eprintid: 6485
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/64/85
datestamp: 2023-11-09 16:18:16
lastmod: 2023-11-09 16:18:16
status_changed: 2023-11-09 16:06:52
type: conference_item
metadata_visibility: show
creators_name: Rubab, S.
creators_name: Hassan, M.F.
creators_name: Mahmood, A.K.
creators_name: Shah, S.N.M.
title: Random Forest Forecast (RFF): One hour ahead jobs in volunteer grid
ispublished: pub
keywords: Decision trees; Information science, Computational grids; Forecasting modeling; Large Hadron Collider; Random forests; Regression; Resource availability; Short-term forecasting; Volunteer grid, Forecasting
note: cited By 0; Conference of 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 ; Conference Date: 15 August 2016 Through 17 August 2016; Conference Code:125433
abstract: 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.
date: 2016
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010422732&doi=10.1109%2fICCOINS.2016.7783223&partnerID=40&md5=a4989bd5cc36c47cb86f01d09a3584c0
id_number: 10.1109/ICCOINS.2016.7783223
full_text_status: none
publication: 2016 3rd International Conference on Computer and Information Sciences, ICCOINS 2016 - Proceedings
pagerange: 253-258
refereed: TRUE
isbn: 9781509051342
citation:   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.