Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine Placement

Mohazabiyeh, A. R.; Amirizadeh, Khosrow

doi:10.11591/ijece.v8i5.pp3890-3901

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…The authors [28] proposed optimal technique based on four-adaptive threshold model to reduce energy consumption. Hosts are clustered into five clusters using K-Means Clustering algorithm viz., 1.…”

Section: Other Heuristicsmentioning

confidence: 99%

Bin packing algorithms for virtual machine placement in cloud computing: a review

Kumaraswamy¹,

Nair

2019

IJECE

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<p>Cloud computing has become more commercial and familiar. The Cloud data centers havhuge challenges to maintain QoS and keep the Cloud performance high. The placing of virtual machines among physical machines in Cloud is signiﬁcant in optimizing Cloud performance. Bin packing based algorithms are most used concept to achieve virtual machine placement(VMP). This paper presents a rigorous survey and comparisons of the bin packing based VMP methods for the Cloud computing environment. Various methods are discussed and the VM placement factors in each methods are analyzed to understand the advantages and drawbacks of each method. The scope of future research and studies are also highlighted.</p>

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Section: Other Heuristicsmentioning

confidence: 99%

Bin packing algorithms for virtual machine placement in cloud computing: a review

Kumaraswamy¹,

Nair

2019

IJECE

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“…The features of the VM resemble the features of AmazonEC2 instance types, but with single core VM. The model uses the Minimum Migration Time (MMT) policy which selects the VM with minimum migration time requirement [24,25].…”

Section: Performance Evaluationmentioning

confidence: 99%

Performance degradation assessment and VM placement policy in cloud

Nair¹,

Nair²

2019

IJECE

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In virtualized servers, with live migration technique pages are copied from one physical machine to another while the virtual machine (VM) is running. The dynamic migration of virtual machines encumbers the data center which in turn reduces the performance of applications running on that particular physical machine. A considerable number of studies have been carried out in the area of performance evaluation during live VM migration. However, all the aspects related to the migration process have not been examined for the performance assessment. In this paper, we propose a novel approach to evaluate the performance during migration process in different types of coupled machine environment. It is presented here that the state of art VM migration technology requires further improvement in realizing effective migration by monitoring comprehensive performance value. We introduced the parameter, θ, to compare performance value which can be used for controlling and halting unsuccessful migration and save significant amount of time in migration operation. Our model is capable of analyzing real time scenario of cloud performance assessment targeting VM migration strategies. It also offers the possibility of further expanding to universal models for analyzing the performance variations that occurs as a result of VM migration.

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“…Each VM runs different OSs and applications; the virtualization cuts the need for additional hardware infrastructure and affords a cost-effective way to run multiple services. Malhotra et al [4] discuss the virtualization in Cloud systems [5][6][7][8][9][10][11]. Authors start with an introduction, characterization and history of cloud systems.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of load balancing approaches for Erlang concurrent application in cloud systems

Jittawiriyanukoon¹

2020

TELKOMNIKA

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Cloud system accommodates the computing environment including PaaS (platform as a service), SaaS (software as a service), and IaaS (infrastructure as service) that enables the services of cloud systems. Cloud system allows multiple users to employ computing services through browsers, which reflects an alternative service model that alters the local computing workload to a distant site. Cloud virtualization is another characteristic of the clouds that deliver virtual computing services and imitate the functionality of physical computing resources. It refers to an elastic load balancing management that provides the flexible model of on-demand services. The virtualization allows organizations to improve high levels of reliability, accessibility, and scalability by having a capability to execute applications on multiple resources simultaneously. In this paper we use a queuing model to consider a flexible load balancing and evaluate performance metrics such as mean queue length, throughput, mean waiting time, utilization, and mean traversal time. The model is aware of the arrival of concurrent applications with an Erlang distribution. Simulation results regarding performance metrics are investigated. Results point out that in Cloud systems both the fairness and load balancing are to be significantly considered.

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Energy-Aware Adaptive Four Thresholds Technique for Optimal Virtual Machine Placement

Cited by 3 publications

References 17 publications

Bin packing algorithms for virtual machine placement in cloud computing: a review

Bin packing algorithms for virtual machine placement in cloud computing: a review

Performance degradation assessment and VM placement policy in cloud

Evaluation of load balancing approaches for Erlang concurrent application in cloud systems

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