Using Multiple Seasonal Holt-Winters Exponential Smoothing to Predict Cloud Resource Provisioning

Shahin, Ashraf A.

doi:10.14569/ijacsa.2016.071113

Cited by 10 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The findings demonstrate that the suggested algorithm is better competing techniques. Yet, in some circumstances, gaps among events might vary and must be taken into account when making predictions [17].…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

SGA Model for Prediction in Cloud Environment

Krishnan¹,

Prasanthi

2023

IJRITCC

View full text Add to dashboard Cite

With virtual information, cloud computing has made applications available to users everywhere. Efficient asset workload forecasting could help the cloud achieve maximum resource utilisation. The effective utilization of resources and the reduction of datacentres power both depend heavily on load forecasting. The allocation of resources and task scheduling issues in clouds and virtualized systems are significantly impacted by CPU utilisation forecast. A resource manager uses utilisation projection to distribute workload between physical nodes, improving resource consumption effectiveness. When performing a virtual machine distribution job, a good estimation of CPU utilization enables the migration of one or more virtual servers, preventing the overflow of the real machineries. In a cloud system, scalability and flexibility are crucial characteristics. Predicting workload and demands would aid in optimal resource utilisation in a cloud setting. To improve allocation of resources and the effectiveness of the cloud service, workload assessment and future workload forecasting could be performed. The creation of an appropriate statistical method has begun. In this study, a simulation approach and a genetic algorithm were used to forecast workloads. In comparison to the earlier techniques, it is anticipated to produce results that are superior by having a lower error rate and higher forecasting reliability. The suggested method is examined utilizing statistics from the Bit brains datacentres. The study then analyses, summarises, and suggests future study paths in cloud environments.

show abstract

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

SGA Model for Prediction in Cloud Environment

Krishnan¹,

Prasanthi

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…Therefore, the Holt-Winters model was utilized to forecast the future in various researches. For instance, a prediction of Bayesian depended on the additive Holt-Winters model [18]; an investigation of the rainfall pattern in Langat River Basin, Malaysia with the time series within more than 25 years was chosen to discover the future [19]; a forecasting of revenue of Bangabandhu Multipurpose Bridge was computed when focusing on the monthly time series data [20]; a research of future cloud resource provisioning was employed by the algorithm of the Holt-Winters exponential smoothing method to model clod workload with multi-seasonal cycles [21]; and a study of the amount of income at the Department of Transportation Yoyakarta was estimated by the Holt-Winters model and confirmed by the parameters and MAPE indicator [22]. With these characteristics, the Holt-Winters model is a high accuracy forecasting tool with trend and seasonality when observing the long time series.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring Efficiency of Generating Electric Processes

2018

View full text Add to dashboard Cite

Electric energy sources are the foundation for supporting for the industrialization and modernization; however, the processes of electricity generation increase CO 2 emissions. This study integrates the Holt-Winters model in number cruncher statistical system (NCSS) to estimate the forecasting data and the undesirable model in data envelopment analysis (DEA) to calculate the efficiency of electricity production in 14 countries all over the world from past to future. The Holt-Winters model is utilized to estimate the future; then, the actual and forecasting data are applied into the undesirable model to compute the performance. From the principle of an undesirable model, the study determines the input and output factors as follows nonrenewable and renewable fuels (inputs), electricity generation (desirable output), and CO 2 emissions (undesirable output). The empirical results exhibit efficient/inefficient terms over the period from 2011-2021 while converting these fuels into electricity energy and CO 2 emissions. The efficiency reveals the environmental effect level from the electricity generation. The analysis scores recommend a direction for improving the inefficient terms via the principle of inputs and undesirable outputs excess and desirable outputs shortfalls in an undesirable model.

show abstract

“…This model is one of the best techniques for time series data that has both seasonal and trend components. Some of its recent applications were found in the study of cloud resource provisioning (Shahin, 2016) and to forecast short-term electricity demand in England and Wales (Ruiter, 2017) as well as in Ireland (Kavanagh, 2017). The seasonal Holt-Winter's model was also applied in the study to forecast natural rubber production in India (Arumugam and Anithakumari, 2013).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Univariate Time Series Modelling for Natural Rubber Production in Malaysia

Mah¹,

Buhary²,

Abdullah³

et al. 2018

MJoC

View full text Add to dashboard Cite

Malaysia is one of the top countries that produces natural rubber and was ranked sixth place globally. The earnings from natural rubber products are making billions of ringgit for the country. However, over the past years the natural rubber production in Malaysia has been inconsistent and the deficiencies in the production can affect Malaysia’s economy. Therefore, it is important for relevant agencies and departments to understand the patterns and trends of natural rubber production in Malaysia besides having the ability to forecast. Hence, the integrated autoregressive moving average (ARIMA), seasonal autoregressive moving average (SARIMA) and the seasonal Holt-Winter’s model were being considered for the purpose of modelling and forecasting this study. The forecast accuracy criteria used to evaluate the performance of the models are the root mean square error (RMSE) and mean absolute percentage error (MAPE). The results showed that the seasonal Holt-Winter’s model appeared to be the best model as it yielded the lowest RMSE and MAPE values. The seasonal Holt-Winter’s model, however, is not a good choice of model as it was unable to forecast six months ahead values. On the other hand, the SARIMA model had a better forecast ability when forecasting the values for the same duration. Therefore, the SARIMA model is taken to be the model in forecasting the natural rubber production in Malaysia for that period. This study has shown that the best fit model that fulfil all the forecast accuracy criteria may not have the best forecast ability.

show abstract

Using Multiple Seasonal Holt-Winters Exponential Smoothing to Predict Cloud Resource Provisioning

Cited by 10 publications

References 14 publications

SGA Model for Prediction in Cloud Environment

SGA Model for Prediction in Cloud Environment

Measuring Efficiency of Generating Electric Processes

A Comparative Study of Univariate Time Series Modelling for Natural Rubber Production in Malaysia

Contact Info

Product

Resources

About