Heterogeneity-aware task allocation in mobile ad hoc cloud / Ibrar Yaqoob

Mobile Ad Hoc Cloud (MAC) enables the use of a multitude of proximate resource-rich mobile devices to provide computational services in the vicinity. MAC is a candidate blueprint for future compute-intensive applications with the aim of delivering high functionalities and a rich experience to mobile...

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Main Author: Ibrar, Yaqoob
Format: Thesis
Published: 2017
Subjects:
Online Access:http://studentsrepo.um.edu.my/7389/
http://studentsrepo.um.edu.my/7389/1/All.pdf
http://studentsrepo.um.edu.my/7389/9/ibrar.pdf
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author Ibrar, Yaqoob
author_facet Ibrar, Yaqoob
author_sort Ibrar, Yaqoob
building UM Research Repository
collection Online Access
description Mobile Ad Hoc Cloud (MAC) enables the use of a multitude of proximate resource-rich mobile devices to provide computational services in the vicinity. MAC is a candidate blueprint for future compute-intensive applications with the aim of delivering high functionalities and a rich experience to mobile users. However, inattention to mobile device resources and operational heterogeneity-measuring parameters, such as CPU speed, number of cores, and workload, when allocating task in MAC, causes inefficient resource utilization that prolongs task execution time and consumes large amounts of energy. Task execution is remarkably degraded because the longer execution time and high energy consumption impede the optimum use of MAC. In this study, we minimize execution time and energy consumption by proposing heterogeneity-aware task allocation solutions for MAC-based compute-intensive tasks. Results reveal that incorporation of the heterogeneity-measuring parameters guarantees a shorter execution time and reduces the energy consumption of the compute-intensive tasks in MAC. We develop a mathematical model to validate the proposed solutions’ empirical results. In comparison with random-based task allocation (RM), the proposed five solutions based on CPU speed (SO), number of cores (CO), workload (WO), CPU speed and workload (SW), and CPU speed, core, and workload (SCW) reduce execution time up to 56.72%, 53.12%, 56.97%, 61.23%, and 71.55%, respectively. In addition, these heterogeneity-aware task allocation solutions save energy up to 69.78%, 69.06%, 68.25%, 67.26%, and 57.33%, respectively. Furthermore, we apply Mann-Whitney U test and Vargha and Delaney’s A12 statistics to find the significance of differences between the results. Our findings from both tests reveal that the proposed solutions have significant statistical and practical differences compared to RM-based solution. For this reason, the proposed solutions significantly improve tasks’ execution performance, which can increase the optimum use of MAC.
first_indexed 2025-11-14T13:41:29Z
format Thesis
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institution University Malaya
institution_category Local University
last_indexed 2025-11-14T13:41:29Z
publishDate 2017
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spelling um-73892020-09-28T23:02:35Z Heterogeneity-aware task allocation in mobile ad hoc cloud / Ibrar Yaqoob Ibrar, Yaqoob QA75 Electronic computers. Computer science Mobile Ad Hoc Cloud (MAC) enables the use of a multitude of proximate resource-rich mobile devices to provide computational services in the vicinity. MAC is a candidate blueprint for future compute-intensive applications with the aim of delivering high functionalities and a rich experience to mobile users. However, inattention to mobile device resources and operational heterogeneity-measuring parameters, such as CPU speed, number of cores, and workload, when allocating task in MAC, causes inefficient resource utilization that prolongs task execution time and consumes large amounts of energy. Task execution is remarkably degraded because the longer execution time and high energy consumption impede the optimum use of MAC. In this study, we minimize execution time and energy consumption by proposing heterogeneity-aware task allocation solutions for MAC-based compute-intensive tasks. Results reveal that incorporation of the heterogeneity-measuring parameters guarantees a shorter execution time and reduces the energy consumption of the compute-intensive tasks in MAC. We develop a mathematical model to validate the proposed solutions’ empirical results. In comparison with random-based task allocation (RM), the proposed five solutions based on CPU speed (SO), number of cores (CO), workload (WO), CPU speed and workload (SW), and CPU speed, core, and workload (SCW) reduce execution time up to 56.72%, 53.12%, 56.97%, 61.23%, and 71.55%, respectively. In addition, these heterogeneity-aware task allocation solutions save energy up to 69.78%, 69.06%, 68.25%, 67.26%, and 57.33%, respectively. Furthermore, we apply Mann-Whitney U test and Vargha and Delaney’s A12 statistics to find the significance of differences between the results. Our findings from both tests reveal that the proposed solutions have significant statistical and practical differences compared to RM-based solution. For this reason, the proposed solutions significantly improve tasks’ execution performance, which can increase the optimum use of MAC. 2017 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/7389/1/All.pdf application/pdf http://studentsrepo.um.edu.my/7389/9/ibrar.pdf Ibrar, Yaqoob (2017) Heterogeneity-aware task allocation in mobile ad hoc cloud / Ibrar Yaqoob. PhD thesis, University of Malaya. http://studentsrepo.um.edu.my/7389/
spellingShingle QA75 Electronic computers. Computer science
Ibrar, Yaqoob
Heterogeneity-aware task allocation in mobile ad hoc cloud / Ibrar Yaqoob
title Heterogeneity-aware task allocation in mobile ad hoc cloud / Ibrar Yaqoob
title_full Heterogeneity-aware task allocation in mobile ad hoc cloud / Ibrar Yaqoob
title_fullStr Heterogeneity-aware task allocation in mobile ad hoc cloud / Ibrar Yaqoob
title_full_unstemmed Heterogeneity-aware task allocation in mobile ad hoc cloud / Ibrar Yaqoob
title_short Heterogeneity-aware task allocation in mobile ad hoc cloud / Ibrar Yaqoob
title_sort heterogeneity-aware task allocation in mobile ad hoc cloud / ibrar yaqoob
topic QA75 Electronic computers. Computer science
url http://studentsrepo.um.edu.my/7389/
http://studentsrepo.um.edu.my/7389/1/All.pdf
http://studentsrepo.um.edu.my/7389/9/ibrar.pdf