Predicting Host CPU Utilization in Cloud Computing using Recurrent Neural Networks

James Duggan

Predicting Host CPU Utilization in Cloud Computing using Recurrent Neural Networks

James Duggan

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

One of the major challenges facing cloud computing is to accurately predict future resource usage for future demands. Cloud resource consumption is constantly changing, which makes it difficult for forecasting algorithms to produce accurate predictions. This motivates the research presented in this paper which aims to predict host machines CPU consumption for a single time-step and multiple time-steps into the future. This research implements a Recurrent Neural Network to predict CPU utilisation, due to their ability to retain information and accurately make predictions for time series problems, making it a promising candidate to predict CPU utilization with greater accuracy when compared to traditional approaches.

Original language	English (Ireland)
Title of host publication	2017 12TH INTERNATIONAL CONFERENCE FOR INTERNET TECHNOLOGY AND SECURED TRANSACTIONS (ICITST)
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	2164-7046
ISBN (Print)	2164-7046
Publication status	Published - 1 Jan 2017

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Duggan, M;Mason, K;Duggan, J;Howley, E;Barrett, E

Cite this

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title = "Predicting Host CPU Utilization in Cloud Computing using Recurrent Neural Networks",

abstract = "One of the major challenges facing cloud computing is to accurately predict future resource usage for future demands. Cloud resource consumption is constantly changing, which makes it difficult for forecasting algorithms to produce accurate predictions. This motivates the research presented in this paper which aims to predict host machines CPU consumption for a single time-step and multiple time-steps into the future. This research implements a Recurrent Neural Network to predict CPU utilisation, due to their ability to retain information and accurately make predictions for time series problems, making it a promising candidate to predict CPU utilization with greater accuracy when compared to traditional approaches.",

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year = "2017",

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N2 - One of the major challenges facing cloud computing is to accurately predict future resource usage for future demands. Cloud resource consumption is constantly changing, which makes it difficult for forecasting algorithms to produce accurate predictions. This motivates the research presented in this paper which aims to predict host machines CPU consumption for a single time-step and multiple time-steps into the future. This research implements a Recurrent Neural Network to predict CPU utilisation, due to their ability to retain information and accurately make predictions for time series problems, making it a promising candidate to predict CPU utilization with greater accuracy when compared to traditional approaches.

AB - One of the major challenges facing cloud computing is to accurately predict future resource usage for future demands. Cloud resource consumption is constantly changing, which makes it difficult for forecasting algorithms to produce accurate predictions. This motivates the research presented in this paper which aims to predict host machines CPU consumption for a single time-step and multiple time-steps into the future. This research implements a Recurrent Neural Network to predict CPU utilisation, due to their ability to retain information and accurately make predictions for time series problems, making it a promising candidate to predict CPU utilization with greater accuracy when compared to traditional approaches.

M3 - Conference Publication

SN - 2164-7046

BT - 2017 12TH INTERNATIONAL CONFERENCE FOR INTERNET TECHNOLOGY AND SECURED TRANSACTIONS (ICITST)

PB - IEEE

ER -

Predicting Host CPU Utilization in Cloud Computing using Recurrent Neural Networks

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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Cite this