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.
Highlights
Advancements in the Information and Communication Technologies sector and related Internet of Things (IoT) technologies have an impact on humans’ lives all over the world.IoT is, in simple words, intelligent things equipped with sensors that gather data without the interactions of humans
We explored task scheduling and task enhancement effects on Mobile cloud computing (MCC) to improve the performance of mobile devices
Based on the proposed model in this paper, we submit a new task scheduling policy that incorporates and provides offloading mobile device calculation and offloading probability based on the time calculation of different scenarios of task scheduling from the mobile device to the cloud
Summary
Advancements in the Information and Communication Technologies sector and related Internet of Things (IoT) technologies have an impact on humans’ lives all over the world. Different cloud service providers (CSP) provide such services to their cloud users for better understanding and convenience [15] Several existing methodologies such as intelligent batteries, power scheduling, efficient operating system, energy-aware communication protocols, and applications are introduced to reduce mobile devices’ energy and time consumption. The task that should be offloaded from mobile devices to the MCC VM must be decided based on some significant factors like memory, execution time, processing energy, network bandwidth, processor utilization, allo-. Limited capabilities of mobile devices in terms of energy, storage, time, transmission bandwidth, and computations trigger problems associated with energy optimization and time management while processing tasks on a mobile phone This problem needs to be addressed in this paper, focusing on efficient dynamic decision-based task scheduling in MCC.
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