A New Task Scheduling Scheme Based on Genetic Algorithm for Edge Computing

With the continuous evolution of smart grid and global energy interconnection technology, amount of intelligent terminals have been connected to power grid, which can be used for providing resource services as edge nodes. Traditional cloud computing can be used to provide storage services and task computing services in the power grid, but it faces challenges such as resource bottlenecks, time delays, and limited network bandwidth resources. Edge computing is an effective supplement for cloud computing, because it can provide users with local computing services with lower latency. However, because the resources in a single edge node are limited, resource-intensive tasks need to be divided into many subtasks and then assigned to different edge nodes by resource cooperation. Making task scheduling more efficient is an important issue. In this paper, a two-layer resource management scheme is proposed based on the concept of edge computing. In addition, a new task scheduling algorithm named GA-EC(Genetic Algorithm for Edge Computing) is put forth, based on a genetic algorithm, that can dynamically schedule tasks according to different scheduling goals. The simulation shows that the proposed algorithm has a beneficial effect on energy consumption and load balancing, and reduces time delay.