A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers

Dabiah Alboaneen*, Hugo Tianfield, Yan Zhang, Bernardi Pranggono

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)
169 Downloads (Pure)


The virtual machine (VM) allocation problem is one of the main issues in cloud data centers. This article proposes a new metaheuristic method to optimize joint task scheduling and VM placement (JTSVMP) in cloud data center. The JTSVMP problem, though composed of two parts, namely task scheduling and VM placement, is treated as a joint problem to be resolved by using metaheuristic optimization algorithms (MOAs). The proposed co-optimization process aims to schedule task into the VM which has the least execution cost within deadline constraint and then to place the selected VM on most utilized physical host (PH) within capacity constraint. To evaluate the performance of our proposed co-optimization process, we compare the performances of two different scenarios, i.e., task scheduling algorithms and integrateion co-optimization of task scheduling and VM placement using MOAs, namely the basic glowworm swarm optimization (GSO), moth-flame glowworm swarm optimization (MFGSO) and genetic algorithm (GA). Simulation results show that optimizing joint task scheduling and VM placement leads to better overall results in terms of minimizing execution cost, makespan and degree of imbalance and maximizing PHs resource utilization.

Original languageEnglish
Pages (from-to)201–212
Number of pages12
JournalFuture Generation Computer Systems
Early online date11 Sep 2020
Publication statusPublished - Feb 2021


  • cloud
  • data center
  • metaheuristic
  • task scheduling
  • virtual machine placement

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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