A PSO‐based task scheduling algorithm improved using a load‐balancing technique for the cloud computing environment

Summary Dynamic on‐demand resource provisioning is one of the primary goals of the cloud computing task scheduling process. Task scheduling is a nondeterministic polynomial time (NP)‐hard problem and is responsible for assigning tasks to virtual machines (VMs) in a way that increases the resource ut...

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Bibliographic Details
Published in:Concurrency and computation Vol. 30; no. 12
Main Authors: Ebadifard, Fatemeh, Babamir, Seyed Morteza
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc 25.06.2018
Subjects:
Algorithms
Basic converters
Cloud computing
Computer simulation
Load balancing
makespan
Particle swarm optimization
Preempting
Production scheduling
Provisioning
Resource allocation
resource utilization
Response time
Scheduling algorithms
Task scheduling
Virtual environments
ISSN:1532-0626, 1532-0634
Online Access:Get full text
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Summary:Summary Dynamic on‐demand resource provisioning is one of the primary goals of the cloud computing task scheduling process. Task scheduling is a nondeterministic polynomial time (NP)‐hard problem and is responsible for assigning tasks to virtual machines (VMs) in a way that increases the resource utilization and performance, reduces response time, and keeps the whole system balanced. In this paper, we present a static task scheduling method based on the particle swarm optimization (PSO) algorithm where the tasks are assumed to be non‐preemptive and independent. We have improved the performance of the basic PSO method using a load‐balancing technique. We have compared our proposed method with round robin (RR) task scheduling, improved PSO task scheduling and a load‐balancing technique. The simulation results show that our method outperforms these algorithms by an increase of resource utilization of 22% and a decrease of makespan by 33%, compared with the basic PSO algorithm. The results illustrate that our proposed method converges to the near optimal solution faster than the basic PSO algorithm and is more efficacious with more tasks.
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ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.4368