Energy-efficient task scheduling and consolidation algorithm for workflow jobs in cloud

Mustafa Khaleel, Michelle M. Zhu

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

The energy consumption of underlying cloud hardware has dramatically increased. The cloud service providers need to adopt some cost-effective and energy-Aware job scheduler without compromising the quality of service (QoS) specified in the service level agreement (SLA). Based on a rigorous mathematical model, we formulate an energy efficient problem to improve the resource utilisation for high system throughput. A multiple-procedure heuristic workflow scheduling and consolidation strategy is proposed with objectives to maximise the resource utilisation and minimise the power. Several techniques have been utilised including dynamic voltage and frequency scaling (DVFS) with task module migration for workload balance and task consolidation for virtual machine (VM) overhead reduction. The simulation results illustrate that our approach consistently achieves a lower power consumption and higher resource utilisation rate within the execution time bound compared with other similar scheduling algorithms as well as our previous algorithm without the task migration based on VM threshold.

Original languageEnglish
Pages (from-to)268-284
Number of pages17
JournalInternational Journal of Computational Science and Engineering
Volume13
Issue number3
DOIs
StatePublished - 1 Jan 2016

Fingerprint

Task Scheduling
Consolidation
Energy Efficient
Work Flow
Scheduling
Virtual Machine
Resources
Migration
Scheduling algorithms
Quality of service
Service Level Agreement
Electric power utilization
Energy utilization
Throughput
Mathematical models
Scheduler
Hardware
Scheduling Algorithm
Execution Time
Power Consumption

Keywords

  • Cloud computing
  • Dag
  • Directed acyclic graph
  • Energy-efficient
  • Scientific workflow scheduling
  • Task consolidation

Cite this

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Energy-efficient task scheduling and consolidation algorithm for workflow jobs in cloud. / Khaleel, Mustafa; Zhu, Michelle M.

In: International Journal of Computational Science and Engineering, Vol. 13, No. 3, 01.01.2016, p. 268-284.

Research output: Contribution to journalArticle

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