An Efficient Scheduling Strategy for Collaborative Cloud and Edge Computing in System of Intelligent Buildings

DOI Web Site Web Site 参考文献20件
  • Feng Xiaodong
    Huaneng Hunan Yueyang Power Generation Co., Ltd. Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, School of Automation and Electronic Information, Xiangtan University
  • Yi Lingzhi
    Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, School of Automation and Electronic Information, Xiangtan University
  • Liu Ning
    Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, School of Automation and Electronic Information, Xiangtan University
  • Gao Xieyi
    Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, School of Automation and Electronic Information, Xiangtan University
  • Liu Weiwei
    Xiangtan of Hunan Branch, China Telecom
  • Wang Bin
    Zhangjiajie of Hunan Branch, China Mobile Communications Group Co., Ltd.

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<p>Edge computing is a new computing method, and task scheduling is challenging work. Using edge computing in intelligent buildings for managing smart home devices has gained popularity because it can reduce the delay and network congestion brought by cloud computing. Edge computing has the advantage of fast response speeds, but its computing capacity is limited. To solve this practical problem, a system framework of collaborative cloud and edge computing is constructed for intelligent buildings. First, the communication time, task completion time, and CPU energy consumption are considered comprehensively, and a mathematical model of the system is developed. Considering the compute-intensity task, the splitting ratio is determined for tasks to achieve the collaboration of cloud computing and edge computing. Then, the search mechanism of a single gene mutation in the genetic algorithm (GA) is introduced to compensate for the defects of the salp swarm algorithm (SSA), while focusing on the search ability and optimization efficiency. Finally, the proposed strategy is theoretically analyzed and experimentally evaluated. The simulation results show that the hybrid algorithm of SSA-GA has better performance than other algorithms, and the proposed collaborative cloud and edge computing task scheduling strategy demonstrated a lower delay and makespan.</p>

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