Random Forest Forecast (RFF): One hour ahead jobs in volunteer grid

Short term forecasting is significant operation to forecast the future jobs for computational grids as it can provide a solution for inconsistent resource availability and feasible job scheduling. A job forecasting model is presented to forecast one hour ahead of jobs submitted for computations usin...

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Main Authors: Rubab, S., Hassan, M.F., Mahmood, A.K., Shah, S.N.M.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.30489 /
Published: Institute of Electrical and Electronics Engineers Inc. 2016
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010422732&doi=10.1109%2fICCOINS.2016.7783223&partnerID=40&md5=a4989bd5cc36c47cb86f01d09a3584c0
http://eprints.utp.edu.my/30489/
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spelling utp-eprints.304892022-03-25T06:55:55Z Random Forest Forecast (RFF): One hour ahead jobs in volunteer grid Rubab, S. Hassan, M.F. Mahmood, A.K. Shah, S.N.M. Short term forecasting is significant operation to forecast the future jobs for computational grids as it can provide a solution for inconsistent resource availability and feasible job scheduling. A job forecasting model is presented to forecast one hour ahead of jobs submitted for computations using regression random forests. The training data constitutes the information about the type of job and jobs submitted on average each hour. The forecast model is built on the basis of training process. A real job data set from LCG (Large Hadron Collider Computing Grid) is used for evaluating the proposed forecast model, while considering the fact that jobs submitted are inconsistent. Findings provide a proof that by using proposed method the forecast error can be reduced and the effectiveness of job forecast can be improved for long test periods. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2016 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010422732&doi=10.1109%2fICCOINS.2016.7783223&partnerID=40&md5=a4989bd5cc36c47cb86f01d09a3584c0 Rubab, S. and Hassan, M.F. and Mahmood, A.K. and Shah, S.N.M. (2016) Random Forest Forecast (RFF): One hour ahead jobs in volunteer grid. In: UNSPECIFIED. http://eprints.utp.edu.my/30489/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Short term forecasting is significant operation to forecast the future jobs for computational grids as it can provide a solution for inconsistent resource availability and feasible job scheduling. A job forecasting model is presented to forecast one hour ahead of jobs submitted for computations using regression random forests. The training data constitutes the information about the type of job and jobs submitted on average each hour. The forecast model is built on the basis of training process. A real job data set from LCG (Large Hadron Collider Computing Grid) is used for evaluating the proposed forecast model, while considering the fact that jobs submitted are inconsistent. Findings provide a proof that by using proposed method the forecast error can be reduced and the effectiveness of job forecast can be improved for long test periods. © 2016 IEEE.
format Conference or Workshop Item
author Rubab, S.
Hassan, M.F.
Mahmood, A.K.
Shah, S.N.M.
spellingShingle Rubab, S.
Hassan, M.F.
Mahmood, A.K.
Shah, S.N.M.
Random Forest Forecast (RFF): One hour ahead jobs in volunteer grid
author_sort Rubab, S.
title Random Forest Forecast (RFF): One hour ahead jobs in volunteer grid
title_short Random Forest Forecast (RFF): One hour ahead jobs in volunteer grid
title_full Random Forest Forecast (RFF): One hour ahead jobs in volunteer grid
title_fullStr Random Forest Forecast (RFF): One hour ahead jobs in volunteer grid
title_full_unstemmed Random Forest Forecast (RFF): One hour ahead jobs in volunteer grid
title_sort random forest forecast (rff): one hour ahead jobs in volunteer grid
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010422732&doi=10.1109%2fICCOINS.2016.7783223&partnerID=40&md5=a4989bd5cc36c47cb86f01d09a3584c0
http://eprints.utp.edu.my/30489/
_version_ 1741197415412662272
score 11.62408

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