A Novel Coevolutionary Approach to Reliability Guaranteed Multi-Workflow Scheduling upon Edge Computing Infrastructures

Abstract

Recently, mobile edge computing (MEC) is widely believed to be a promising and powerful paradigm for bringing enterprise applications closer to data sources such as IoT devices or local edge servers. It is capable of energizing novel mobile applications, especially the ultra-latency-sensitive ones, by providing powerful local computing capabilities and lower end-to-end delays. Nevertheless, various challenges, especially the reliability-guaranteed scheduling of multitask business processes in terms of, e.g., workflows, upon distributed edge resources and servers, are yet to be carefully addressed. In this paper, we propose a novel edge-environment-based multi-workflow scheduling method, which incorporates a reliability estimation model for edge-workflows and a coevolutionary algorithm for yielding scheduling decisions. The proposed approach aims at maximizing the reliability, in terms of success rates, of services deployed upon edge infrastructures while minimizing service invocation cost for users. We conduct simulative experimental case studies based on multiple well-known scientific workflow templates and a well-known dataset of edge resource locations as well. Simulative results clearly suggest that our proposed approach outperforms traditional ones in terms of workflow success rate and monetary cost.

Computational Offloading of Service Workflow in Mobile Edge Computing

“…There have been some studies conducted on the computation offloading strategy in MEC networks [11][12][13][14][15][16][17][18][19]. In [11], a new graph-based MEC workflow application strategy was proposed.…”

“…For the multi-sever or multi-VM scenario, in [19], the authors proposed a new multiworkflow scheduling method based on edge environment adopting a reliability estimation model and coevolutionary algorithm. The proposed method maximized the reliability of the success rate of workflow task offloading while reducing the cost of service invocation for users.…”