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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Veröffentlicht in:	International journal of parallel programming Jg. 42; H. 5; S. 739 - 754
Hauptverfasser:	Ramezani, Fahimeh, Lu, Jie, Hussain, Farookh Khadeer
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.10.2014 Springer Nature B.V
Schlagworte:	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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Abstract	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.
AbstractList	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. 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.[PUBLICATION ABSTRACT]
Author	Hussain, Farookh Khadeer Lu, Jie Ramezani, Fahimeh
Author_xml	– sequence: 1 givenname: Fahimeh surname: Ramezani fullname: Ramezani, Fahimeh email: Fahimeh.Ramezani@student.uts.edu.au organization: Decision Systems and e-Service Intelligence Lab, Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, School of Software, University of Technology, Sydney – sequence: 2 givenname: Jie surname: Lu fullname: Lu, Jie organization: Decision Systems and e-Service Intelligence Lab, Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, School of Software, University of Technology, Sydney – sequence: 3 givenname: Farookh Khadeer surname: Hussain fullname: Hussain, Farookh Khadeer organization: Decision Systems and e-Service Intelligence Lab, Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, School of Software, University of Technology, Sydney
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SubjectTerms	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
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Title	Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization
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