Title: Fog-cloud task scheduling of energy consumption optimisation with deadline consideration

Authors: Jiuyun Xu; Xiaoting Sun; Ruru Zhang; Hongliang Liang; Qiang Duan

Addresses: School of Computer and Communication Engineering, China University of Petroleum, Qingdao, China ' School of Computer and Communication Engineering, China University of Petroleum, Qingdao, China ' The China Mobile (Suzhou) Software Technology Company, No. 58 Kunshan Road, Science and Technology City, Suzhou High-Tech Zone, Jiangsu Province, China ' Department of Informatics, Beijing University of Posts and Telecommunications, Beijing, China ' Information Sciences and Technology Department, Pennsylvania State University, Pennsylvania, USA

Abstract: The emerging IoT introduces many new challenges that cannot be adequately addressed by the current 'cloud-only' architectures. The cooperation of the fog and cloud is considered to be a promising architecture, which efficiently handles IoT's data processing and communications requirements. However, how to schedule tasks to better adapt to IoT real-time needs and reduce the energy in the fog-cloud system is not well addressed. In this paper, we first model the energy consumption of the fog and cloud, respectively, and formulate a task scheduling problem into a constrained optimisation problem in fog-cloud computing system. Then, an efficient deadline-energy scheduling algorithm based on ant colony optimisation (DEACO) is put forward to tackle this problem, which achieves to reduce energy consumption on the condition of satisfying the task deadline. Finally, algorithms have been simulated on the extended CloudSim simulator. The experimental results have shown that our scheduling approach reduces energy more effective.

Keywords: internet of things; IoT; cloud computing; fog computing; energy consumption; task scheduling; optimal ant colony algorithm.

DOI: 10.1504/IJIMS.2020.110228

International Journal of Internet Manufacturing and Services, 2020 Vol.7 No.4, pp.375 - 392

Received: 03 Jul 2018
Accepted: 05 Nov 2018
Published online: 12 Oct 2020 *

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