Energy-efficient workload allocation and computation resource configuration in distributed cloud/edge computing systems with stochastic workloads

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

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

Energy efficiency is one of the most important concerns in cloud/edge computing systems. A major benefit of the Dynamic Voltage and Frequency Scaling (DVFS) technique is that a Virtual Machine (VM) can dynamically scale its computation frequency on an on-demand basis, which is helpful in reducing the energy cost of computation when dealing with stochastic workloads. In this paper, we study the joint workload allocation and computation resource configuration problem in distributed cloud/edge computing. We propose a new energy consumption model that considers the stochastic workloads for computation capacity reconfiguration-enabled VMs. We define Service Risk Probability (SRP) as the probability a VM fails to process the incoming workloads in the current time slot, and we study the energy-SRP tradeoff problem in single VM. Without specifying any distribution of the workloads, we prove that, theoretically there exists an optimal SRP that achieves minimal energy cost, and we derive the closed form of the condition to achieve this minimal energy point. We also derive the closed form for computing the optimal SRP when the workloads follow a Gaussian distribution. We then study the joint workload allocation and computation frequency configuration problem for multiple distributed VMs scenario, and we propose solutions to solve the problem for both Gaussian and unspecified distributions. Our performance evaluation results on both synthetic and real-world workload trace data demonstrate the effectiveness of the proposed model. The closeness between the simulation results and the analytical results prove that our proposed method can achieve lower energy consumption compared with fixed computation capacity configuration methods.

Original languageEnglish
Article number9063490
Pages (from-to)1118-1132
Number of pages15
JournalIEEE Journal on Selected Areas in Communications
Volume38
Issue number6
DOIs
StatePublished - Jun 2020

Bibliographical note

Funding Information:
Manuscript received April 15, 2019; revised December 3, 2019; accepted January 29, 2020. Date of publication April 10, 2020; date of current version May 21, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61772064, in part by the Chinese National Engineering Laboratory for Big Data System Computing Technology, and in part by the Canadian Natural Sciences and Engineering Research Council. (Corresponding author: Zhenjiang Zhang.) Wenyu Zhang and Zhenjiang Zhang are with the Key Laboratory of Communication and Information Systems, School of Electronic and Information Engineering, Beijing Municipal Commission of Education, Beijing Jiao-tong University, Beijing 100044, China (e-mail: wenyuzhang@bjtu.edu.cn; zhjzhang1@bjtu.edu.cn).

Publisher Copyright:
© 1983-2012 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Computation capacity scaling
  • cloud/edge computing
  • energy efficiency
  • service risk probability
  • stochastic workload
  • workload allocation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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