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

Minoo Hosseinzadeh, Andrew Wachal, Hana Khamfroush, Daniel E. Lucani

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

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
Pages (from-to)319-325
Number of pages7
JournalProceedings - IEEE Consumer Communications and Networking Conference, CCNC
DOIs
StatePublished - 2022
Event19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022 - Virtual, Online, United States
Duration: Jan 8 2022Jan 11 2022

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Deep Learning
  • Edge Computing
  • Priority
  • Quality-of-Service
  • Resource Management
  • Task Offloading

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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