Design and implementation of adaptive power-aware virtual machine provisioner (APA-VMP) using swarm intelligence

Jeyarani, R.; Nagaveni, N.; Ram, Raju

doi:10.1016/j.future.2011.06.002

Cited by 72 publications

(29 citation statements)

References 13 publications

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“…In this Paper, With 10 or more data values, the dynamic lower threshold is calculated according to (2); otherwise, the lower threshold assumes a constant value of 0.25 (Step 6, Phase 2).…”

Section: B Phase 2: Identification Of Underloaded Hostsmentioning

confidence: 99%

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Optimization of Dynamic Virtual Machine Consolidation in Cloud Computing Data Centers

Najari¹,

Alavi²,

Noorimehr³

2016

ijacsa

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Abstract-The present study aims at recognizing the problem of dynamic virtual machine (VM) Consolidation using virtualization, live migration of VMs from underloaded and overloaded hosts and switching idle nodes to the sleep mode as a very effective approach for utilizing resources and accessing energy efficient cloud computing data centres. The challenge in the present study is to reduce energy consumption thus guarantee Service Level Agreement (SLA) at its highest level.

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“…In this Paper, With 10 or more data values, the dynamic lower threshold is calculated according to (2); otherwise, the lower threshold assumes a constant value of 0.25 (Step 6, Phase 2).…”

Section: B Phase 2: Identification Of Underloaded Hostsmentioning

confidence: 99%

“…Infrastructure as a Service (IaaS) is among the services provided by cloud computing that offers processing resources to users as services. The clients rent the equipment from infrastructure providers as a service and only pay for the amount of service they really consume [2,3].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Dynamic Virtual Machine Consolidation in Cloud Computing Data Centers

Najari¹,

Alavi²,

Noorimehr³

2016

ijacsa

View full text Add to dashboard Cite

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“…Finally, the works in [26,22] address the problem of job scheduling in Clouds while reducing energy consumption, which is a crucial problem [28] mainly because the environmental impact in terms of carbon dioxide (CO2) emissions caused by high energy consumption. [26] focuses however only on achieving competitive makespan.…”

Section: Related Workmentioning

confidence: 99%

“…[26] focuses however only on achieving competitive makespan. On the other hand, in [22] a new scheduling policy that models and manages a virtualized datacenter is proposed.…”

Section: Related Workmentioning

confidence: 99%

Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)

Pacini

Mateos

Garino

2015

Advances in Engineering Software

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“…This model provides them for applicants via available connection infrastructure such as network and the Internet in an easy, rapid and on-demand manner, while taking into account quality of service. IaaS, PaaS, and SaaS are the three major types of cloud computing services [1,2]. IaaS presents data centre infrastructures, servers and storage spaces as well as hardware policies independently from location and geographical limitations and under computer networks.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Energy Efficient Dynamic Virtual Machine Consolidation in Cloud Environment

Nikzad¹,

Alavi²,

Soltanaghaei³

2016

ijacsa

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Abstract-Nowadays, as the use of cloud computing service becomes more extensive and the customers welcome this service, an increasing trend in energy consumption and operational costs of these centers may be seen. To reduce operational costs, the providers should decrease energy consumption to an extent that Service Level Agreement (SLA) maintains at a desirable level. This paper adopts the virtual machine consolidation problem in cloud computing data centers as a solution to achieve this goal, putting forward solutions to make the decision regarding the necessity of migration from hosts and finding appropriate hosts as destinations of migration. Using time-series forecasting method and Double Exponential Smoothing (DES) technique, the proposed algorithm predicts CPU utilization in near future. It also proposes an optimal equation for the dynamic lower threshold. Comparing current and predicted CPU utilization with dynamic upper and lower thresholds, this algorithm identifies and categorizes underloaded and overloaded hosts. According to this categorization, migration then occurs from the hosts that meet the necessary conditions for migration. This paper identifies a certain type of hosts as "troublemaker hosts". Most probably, the process of prediction and decision making regarding the necessity of migration will be disrupted in the case of these hosts. Upon encountering this type of hosts, the algorithm adopts policies to modify them or switch them to sleep mode, thereby preventing the adverse effects caused by their existence. The researchers excluded all overloaded, prone-to-beoverloaded, underloaded, and prone-to-be-underloaded hosts from the list of suitable hosts to find suitable hosts as destinations of migration. An average improvement of 86.2%, 28.4%, and 87.2% respectively for the number of migrations of virtual machines, energy consumption, and SLA violation is among the simulation achievements of this algorithm using Clouds tool.

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Design and implementation of adaptive power-aware virtual machine provisioner (APA-VMP) using swarm intelligence

Cited by 72 publications

References 13 publications

Optimization of Dynamic Virtual Machine Consolidation in Cloud Computing Data Centers

Optimization of Dynamic Virtual Machine Consolidation in Cloud Computing Data Centers

Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)

An Approach for Energy Efficient Dynamic Virtual Machine Consolidation in Cloud Environment

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