Hybrid edge-cloud collaborator resource scheduling approach based on deep reinforcement learning and multiobjective optimization

Abstract

—Collaborative resource scheduling between edge terminals 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 performance, 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.

Document Type

Journal Article

Date of Publication

1-1-2024

Volume

73

Issue

1

Publication Title

IEEE Transactions on Computers

Publisher

IEEE

School

School of Engineering

RAS ID

62435

Funders

National Key Research and Development Project of China / National Natural Science Foundation of China / King Khalid University

Comments

Zhang, J., Ning, Z., Waqas, M., Alasmary, H., Tu, S., & Chen, S. (2024). Hybrid edge-cloud collaborator resource scheduling approach based on deep reinforcement learning and multiobjective optimization. IEEE Transactions on Computers, 73(1), 192-205. https://doi.org/10.1109/TC.2023.3326977

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subscription content

First Page

192

Last Page

205

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Link to publisher version (DOI)

10.1109/TC.2023.3326977