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JACIII Vol.27 No.5 pp. 948-958
doi: 10.20965/jaciii.2023.p0948
(2023)

Research Paper:

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

Xiaodong Feng*1,*2,†, Lingzhi Yi*2, Ning Liu*2, Xieyi Gao*2, Weiwei Liu*3, and Bin Wang*4

*1Huaneng Hunan Yueyang Power Generation Co., Ltd.
Yueyanglou District, Yueyang, Hunan 414000, China

*2Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, School of Automation and Electronic Information, Xiangtan University
Yuhu District, Xiangtan, Hunan 411105, China

*3Xiangtan of Hunan Branch, China Telecom
Yuhu District, Xiangtan, Hunan 411100, China

*4Zhangjiajie of Hunan Branch, China Mobile Communications Group Co., Ltd.
Yingbin Road, Yongding District, Zhangjiajie, Hunan 427000, China

Corresponding author

Received:
March 22, 2021
Accepted:
June 9, 2023
Published:
September 20, 2023
Keywords:
edge computing, collaborative cloud and edge computing, task scheduling, intelligent building, latency
Abstract

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.

Cloud-edge collaboration framework for intelligent buildings

Cloud-edge collaboration framework for intelligent buildings

Cite this article as:
X. Feng, L. Yi, N. Liu, X. Gao, W. Liu, and B. Wang, “An Efficient Scheduling Strategy for Collaborative Cloud and Edge Computing in System of Intelligent Buildings,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.5, pp. 948-958, 2023.
Data files:
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Last updated on Jul. 12, 2024