QoS and profit aware task scheduling with simulated-annealing-based bi-objective differential evolution in green clouds

Haitao Yuan, Jing Bi, Mengchu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Distributed clouds (DCs) often require a huge amount of energy to provide multiple services to users around the world. Users bring revenue to DC providers based on the quality of service (QoS) of tasks. These tasks are transmitted to DCs through many available Internet service providers (ISPs) with different bandwidth prices and capacities. Besides, power grid prices, and green energy in different DCs differ with different geographical sites. Consequently, it is challenging to execute tasks among DCs in a high-QoS and high-profit way. This work proposes a bi-objective optimization algorithm to maximize the profit of a DC provider, and minimize the loss possibility of all tasks by specifying the allocation of tasks among different ISPs, and task service rates of each DC. A constrained optimization problem is given and solved by a novel Simulated-annealing-based Bi-objective Differential Evolution (SBDE) algorithm to produce a close-to-optimal Pareto set of solutions. The minimum Manhattan distance is further used to obtain a knee solution, and it determines Pareto optimal service rates and task allocation among ISPs. Realistic trace-driven results demonstrate that SBDE realizes less loss possibility of tasks, and higher profit than several state-of-the-art scheduling algorithms.

Original languageEnglish (US)
Title of host publication2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PublisherIEEE Computer Society
Pages904-909
Number of pages6
ISBN (Electronic)9781728103556
DOIs
StatePublished - Aug 2019
Event15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada
Duration: Aug 22 2019Aug 26 2019

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2019-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference15th IEEE International Conference on Automation Science and Engineering, CASE 2019
CountryCanada
CityVancouver
Period8/22/198/26/19

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Bi-objective differential evolution
  • Data centers
  • Green clouds
  • Optimization
  • Quality of service
  • Simulated annealing

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