Resource offload consolidation based on deep-reinforcement learning approach in cyber-physical systems.

Mekala, M.S.; Jolfaei, Alireza; Srivastava, Gautam; Zheng, Xi; Anvari-Moghaddam, Amjad; Viswanathan, P.

Abstract

In cyber-physical systems, it is advantageous to leverage cloud with edge resources to distribute the workload for processing and computing user data at the point of generation. Services offered by cloud are not flexible enough against variations in the size of underlying data, which leads to increased latency, violation of deadline and higher cost. On the other hand, resolving above-mentioned issues with edge devices with limited resources is also challenging. In this work, a novel reinforcement learning algorithm, Capacity-Cost Ratio-Reinforcement Learning (CCR-RL), is proposed which considers both resource utilization and cost for the target cyber-physical systems. In CCR-RL, the task offloading decision is made considering data arrival rate, edge device computation power, and underlying transmission capacity. Then, a deep learning model is created to allocate resources based on the underlying communication and computation rate. Moreover, new algorithms are proposed to regulate the allocation of communication and computation resources for the workload among edge devices and edge servers. The simulation results demonstrate that the proposed method can achieve a minimal latency and a reduced processing cost compared to the state-of-the-art schemes.

Citation

MEKALA, M.S., JOLFAEI, A., SRIVASTAVA, G., ZHENG, X., ANVARI-MOGHADDAM, A. and VISWANATHAN, P. 2022. Resource offload consolidation based on deep-reinforcement learning approach in cyber-physical systems. IEEE transactions on emerging topics in computational intelligence [online], 6(2), pages 245-254. Available from: https://doi.org/10.1109/tetci.2020.3044082

• Journal Article Type: Article
• Acceptance Date: Dec 1, 2020
• Online Publication Date: Dec 28, 2020
• Publication Date: Apr 30, 2022
• Deposit Date: Feb 27, 2023
• Publicly Available Date: Feb 27, 2023
• Journal: IEEE Transactions on Emerging Topics in Computational Intelligence
• Print ISSN: 2471-285X
• Electronic ISSN: 2471-285X
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 6
• Issue: 2
• Pages: 245-254
• DOI: https://doi.org/10.1109/tetci.2020.3044082
• Keywords: Artificial Intelligence; Edge computing; Game theory; Deep-reinforcement learning; Resource provision; Measurement systems
• Public URL: https://rgu-repository.worktribe.com/output/1867381