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A DRL-based service offloading approach using DAG for edge computational orchestration.

Mekala, M.S.; Dhiman, Gaurav; Srivastava, Gautam; Nain, Zulqar; Zhang, Haolin; Viriyasitavat, Wattana; Varma, G.P.S.

Authors

Gaurav Dhiman

Gautam Srivastava

Zulqar Nain

Haolin Zhang

Wattana Viriyasitavat

G.P.S. Varma



Abstract

Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high required computational social service requests (SRs) in a time slot based on directed acyclic graph (DAG) is an NP-complete problem. Most state-of-art methods concentrate on the energy preservation of networks but neglect the resource sharing cost and dynamic subservice execution time (SET) during the computation and resource sharing. This article proposes a two-step deep reinforcement learning (DRL)-based service offloading (DSO) approach to diminish edge server costs through a DRL influenced resource and SET analysis (RSA) model. In the first level, the service and edge server cost is considered during service offloading. In the second level, the R-retaliation method evaluates resource factors to optimize resource sharing and SET fluctuations. The simulation results show that the proposed DSO approach achieves low execution costs by streamlining dynamic service completion and transmission time, server cost, and deadline violation rate attributes. Compared to the state-of-art approaches, our proposed method has achieved high resource usage with low energy consumption.

Citation

MEKALA, M.S., DHIMAN, G., SRIVASTAV, G., NAIN, Z., ZHANG, H., VIRIYASITAVAT, W. and VARMA, G.P.S. 2022. A DRL-based service offloading approach using DAG for edge computational orchestration. IEEE transactions on computational social systems [online], Early Access. Available from: https://doi.org/10.1109/tcss.2022.3161627

Journal Article Type Article
Acceptance Date Mar 18, 2022
Online Publication Date Apr 12, 2022
Deposit Date Feb 27, 2023
Publicly Available Date Feb 27, 2023
Journal IEEE Transactions on Computational Social Systems
Print ISSN 2373-7476
Electronic ISSN 2329-924X
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/tcss.2022.3161627
Keywords Adaptive quality-of-service (QoS); Deep reinforcement learning (DRL) method; Edge computing; Optimal measurement analysis; Service offloading (SO) and scheduling
Public URL https://rgu-repository.worktribe.com/output/1867343

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MEKALA 2022 A DRL-based service (AAM) (1.3 Mb)
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Copyright Statement
© 2022 IEEE.





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