A Game Theoretical Balancing Approach for Offloaded Tasks in Edge Datacenters

Abstract

Edge computing is the next-generation computing paradigm that brings the processing capability closer to the location where it is needed. 5G and beyond 5G aim to achieve substantial improvement for the performance of edge computing in terms of e.g. higher throughput and lower latency. Smart base stations are often attached with edge datacenters consisting of many edge servers equipped with computing and storage capabilities. These servers are used to execute offloaded tasks from edge equipment such as Internet of Things. It is important to have an efficient offloading algorithm that can guarantee specific service-level objectives (SLOs) by assigning tasks to appropriate edge servers. Traditional offloading schemes such as static and learning-based algorithms either have limited performance or result in high overhead for task assignment to servers. In this paper, we propose an efficient game-theoretical scheduling algorithm for offloaded tasks at edge datacenters. The core contribution of the algorithm is to design a public goods investment model for edge servers. Based on the model, we design a lightweight scheduling algorithm to reduce the average load of edge servers and enhance the stability of edge datacenter systems. Experimental results demonstrate the significant benefits of the proposed algorithm in reducing the response latency of tasks and balancing the workload of edge servers.