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...
| Main Authors: | , , , , |
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| Format: | Proceeding Paper |
| Language: | English English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2018
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| Online Access: | 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 |
| _version_ | 1848786964288897024 |
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| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T17:17:24Z |
| format | Proceeding Paper |
| id | iium-68563 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-14T17:17:24Z |
| publishDate | 2018 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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 Proceeding Paper 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 |
| 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 |
| title | 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_short | Prediction-based resource allocation model for real time tasks |
| title_sort | prediction-based resource allocation model for real time tasks |
| topic | T10.5 Communication of technical information |
| 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 |