Abstract:

When there are known and unknown computing nodes in the edge computing platform,the task scheduling in this scene is called semi-online task scheduling.Due to the influence of unknown nodes,the normal task scheduling method may lead to a long makespan or transmission time,which aggravates the problem of high energy consumption on the edge computing platform.To solve this problem,this paper proposes a Dynamic semi-online task Scheduling Strategy (DSS) for the edge computing platform,aiming at energy consumption optimization.First,by considering the main factors affecting the energy consumption on the edge computing platform,the energy consumption of task execution,task transmission and idle are introduced from the perspectives of the processing speed,routing delay and queue delay of the edge nodes,and then an energy consumption optimization-oriented task scheduling model is established.Second,for the unknown node,this paper proposes a dynamic mapping-based semi-online task scheduling algorithm which assumes that the performances of unknown nodes are equal to a certain given node to form the mapping between unknown and known nodes.Then this algorithm dynamically adjusts the mapping relation of both sides through continuous perception of their task queue lengths,thus making full use of prior knowledge and reducing the energy consumption.Finally,a comparative evaluation is performed on the CloudSim platform,with the results showing that the proposed method can effectively reduce the energy consumption on the edge computing platform.

Key words: edge computing, task scheduling, unknown computing node, dynamic mapping