An Energy Conservation through an Adapted Probabilistic Scheduler for Timely Data Exchange in Local Mobile Cloud

Abstract

Mobile Cloud Computing has emerged as a pivotal technology, enabling mobile devices to harness external resources for hosting  applications and significantly reducing latency. Recent research introduces the concept of a 'local mobile cloud,' formed by proximate  mobile devices, to offload complex real-time applications to nearby devices, which minimizes energy requirement, and communication latency. This research introduces a more efficient task scheduling algorithm that is based on probabilistic task scheduling technique. This  moves computations from multiple source nodes to closer processing nodes. A simulation model for local mobile clouds using OMNET++,  is used for assessing the performance of the task scheduling algorithm. Additionally, a comparative analysis of the task scheduler with  alternative scheduling schemes was conducted to evaluate performance in terms of the energy consumption, and process completion  time. The outcome of the study showed that the probabilistic task scheduling technique improved the computing time and further  conserved the energy resource requirement.