Particle Swarm Based Service Migration Scheme in the Edge Computing Environment

Abstract

With the development of Mobile Edge Computing (MEC), it has become a key technology to realize the vision of the Internet of Things. In MEC, users can upload tasks to edge nodes for faster processing speed and lower local energy consumption. However, as the mobility of users and the limited resources of the edge nodes, some edge nodes cannot provide high-quality services. In this case, we study service migration strategy in the MEC system to migrate services from the initial nodes to other edge nodes that can provide services to meet the needs of users. By making service migration decision and allocating computation resource, our work minimizes the delay and the energy consumption caused by finishing tasks. Specifically, we set up an efficient service migration model and formulate the service migration problem as a non-linear 0-1 programming problem. To solve this problem, we design a Particle Swarm based Service Migration scheme (PSSM) which includes Queuing Delay Prediction algorithm (QDP), Delay-aware Computation Resource Allocation algorithm (DCRA), and Modified Quantum Particle Swarm algorithm (MQPS). For evaluating the performance of the proposed PSSM, we conduct simulation in a practical scenario. The results demonstrate that our scheme not only can effectively reduce delay and energy consumption, but also improve the processing capability of servers.