Heterogeneity-aware task allocation in mobile ad hoc cloud / Ibrar Yaqoob - Ibrar, Yaqoob (2017)
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.