Prediction Based Energy Efficient Virtual Machine Consolidation in Cloud Computing

Gondhi, Naveen Kumar; Kailu, Paras

doi:10.1109/icacce.2015.148

Cited by 10 publications

(5 citation statements)

References 10 publications

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“…Numerous studies have been carried out on the prediction in cloud computing according to various objectives of the research: Prediction of the servers' load [6], [7], [8], [9], [10], prediction of the VMs' load [11], [12], prediction of the VM use [13], [14], prediction of the host use [15].…”

Section: Related Workmentioning

confidence: 99%

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CPU-based Prediction with Self Organizing Map in Dynamic Cloud Data Centers

Djennane

Yacoub²,

Aoudjit

et al. 2021

SWCC

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Backgroud: The major objective of resource management systems in the cloud environments is to assist providers in making consistent and cost-effective decisions related to the dynamic resource allocation. However, because of the demand changes of the applications and the exponential evolution of the cloud, the resource management systems are constantly called into question with regard to their ability to guarantee an effective resource provisioning. Objective: To tackle these challenges, the future demand prediction is a practical solution that has been adopted in the literature. The prediction has widely relied on the CPU utilization since it is considered as a leading cause of the Quality of Service dropping. Method: The successful application of artificial intelligence techniques in forecasting problems motivated us to use the Kohonen Self Organizing Maps that tries to capture the gathered empirical CPU load time series in regular behaviors to perform an accurate forecast. The proposed solution is a two-step approach that first classifies the collected data and then predicts the future CPU load. Results and conclusion: The experimental results show that our proposed system outperforms other models reported in the literature. In addition, we proved that Self Organizing Maps known for its strength in classification is also effective for prediction.

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Section: Related Workmentioning

confidence: 99%

“…Cloud data centers contain hundreds of thousands of servers, which host millions of VMs of different sizes, types and applications. Hence, since server resources are strongly influenced by the VMs they host, it makes more sense to focus on VMs resource management rather than server management as in [6] [7] [8].…”

Section: Related Workmentioning

confidence: 99%

CPU-based Prediction with Self Organizing Map in Dynamic Cloud Data Centers

Djennane

Yacoub²,

Aoudjit

et al. 2021

SWCC

View full text Add to dashboard Cite

show abstract

“…Many studies have been conducted on various predictions in cloud computing. From the perspective of research objectives, some researchers have studied server load prediction [ [6][7][8][9][10], VM load prediction [11,12], VM utilization prediction [13,14], host utilization prediction [15], web application workload prediction [16], cloud service workload prediction [17][18][19], workflow workload prediction [20], service quality prediction [21], and workload characterization [22][23][24]. Toumi et al [6] described a server load according to the submitted task types and the submission rate and applied a stream mining technique to predict server loads.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

Jing

Wang

2019

Journal of Electrical and Computer Engineering

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Dynamic resource scheduling is a critical activity to guarantee quality of service (QoS) in cloud computing. One challenging problem is how to predict future host utilization in real time. By predicting future host utilization, a cloud data center can place virtual machines to suitable hosts or migrate virtual machines in advance from overloaded or underloaded hosts to guarantee QoS or save energy. However, it is very difficult to accurately predict host utilization in a timely manner because host utilization varies very quickly and exhibits strong instability with many bursts. Although machine learning methods can accurately predict host utilization, it usually takes too much time to ensure rapid resource allocation and scheduling. In this paper, we propose a hybrid method, EEMD-RT-ARIMA, for short-term host utilization prediction based on ensemble empirical mode decomposition (EEMD), runs test (RT), and autoregressive integrated moving average (ARIMA). First, the EEMD method is used to decompose the nonstationary host utilization sequence into relatively stable intrinsic mode function (IMF) components and a residual component to improve prediction accuracy. Then, efficient IMF components are selected and then reconstructed into three new components to reduce the prediction time and error accumulation due to too many IMF components. Finally, the overall prediction results are obtained by superposing the prediction results of three new components, each of which is predicted by the ARIMA method. An experiment is conducted on real host utilization traces from a cloud platform. We compare our method with the ARIMA model and the EEMD-ARIMA method in terms of error, effectiveness, and time-cost analysis. The results show that our method is a cost-effective method and is more suitable for short-term host utilization prediction in cloud computing.

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“…Server has huge electrical power utilization and accordingly the expense of activity and support, has turned into this issue in cloud computing. Prediction based quicker power productive VM solidification conspire his outcome is quicker VM combination enhance QoS and execution instant diminishing power utilization [11]. A stochastic writing computer programs is figured by incorporating the imperatives related with load allocation, bills/selling, battery the executives, backup generators, and power adjusting.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Consolidation of VM Allocation and VM Migration to Optimize Energy Consumption of Cloud Data Centers

Kumar¹,

Kumar²

2019

IJEAT

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Including the expanding fame of the cloud model and quick multiplication of cloud frameworks there are expanding concerns about energy utilization and subsequent effect of cloud as a supporter of worldwide CO2 discharges. Until now, little is thought about how to fuse energy utilization and CO2 worries into cloud application. Energy consumption has become an important cost factor for computing resources. In this research article, we proposed an algorithm to VM Allocation and VM migrations in the context of power utilisation in the data centers. This mechanism is to minimize the energy utilization in the cloud computing environment. We validate our results with the help of prediction based faster energy efficient VMs approach and modified Best Fit approach which shows the faster assignments and increase the performance when consumption of the energy is optimised. As well as we also simulate our results in the cloudsim in the multiple numbers of host and virtual machine to reduce the energy consumptions.

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Prediction Based Energy Efficient Virtual Machine Consolidation in Cloud Computing

Cited by 10 publications

References 10 publications

CPU-based Prediction with Self Organizing Map in Dynamic Cloud Data Centers

CPU-based Prediction with Self Organizing Map in Dynamic Cloud Data Centers

A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

Dynamic Consolidation of VM Allocation and VM Migration to Optimize Energy Consumption of Cloud Data Centers

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