Scheduling Real-Time Parallel Applications in Cloud to Minimize Energy Consumption

Biao Hu, Zhengcai Cao, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


Cloud computing has become an important paradigm in which scalable resources such as CPU, memory, disk and IO devices can be provided to users to remotely process their applications. In a cloud computing platform, energy consumption accounts for a significant cost portion. This article thus aims to present an energy-efficient scheduling algorithm for processing a user application with a real-time requirement. This problem is formulated as a non-linear mixed integer programming problem. We start with providing an optimal closed-form solution to its relaxation problem that aims to minimize the energy consumption without considering real-time requirements. To meet real-time requirements, we propose how to adjust task placement and resource allocation by making a good tradeoff between energy consumption and task execution time. Lastly, we find two equivalent optimal resource allocation strategies once task placement has been done. We then propose to adjust the start time of task execution such that an application's completion time can be further shortened. Experimental results on two real-case enchmarks and extensive synthetic applications demonstrate that our proposed method finds a schedule that generally has 30 and 20 percent less energy consumption than enhancement heterogeneous earliest finish time (E-HEFT) and genetic algorithm, respectively. Besides, the proposed method has a higher rate to successfully find a feasible schedule than them, and its computation time is close to E-HEFT's, but far less than the genetic algorithm's.

Original languageEnglish (US)
Pages (from-to)662-674
Number of pages13
JournalIEEE Transactions on Cloud Computing
Issue number1
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


  • Energy consumption minimization
  • cloud computing
  • optimization methods
  • parallel application
  • real-time scheduling


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