Heterogeneity-Aware Task Allocation in Mobile Ad Hoc Cloud

IEEE Access

Ibrar Yaqoob¹  Abdullah Gani¹  Ejaz Ahmed¹  Muhammad Imran²  Salimah Mokhtar³ 

[1] Centre for Mobile Cloud Computing Research, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia;College of Computer and Information Sciences, King Saud University, Almuzahmiyah, Saudi Arabia;Department of Information System, University of Malaya, Kuala Lumpur, Malaysia;

关键词: Mobile ad hoc cloud;    mobile cloud;    task allocation;    mobile cloud computing;   

DOI  :  10.1109/ACCESS.2017.2669080

来源: DOAJ

【 摘 要 】

Mobile Ad Hoc Cloud (MAC) enables the use of a multitude of proximate resource-rich mobile devices to provide computational services in the vicinity. 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. This paper aims to minimize execution time and energy consumption by proposing heterogeneity-aware task allocation solutions for MAC-based compute-intensive tasks. Results of the proposed solutions 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. A system model is developed to validate the proposed solutions' empirical results. In comparison with random-based task allocation, the proposed five solutions based on CPU speed, number of core, workload, CPU speed and workload, and CPU speed, core, and workload 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. For this reason, the proposed solutions significantly improve tasks' execution performance, which can increase the optimum use of MAC.

【 授权许可】

Unknown