Flow-time minimization for timely data stream processing in UAV-aided mobile edge computing

Xu, Zichuan, Qiao, Haiyang, Liang, Weifa, Xu, Zhou, Xia, Qiufen, Zhou, Pan, Rana, Omer F. ORCID: https://orcid.org/0000-0003-3597-2646 and Xu, Wenzheng 2024. Flow-time minimization for timely data stream processing in UAV-aided mobile edge computing. ACM Transactions on Sensor Networks 20 (3) , 58. 10.1145/3643813

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Abstract

Unmanned Aerial Vehicle (UAV) has gained increasing attentions by both academic and industrial communities, due to its flexible deployment and efficient line-of-sight communication. Recently, UAVs equipped with base stations have been envisioned as a key technology to provide 5G network services for mobile users. In this paper, we provide timely services on the data streams of mobile users in a UAV-aided Mobile Edge Computing (MEC) network, in which each UAV is equipped with a 5G small-cell base station for communication and data processing. Specifically, we first formulate a flow-time minimization problem by jointly caching services and offloading tasks of mobile users to the UAV-aided MEC with the aim to minimize the flow-time, where the flow-time of a user request is referred to the time duration from the request issuing time point to its completion point, subject to resource and energy capacity on each UAV. We then propose a spatial-temporal learning optimization framework. We also devise an online algorithm with a competitive ratio for the problem based upon the framework, by leveraging the round-robin scheduling and dual fitting techniques. Finally, we evaluate the performance of the proposed algorithms through experimental simulation. The simulation results demonstrated that the proposed algorithms outperform their comparison counterparts, by reducing the flow-time no less than 19% on average.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computing Machinery (ACM)
ISSN:	1550-4859
Date of First Compliant Deposit:	25 March 2024
Date of Acceptance:	11 January 2024
Last Modified:	10 Nov 2024 20:45
URI:	https://orca.cardiff.ac.uk/id/eprint/166244

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