Novel Approach for Job Offloading Technique in Mobile Cloud Computing

Abstract

User preferences for computing have evolved as a result of recent advancements in mobile computing technologies. Mobile devices (SMDs) are still low potential computer platforms with capped memory sizes, CPU speeds, and battery life. Computationally heavy mobile apps may be run on SMDs thanks to Mobile Cloud Computing (MCC), which uses computational offloading. In both the business and academic worlds, there is rising interest in the mobile cloud computing is a new computing paradigm. Lack of a comprehensive experimental framework to use in their experiments and to evaluate their proposed work is a serious issue for mobile cloud computing researchers. Through MCC, mobile devices will be able to serve a wider variety of resource-intensive tasks while maintaining and growing their resource pools. In order to improve the mobile user experience, it places a high priority on improving energy efficiency, storage capacity, computational power, and data security. Since both the mobile device and the Cloud must determine energy-time trade-offs and decisions made on one side have an influence on the performance of the other, designing MCC systems is a challenging task. According to an examination of the MCC literature, all present models are centred on mobile devices, with the Cloud viewed as a system with infinite resources. Furthermore, no MCC-specific simulation tool is currently known to exist. To fill this need, we present in this study a Novel Approach for Job Offloading in Cloud Environments such as Google Cloud, using OCR application, while attempting to reduce energy use (Power). We are measuring the results of this experiment on both Cloud Computing and Mobile Device Computing.