A New Task Scheduling for Minimizing Completion Time and Execution Cost in Smart Grid Cloud

Cloud computing technology has become the most popular network technology. Therefore, it has been combined into grid, establishing smart grid cloud for independent data management of power supply stations. Smart grid cloud task scheduling is one of the key technologies that affect resource allocation efficiency in cloud computing environment. An improved particle swarm optimization-genetic algorithm cloud task scheduling strategy is proposed in this paper. The algorithm aims at minimizing the task completion time and execution cost, and carries out mathematical modeling on the cloud task scheduling problem. Coding the Particle in Particle Swarm Optimization Algorithm, and integrating the crossover and mutation operations of the genetic algorithm into the particle swarm algorithm so that the particle swarm algorithm can better solve the discrete problem. Finally, adding the precocious judgment and chaotic disturbance mechanism to the algorithm to help the algorithm jump out of the local optimum. The experimental results have shown the proposed strategy behaves well.