Discrete PSO-based workload optimization in virtual machine placement

Yan, Jianen; Zhang, Hongli; Xu, Haodan; Zhang, Zhaoxin

doi:10.1007/s00779-018-1111-z

Cited by 30 publications

(14 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, it is necessary to define the workload in virtual and physical machines. By combining the load of CPU and memory of virtual machines or physical machines together, a new criterion is defined in Equation ( 6) called the volume which denoted by U [30] stated as follows: The best position that particle has ever received…”

Section: 1 First Goal: Objective Function Of Virtual Machine Placementmentioning

confidence: 99%

Improving the Load Balancing and Dynamic Placement of Virtual Machines in Cloud Computing using Particle Swarm Optimization Algorithm

Yousefipour

Rahmani

Jahanshahi

2021

IJE

View full text Add to dashboard Cite

Nowadays, maximizing profits, decreasing operating cost and scheduling tasks are the most important issues of cloud computing with its growing usage. In this regard, one of the challenges in cloud computing is to provide an efficient method to deploy virtual machines on physical machines with the aim of optimizing energy consumption, fair load distribution and task scheduling. The purpose of present study is to provide a method for improving task scheduling through an improved particle swarm optimization algorithm. In the proposed method of present study, selection of a proper objective function has led to balanced workload of virtual machines, decreased time of all tasks as well as maximum utilization of all resources and increased productivity in addition to dynamic placement of virtual machine on physical machine. The results of simulation showed that the proposed method has provided an optimized solution for scheduling tasks, equal allocation of tasks in virtual machines and placement on the appropriate physical machine and less time with an improvement of 0.02 has been spent on the process of outsourcing virtual machines.

show abstract

Section: 1 First Goal: Objective Function Of Virtual Machine Placementmentioning

confidence: 99%

Improving the Load Balancing and Dynamic Placement of Virtual Machines in Cloud Computing using Particle Swarm Optimization Algorithm

Yousefipour

Rahmani

Jahanshahi

2021

IJE

View full text Add to dashboard Cite

show abstract

“…In [37], Yan et al proposed workload optimization approach for VM placement in the data center. They formulated an optimization model based on resource utilization with the aim of minimizing power consumption.…”

Section: Virtual Machine Placementmentioning

confidence: 99%

Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center

Narantuya

Bae

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

In data centers, cloud-based services are usually deployed among multiple virtual machines (VMs), and these VMs have data traffic dependencies on each other. However, traffic dependency between VMs has not been fully considered when the services running in the data center are expanded by creating additional VMs. If highly dependent VMs are placed in different physical machines (PMs), the data traffic increases in the underlying physical network of the data center. To reduce the amount of data traffic in the underlying network and improve the service performance, we propose a traffic-dependency-based strategy for VM placement in software-defined data center (SDDC). The traffic dependencies between the VMs are analyzed by principal component analysis, and highly dependent VMs are grouped by gravity-based clustering. Each group of highly dependent VMs is placed within an appropriate PM based on the Hungarian matching method. This strategy of dependency-based VM placement facilitates reducing data traffic volume of the data center, since the highly dependent VMs are placed within the same PM. The results of the performance evaluation in SDDC testbed indicate that the proposed VM placement method efficiently reduces the amount of data traffic in the underlying network and improves the data center performance.

show abstract

“…Yan et al study the discrete PSO-based workload optimization in virtual machine placement [11]. Virtual machine placement has great potential to significantly improve the efficiency of resource utilization in a cloud center.…”

Section: Review Of the Contributionsmentioning

confidence: 99%