Zhang, Jiangjian, Ning, Zhenhu, Waqas, Muhammad ORCID: https://orcid.org/0000-0003-0814-7544, Alasmary, Hisham, Tu, Shanshan and Chen, Sheng (2023) Hybrid edge-cloud collaborator resource scheduling approach based on deep reinforcement learning and multi-objective optimization. IEEE Transactions on Computer. ISSN 0018-9340 (Print), 1557-9956 (Online) (doi:10.1109/TC.2023.3326977)

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Abstract

Collaborative resource scheduling between edge ter- minals and cloud centers is regarded as a promising means of effectively completing computing tasks and enhancing quality of service. In this paper, to further improve the achievable perfor- mance, the edge cloud resource scheduling (ECRS) problem is transformed into a multi-objective Markov decision process based on task dependency and features extraction. A multi-objective ECRS model is proposed by considering the task completion time, cost, energy consumption and system reliability as the four objectives. Furthermore, a hybrid approach based on deep reinforcement learning (DRL) and multi-objective optimization are employed in our work. Specifically, DRL preprocesses the workflow, and a multi-objective optimization method strives to find the Pareto-optimal workflow scheduling decision. Various experiments are performed on three real data sets with different numbers of tasks. The results obtained demonstrate that the proposed hybrid DRL and multi-objective optimization design outperforms existing design approaches.

Item Type:	Article
Uncontrolled Keywords:	Edge cloud resource scheduling; deep reinforcement learning; multi-objective optimization; Markov decision process
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	10 Nov 2023 08:55
URI:	http://gala.gre.ac.uk/id/eprint/44853

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