MCTE: Minimizes Task Completion Time and Execution Cost to Optimize Scheduling Performance for Smart Grid Cloud

• Published in: IEEE access 2019, Vol.7, p.134793-134803
• Main Authors: Zhang, Hualu, Shi, Jie, Deng, Boya, Jia, Gangyong, Han, Guangjie, Shu, Lei
• Format: Article
• Language: English
• Subjects: Algorithms; Cloud computing; Completion time; Heuristic algorithms; Heuristic methods; Heuristic task scheduling; particle swarm optimization-genetic algorithm; Resource allocation; Resource management; Scheduling; Scheduling algorithms; Smart grid; Smart grid cloud; Task analysis; task scheduling
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2942067

Cloud computing is popular in nowadays for its convenient and cheap. Grid provides services of available everywhere, and shares everyone. Therefore, smart grid cloud is a good way to manage data for sharing with all power supply stations. Grid cloud task scheduling is one of the key technologies that affect resource allocation efficiency in cloud computing environment. The advantages and disadvantages of scheduling algorithms will directly affect the scheduling performance of both cloud computing and the stability of the entire system platform. Cloud task scheduling problem has been proved to be a NP-hard problem, The traditional task scheduling algorithm can no longer meet the actual needs of cloud task scheduling, but the Heuristic algorithm is an effective method to solve this problem. This paper studies and analyzes the application of heuristic algorithms in cloud task scheduling problems, and proposes a cloud task scheduling strategy to minimize the task completion time and execution cost (MCTE) for the smart grid cloud. Then, carry out mathematical modeling on the grid cloud task scheduling problem. The experimental results show MCTE is well for the smart grid cloud.