Task Migration with Partitioning for Load Balancing in Collaborative Edge Computing

Abstract

Multi-access edge computing (MEC) has emerged as a promising technology to facilitate efficient vehicular applications, such as autonomous driving, path planning and navigation. By offloading tasks from vehicles to MEC servers (MECSs), the MEC system can facilitate computation-intensive applications with hard latency constraints in vehicles with limited computing resources. However, owing to the mobility of vehicles, the vehicles are not evenly distributed across the MEC system. Therefore, some MECSs are heavily congested, whereas others are lightly loaded. If a task is offloaded to a congested MECS, it can be blocked or have high latency. Moreover, service interruption would occur because of the high mobility and limited coverage of the MECS. In this paper, we assume that the task can be divided into a set of subtasks and computed by multiple MECSs in parallel. Therefore, we propose a method of task migration with partitioning. To balance loads, the MEC system migrates the set of subtasks of tasks in an overloaded MECS to one or more underloaded MECSs according to the load difference. Simulations have indicated that, compared with conventional methods, the proposed method can increase the satisfaction of quality-of-service requirements, such as low latency, service reliability, and MEC system throughput by optimizing load balancing and task partitioning.