QoS-aware online scheduling of multiple workflows under task execution time uncertainty in clouds

Ahmad Taghinezhad-Niar, Saeid Pashazadeh*, Javid Taheri

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Cloud computing, with elasticity and pay-as-you-go pricing, is a suitable platform for executing workflow applications. Workflow as a Service (WaaS) systems provide scientists with an easy-to-use, and cost-effective platform to execute their workflow applications in the cloud at any time or location worldwide. Quality of Service (QoS) is recognized as a key requirement in WaaS. Monetary cost and time are two primary QoS from a clients’ perspective; whereas, energy consumption is considered a significant problem for cloud providers’ profitability and ability to provide low-cost services. Most workflow scheduling studies assume that workflow tasks have a deterministic Execution Time (ET), which is generally unrealistic in the real world. However, there are few approaches for scheduling in WaaS considering deadlines, and monetary costs with uncertain task ET. These studies typically assume that a cloud resource can execute all types of workflow applications without any need for additional software components. However, using containers is a suitable solution to provide an executable environment for the execution of any workflow type on cloud resources. To this end, we present two cost and energy-aware workflow scheduling that consider the uncertainty in tasks’ ETs. Both solutions are designed for WaaS, leveraging containers to enhance resource utilization rate and reduce energy consumption, resource monetary cost, and workflows deadline violations. Simulated experiments demonstrate that our proposed methods outperform two recent state-of-the-art scheduling algorithms in terms of success rate, monetary cost, energy consumption, and resource utilization rate.

Original languageEnglish
Pages (from-to)3767-3784
Number of pages18
JournalCluster Computing
Volume25
Issue number6
Early online date12 May 2022
DOIs
Publication statusPublished - Dec 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Container
  • Energy
  • Scheduling
  • Uncertain execution time
  • Workflow as a service

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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