%, 31%, 20%, and 42%, respectively. Finally, we think that the strategy can provide adequate offloading performance for decision‐makers.