Balancer genetic algorithm-a novel task scheduling optimization approach in cloud computing

Rohail Gulbaz, Abdul Basit Siddiqui*, Nadeem Anjum, Abdullah Alhumaidi Alotaibi, Turke Althobaiti, Naeem Ramzan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Task scheduling is one of the core issues in cloud computing. Tasks are heterogeneous, and they have intensive computational requirements. Tasks need to be scheduled on Virtual Machines (VMs), which are resources in a cloud environment. Due to the immensity of search space for possible mappings of tasks to VMs, meta-heuristics are introduced for task scheduling. In scheduling makespan and load balancing, Quality of Service (QoS) parameters are crucial. This research contributes a novel load balancing scheduler, namely Balancer Genetic Algorithm (BGA), which is presented to improve makespan and load balancing. Insufficient load balancing can cause an overhead of utilization of resources, as some of the resources remain idle. BGA inculcates a load balancing mechanism, where the actual load in terms of million instructions assigned to VMs is considered. A need to opt for multi-objective optimization for improvement in load balancing and makespan is also emphasized. Skewed, normal and uniform distributions of workload and different batch sizes are used in experimentation. BGA has exhibited significant improvement compared with various state-of-the-art approaches for makespan, throughput and load balancing.

Original languageEnglish
Article number6244
Number of pages24
JournalApplied Sciences (Switzerland)
Volume11
Issue number14
DOIs
Publication statusPublished - 6 Jul 2021

Keywords

  • cloud computing
  • load balancing
  • optimization
  • task scheduling
  • virtual machines

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