Design and analysis of task offloading and scheduling using meta heuristic approaches in mobile cloud environment

Abstract

Mobile Cloud Computing offers the benefit of cloud utilization to newlineexpand the scalability of mobile devices. The rapid growth of cloud newlinecomputing provides many advancements to mobile users with high resource newlinesharing and usage. In order to enhance the computing capability of the mobile newlinedevice, the task offloading process has been developed using offloading newlinetechniques. The mobile device can easily offload the heavy computational newlinetask to the remote server through cloud. newlineThe research has been concentrated on an efficient task offloading newlinemethods that are developed by considering various factors like battery newlinelifetime, storage capacity and performance of the mobile devices. To reduce newlinethe execution time and to increase the computational speed of the task in newlinecloud, various scheduling algorithms are performed. The research has mainly newlinefocused on three task offloading and scheduling models such as Rule newlineGeneration Task offloading and Scheduling (RGTOS) model, Energy-Aware newlineDynamic Mobile Task Offloading and Scheduling (EDTOS) model and Gray newlineWolf Optimization Task Offloading and Scheduling (GWOTOS) Model. newline