MASM: A Multiple-Algorithm Service Model for Energy-Delay Optimization in Edge Artificial Intelligence

Wenyu Zhang, Zhenjiang Zhang, Sherali Zeadally, Han Chieh Chao, Victor C.M. Leung

Research output: Contribution to journalArticlepeer-review

84 Scopus citations


Edge computing has emerged as a promising technique because of its advantages in providing low-latency computation offloading services for resource-limited mobile user devices and Internet of Things applications. Computationally intensive artificial intelligence (AI) tasks are well suited to be offloaded to the Cloudlet server, but there is a lack of energy-delay optimization models specifically designed for this edge AI scenario. In this paper, we propose a multiple algorithm service model (MASM) that provides heterogeneous algorithms with different computation complexities and required data sizes to fulfill the same task, and develop an optimization model that aims at reducing the energy and delay cost by optimizing the workload assignment weights and computing capacities of virtual machines, at the same time guaranteeing the quality of the results (QoRs). We propose a tide ebb algorithm to solve the MASM optimization model, and we prove its Parato optimality. Numerical results obtained demonstrate the effectiveness of our proposed method, and prove that the energy and delay costs can be significantly reduced by sacrificing the QoR of the offloaded AI tasks.

Original languageEnglish
Article number8632751
Pages (from-to)4216-4224
Number of pages9
JournalIEEE Transactions on Industrial Informatics
Issue number7
StatePublished - Jul 2019

Bibliographical note

Funding Information:
Manuscript received December 1, 2018; revised January 7, 2019; accepted January 27, 2019. Date of publication February 1, 2019; date of current version July 3, 2019. This work was supported in part by National Natural Science Foundation of China under Grant 61772064, and in part by the Academic Discipline, Post-Graduate Education Project of the Beijing Municipal Commission of Education. Paper no. TII-18-3219. (Corresponding author: Zhenjiang Zhang.) W. Zhang and Z. Zhang are with the School of Electronic and Information Engineering, Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China (e-mail:,;

Publisher Copyright:
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Copyright 2019 Elsevier B.V., All rights reserved.


  • Artificial intelligence (AI)
  • edge computing
  • energy-delay optimization
  • resource management
  • workload assignment

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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