Computing tasks assignment optimization among edge computing servers via SDN

Abstract

As an extension of cloud computing, the edge computing has become an important pattern to deal with novel service scenarios of Internet of Everything (IoE) under 5G, especially for the delay sensitive computing tasks generated from edge equipment. The edge computing provides the key support to meet the characteristics of delay sensitivity by deploying servers near network edges. However, a great many uneven distributed computing tasks in different network edges usually lead to task processing delay bottleneck for single Edge Computing Server (ECS). Tasks assignment is mainly based on the local ECS status without the global network view considered, which also easily leads to unbalanced task loads among multiple ECSs. In this paper, the novel networking idea of Software Defined Network (SDN) is introduced into the edge computing pattern. The logically highly centralized control plane consists of multiple physically distributed ECSs, so as to collaboratively assign computing tasks in a global view. In order to optimize the task assignment and minimize the task processing delay, three schemes are proposed in this paper. The scheme of assessing the ECS’s task computing features is firstly proposed, then the scheme of predicting the ECS’s future unit task processing time is presented. Thus, different types of computing tasks can be assigned to appropriate ECSs that are better at dealing with them with processing delay minimized. Furthermore, the scheme of optimizing the delay of task processing time estimation is devised, so as to further improve task assignment efficiency. Experimental results show that the proposed mechanism is able to optimize the task assignment and minimize the task processing delay more efficiently than the state of the art. Specifically, our mechanism is capable of improving the average unit task processing delay and the ECS load balancing degree by about 14% and 23% respectively, compared with corresponding work.