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QoS-Aware Priority-Based Task Offloading for Deep Learning Services at the Edge

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

  • Minoo Hosseinzadeh, University of Kentucky, United States
  • Andrew Wachal, University of Kentucky, United States
  • Hana Khamfroush, University of Kentucky, United States
  • Daniel Enrique Lucani Rötter
Emerging Edge Computing~(EC) technology has shown promise for many delay-sensitive Deep Learning~(DL) based applications of smart cities in terms of improved Quality-of-Service~(QoS). EC requires judicious decisions which jointly consider the limited capacity of the edge servers and provided QoS of DL-dependent services. In a smart city environment, tasks may have varying priorities in terms of when and how to serve them; thus, priorities of the tasks have to be considered when making resource management decisions. In this paper, we focus on finding optimal offloading decisions in a three-tier user-edge-cloud architecture while considering different priority classes for the DL-based services and making a trade-off between a task's completion time and the provided accuracy by the DL-based service. We cast the optimization problem as an Integer Linear Program~(ILP) where the objective is to maximize a function called gain of system~(GoS) defined based on provided QoS and priority of the tasks. We prove the problem is NP-hard. We then propose an efficient offloading algorithm, called PGUS, that is shown to achieve near-optimal results in terms of the provided GoS. Finally, we compare our proposed algorithm, PGUS, with heuristics and a state-of-the-art algorithm, called GUS, using both numerical analysis and real-world implementation. Our results show that PGUS outperforms GUS by a factor of 45\% in average in terms of serving the top 25\% higher priority classes of the tasks while still keeping the overall percentage of the dropped tasks minimal and the overall gain of system maximized.
Original languageEnglish
Title of host publication2022 IEEE Annual Consumer Communications & Networking Conference (CCNC)
Number of pages7
Publication year2022
ISBN (print)978-1-6654-3161-3
Publication statusPublished - 2022
EventIEEE 19th Annual Consumer Communications & Networking Conference (CCNC) - Las Vegas, United States
Duration: 8 Jan 202211 Jan 2022
Conference number: 19


ConferenceIEEE 19th Annual Consumer Communications & Networking Conference (CCNC)
LandUnited States
By Las Vegas
SeriesProceedings of the IEEE Consumer Communications and Networking Conference (CCNC)

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