Prediction-based resource allocation model for real time tasks

High performance computing (HPC) platforms provides computing, storage and communication facilities to process real-time applications efficiently. Such applications produce less important results if the deadlines are missed. Most of the real-time algorithms decently schedule applications tasks offli...

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Main Authors:	Qureshi, Muhammad Shuaib, Qureshi, Muhammad Bilal, Raza, Ali, Ul Qayyum, Noor, Shah, Asadullah
Format:	Conference or Workshop Item
Language:	English English English
Published:	Institute of Electrical and Electronics Engineers Inc. 2018
Subjects:	T10.5 Communication of technical information
Online Access:	http://irep.iium.edu.my/68563/ http://irep.iium.edu.my/68563/ http://irep.iium.edu.my/68563/ http://irep.iium.edu.my/68563/7/68563_Prediction-based%20Resource%20Allocation%20Model_complete.pdf http://irep.iium.edu.my/68563/13/68563_Prediction-based%20resource%20allocation%20model_SCOPUS.pdf http://irep.iium.edu.my/68563/14/68563_Prediction-based%20resource%20allocation%20model_WOS.pdf

id	iium-68563
recordtype	eprints
spelling	iium-685632019-06-13T01:51:01Z http://irep.iium.edu.my/68563/ Prediction-based resource allocation model for real time tasks Qureshi, Muhammad Shuaib Qureshi, Muhammad Bilal Raza, Ali Ul Qayyum, Noor Shah, Asadullah T10.5 Communication of technical information High performance computing (HPC) platforms provides computing, storage and communication facilities to process real-time applications efficiently. Such applications produce less important results if the deadlines are missed. Most of the real-time algorithms decently schedule applications tasks offline, but they usually take longer in processing which results in deadlines miss when tasks need some data from remote storage locations. In this paper, we propose a prediction-based model which analyze task feasibility before scheduling on the HPC resources when tasks have data-intensive constraints. The main advantage of the prediction analysis modules is to save time by refraining further analysis on non-scheduled tasks. The model helps in searching suitable resources and improved resource utilization by considering task workload in advance. Institute of Electrical and Electronics Engineers Inc. 2018-11 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/68563/7/68563_Prediction-based%20Resource%20Allocation%20Model_complete.pdf application/pdf en http://irep.iium.edu.my/68563/13/68563_Prediction-based%20resource%20allocation%20model_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/68563/14/68563_Prediction-based%20resource%20allocation%20model_WOS.pdf Qureshi, Muhammad Shuaib and Qureshi, Muhammad Bilal and Raza, Ali and Ul Qayyum, Noor and Shah, Asadullah (2018) Prediction-based resource allocation model for real time tasks. In: 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS), 22nd-23rd November 2018, Bangkok, Thailand. (Unpublished) https://icetas.etssm.org 10.1109/ICETAS.2018.8629169
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	T10.5 Communication of technical information
spellingShingle	T10.5 Communication of technical information Qureshi, Muhammad Shuaib Qureshi, Muhammad Bilal Raza, Ali Ul Qayyum, Noor Shah, Asadullah Prediction-based resource allocation model for real time tasks
description	High performance computing (HPC) platforms provides computing, storage and communication facilities to process real-time applications efficiently. Such applications produce less important results if the deadlines are missed. Most of the real-time algorithms decently schedule applications tasks offline, but they usually take longer in processing which results in deadlines miss when tasks need some data from remote storage locations. In this paper, we propose a prediction-based model which analyze task feasibility before scheduling on the HPC resources when tasks have data-intensive constraints. The main advantage of the prediction analysis modules is to save time by refraining further analysis on non-scheduled tasks. The model helps in searching suitable resources and improved resource utilization by considering task workload in advance.
format	Conference or Workshop Item
author	Qureshi, Muhammad Shuaib Qureshi, Muhammad Bilal Raza, Ali Ul Qayyum, Noor Shah, Asadullah
author_facet	Qureshi, Muhammad Shuaib Qureshi, Muhammad Bilal Raza, Ali Ul Qayyum, Noor Shah, Asadullah
author_sort	Qureshi, Muhammad Shuaib
title	Prediction-based resource allocation model for real time tasks
title_short	Prediction-based resource allocation model for real time tasks
title_full	Prediction-based resource allocation model for real time tasks
title_fullStr	Prediction-based resource allocation model for real time tasks
title_full_unstemmed	Prediction-based resource allocation model for real time tasks
title_sort	prediction-based resource allocation model for real time tasks
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2018
url	http://irep.iium.edu.my/68563/ http://irep.iium.edu.my/68563/ http://irep.iium.edu.my/68563/ http://irep.iium.edu.my/68563/7/68563_Prediction-based%20Resource%20Allocation%20Model_complete.pdf http://irep.iium.edu.my/68563/13/68563_Prediction-based%20resource%20allocation%20model_SCOPUS.pdf http://irep.iium.edu.my/68563/14/68563_Prediction-based%20resource%20allocation%20model_WOS.pdf
first_indexed	2023-09-18T21:37:18Z
last_indexed	2023-09-18T21:37:18Z
_version_	1777412906902618112

Prediction-based resource allocation model for real time tasks

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