Adaptive Resource Allocation Optimization in Heterogeneous Mobile Cloud Systems
Recent growth of cloud computing has driven the development of Heterogeneous Cloud Federation (HCF) that has been considered an approach of improving the diversity of cloud systems by enable cloud users to select resources provided by different service providers. Using this mechanism can divide a complicate task into a group of sub-tasks in order to increase the efficiency of application executions, which can support simultaneously executing different cloud systems. One of the resource allocation mechanisms is to schedule tasks among cloud providers. However, current challenge of implementing this mechanism is to generate an efficient task scheduling. We consider mobile cloud computing an adaptive approach for increasing the utilizations of mobile devices. Addressing this dimension, we propose our solution named Mobile Cloud-based Heterogeneous Resource Allocation Model (MC-HRAM), which is designed to minimize the execution time. The main algorithm used in the proposed model is Dynamic Heterogeneous Task Assignments Algorithm (DHTA). Our experimental evaluation has proved that our proposed schema has an advantage of saving execution time.