Abstract:

The existing task scheduling methods for edge computing do not consider the problem of uncertain performance of edge nodes caused by network delay,and cannot adapt to the delay sensitive edge computing platform with uncertain node performance.To solve this problem,this paper proposes a cluster-oriented semi-online scheduling method for delay-sensitive edge computing platform.First,considering the nodes with uncertain performance caused by network delay,a performance uncertainty metric is designed to represent the degree of performance certainty of edge nodes.This metric provides extra pieces of information for the semi-online scheduling algorithm.Second,a dual-objective QoS guarantee model and a task completion time optimization model are proposed to establish a dual-objective optimization model for task scheduling,which focuses on guaranteeing QoS and minimizing makespan.Third,to address the NP-hard problem of task scheduling,a mapping-based semi-online task scheduling algorithm(MSSA) is proposed which divides the service area range based on the performance uncertainty metric and user location,establishes a cluster-oriented edge computing platform model,and dynamically adjusts cluster capacity based on load changes,thus enabling efficient semi-online task scheduling.Finally,by using trace data from a real edge computing platform,simulation experiments are conducted to compare the proposed algorithm with other methods.Experimental results demonstrate that the proposed algorithm can reduce the task completion time by 26% and improves the QoS guarantee by 19%.

Key words: edge computing, scheduling algorithms, quality of service, makespan