Abstract:

With the emergent development of edge computing, service deployment and task offloading are two significant challenges to be addressed. However, currently, the single problem of task offloading in edge environments is solved, while service deployment is rarely considered simultaneously.Because service deployment and task offloading are highly coupled, considering only one has limitations and can cause some wasted resources and significant latency, thus affecting user experience.Meanwhile, traditional evolutionary algorithms can not manage multiple single-objective or multi-objective optimization tasks simultaneously.Therefore, to solve both challenges simultaneously, this study focuses on constructing a multi-task multi-objective model, where each optimization problem is treated as a task.An improved multifactor optimization-based evolutionary multitasking algorithm is proposed and a location update strategy is introduced to increase the search population diversity. The proposed design improves the selective mating method and increases the quality of offspring individuals. Experimental simulation results demonstrate that, compared with different multi-objective algorithms, the proposed algorithm performs well in SP, Span, PD and other indicators, has better convergence performance, and significantly accelerates solution speed, which improves the overall system performance by approximately 11.4%.

Key words: Mobile Edge Computing(MEC), service deployment, task offloading, evolutionary multitasking algorithm, multi-objective optimization

摘要：

服务部署和任务卸载是边缘计算面临的两大挑战，但目前在边缘环境下都是对任务卸载这一单一问题的求解，较少考虑服务部署问题。由于服务部署与任务卸载是高度耦合的，只考虑其中一个问题具有局限性，会造成资源的浪费及较大的时延，从而影响用户的体验感。此外，传统的进化算法不能同时处理多个单目标或多目标优化任务。为解决上述问题，构建一个多任务多目标模型，将每个优化问题视作一个任务，并针对该模型提出一种改进的基于多因子优化的进化多任务算法，通过引入位置更新策略来增加搜索种群的多样性，并在此基础上设计改进选型交配方法，提高后代个体的质量。仿真实验结果表明，与多目标算法对比，该算法在SP、Span、PD等多个指标上均有较好的表现，明显提高了算法收敛性能，大幅加快了求解速度，整体系统性能提高了11.4%。

关键词: 移动边缘计算, 服务部署, 任务卸载, 进化多任务算法, 多目标优化