A Study on Benefits and Classification of Load Balancing in Cloud Computing Environment

Saxena, Akash; Sharma, Navneet; Goyal, Jayanti; Saxena, Shweta

doi:10.2139/ssrn.3166545

Cited by 1 publication

(3 citation statements)

References 5 publications

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“…Substitute the calculated parameters α and u into Eq. ( 7), and solve the equation, and take x (1) (0) = x (0) (1). Then the grey forecasting model is obtained:…”

Section: Forecasting Model a Single Forecasting Model 1) Grey Forecasting Modelmentioning

confidence: 99%

“…According to Eq. ( 9), the simulated values of successively accumulated valuesx (1) (t + 1) are obtained, and then the real forecasted values are obtained by one more subtraction:…”

Section: Forecasting Model a Single Forecasting Model 1) Grey Forecasting Modelmentioning

confidence: 99%

“…Cloud computing is a blend of conveyed, parallel, multitenant computing model established on various advancements, for example, virtualization, network, benefit and autonomic computing. Cloud computing innovation enables clients to get solid computing and memory assets and in the meantime, the user isn't keen on the area and settings of these assets [1]. With the development of cloud computing, the processing is transferred to the cloud means that data centers and transmission networks need more capacity, which brings new resource integration and usage patterns [2].…”

Section: Introductionmentioning

confidence: 99%

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Combined Forecasting Model of Cloud Computing Resource Load for Energy-Efficient IoT System

Zhang

et al. 2019

IEEE Access

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Cloud computing is generally considered as a special energy-efficient form for the Internet of Things (IoT) resource usage. Dedicated server systems for cloud services, better capacity utilization and economies of scale because of the use of larger and more energy-efficient data centers are the reasons why cloud solutions typically use less energy than traditional on-premise systems. To scientifically and rationally configure the hardware and software resources of the cloud computing, the research on forecasting a cloud computing resource load becomes a research focus. However, the widely-used single forecasting model cannot contain all the characteristics of the cloud computing resource load sequence, resulting in inaccurate forecasting results. In this paper, a combined forecasting approach of cloud computing resource load based on wavelet decomposition is proposed, which combined the grey model and cubic exponential smoothing model. It can well preserve details and reduce noise. Firstly, the cloud computing resource load sequence is decomposed into several frequencies by the wavelet decomposition method. The decomposed load sequences with different characteristics are divided into different resolution scale subspaces in deferent frequencies. The noise of the load sequences is reduced by the wavelet threshold denoising method. And then, the load sequences are reconstructed according to the wavelet coefficients. The reconstructed load sequence not only contains less noise but also reserves detailed information. Consequently, it is closer to the real data and more regular. Experimental results show that our proposed combined forecasting model with wavelet decomposition can provide more accurate forecasting results than each single forecasting model or the combined forecasting model without using the wavelet decomposition method. Thus, our proposal is demonstrated to be efficient for forecasting the cloud computing resource load and helping to reduce energy consumption.INDEX TERMS Cloud computing resource load, wavelet decomposition, combined forecasting model, grey model, cubic exponential smoothing model.

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“…Substitute the calculated parameters α and u into Eq. ( 7), and solve the equation, and take x (1) (0) = x (0) (1). Then the grey forecasting model is obtained:…”

Section: Forecasting Model a Single Forecasting Model 1) Grey Forecasting Modelmentioning

confidence: 99%

“…According to Eq. ( 9), the simulated values of successively accumulated valuesx (1) (t + 1) are obtained, and then the real forecasted values are obtained by one more subtraction:…”

Section: Forecasting Model a Single Forecasting Model 1) Grey Forecasting Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Combined Forecasting Model of Cloud Computing Resource Load for Energy-Efficient IoT System

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

A Study on Benefits and Classification of Load Balancing in Cloud Computing Environment

Cited by 1 publication

References 5 publications

Combined Forecasting Model of Cloud Computing Resource Load for Energy-Efficient IoT System

Combined Forecasting Model of Cloud Computing Resource Load for Energy-Efficient IoT System

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