An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing

TLDR

Mobile devices have limited storage, computation, energy, and transmission capabilities, which hinder energy optimization and time management during task processing, especially in mobile, fog, and edge computing contexts, leading to challenges in task retention and offloading. The study proposes a novel task scheduling algorithm that uses an energy‑efficient dynamic decision‑based method to reduce energy consumption and execution time. The algorithm adapts to cloud tasks and mobile device energy/time metrics, and a task‑scheduling server offloads computation to the cloud, leveraging an empirical algorithm to improve decision‑making and performance. The results show that the proposed scheduler significantly reduces energy consumption and improves task scheduling efficiency.

Abstract

Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device’s decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.