Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization

Live virtual machine (VM) migration is a technique for achieving system load balancing in a cloud environment by transferring an active VM from one physical host to another. This technique has been proposed to reduce the downtime for migrating overloaded VMs, but it is still time- and cost-consuming...

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Vydané v:International journal of parallel programming Ročník 42; číslo 5; s. 739 - 754
Hlavní autori: Ramezani, Fahimeh, Lu, Jie, Hussain, Farookh Khadeer
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Boston Springer US 01.10.2014
Springer Nature B.V
Predmet:
Algorithms
Analysis
Cloud computing
Computer programming
Computer Science
Computer simulation
Cost control
Customer satisfaction
Downtime
Load
Load balancing (computing)
Migration
Optimization
Parallel processing
Processor Architectures
Scandals
Scheduling
Software Engineering/Programming and Operating Systems
Software services
Studies
Swarm intelligence
Tasks
Theory of Computation
Task scheduling
Cloud computing
Virtual machine migration
Cloudsim
Jswarm
Particle swarm optimization
ISSN:0885-7458, 1573-7640
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Popis
Shrnutí:Live virtual machine (VM) migration is a technique for achieving system load balancing in a cloud environment by transferring an active VM from one physical host to another. This technique has been proposed to reduce the downtime for migrating overloaded VMs, but it is still time- and cost-consuming, and a large amount of memory is involved in the migration process. To overcome these drawbacks, we propose a Task-based System Load Balancing method using Particle Swarm Optimization (TBSLB-PSO) that achieves system load balancing by only transferring extra tasks from an overloaded VM instead of migrating the entire overloaded VM. We also design an optimization model to migrate these extra tasks to the new host VMs by applying Particle Swarm Optimization (PSO). To evaluate the proposed method, we extend the cloud simulator (Cloudsim) package and use PSO as its task scheduling model. The simulation results show that the proposed TBSLB-PSO method significantly reduces the time taken for the load balancing process compared to traditional load balancing approaches. Furthermore, in our proposed approach the overloaded VMs will not be paused during the migration process, and there is no need to use the VM pre-copy process. Therefore, the TBSLB-PSO method will eliminate VM downtime and the risk of losing the last activity performed by a customer, and will increase the Quality of Service experienced by cloud customers.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-013-0275-4