Offloading of Mobile Apps in Hand-Held Devices using Machine Learning
Neelesh Chourasiya1, Neeraj Mohan2
1Neelesh Chourasiya,Computer Science Engineering Dept., IKGPTU, Kapurthala, Punjab, India.
2Neeraj Mohan, Computer Science Engineering Dept., IKGPTU, Kapurthala, Punjab, India.
Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 10536-10543 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4602118419/2019©BEIESP | DOI: 10.35940/ijrte.D4602.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The usage of hand-held and mobile devices has been increased rapidly in recentyears. The execution of sophisticated softwares and Apps on mobile phonescan lead to poor performance with respect to energy consumption and response- time. With the emergence of the offloading concept of App workloads, an attempt has been made toimprove the performance of the hand-held devices by exploiting cloud service. The computation offloadingin hand-held devices consumes energy as well as time for transferring the datafrom hand-held devices to cloud. For the effective use of cloud services, there is a need to optimize the execution time of mobile App and energy consumedby the respective App. Many research endeavors have been made in recentyears to reduce the execution time and energy consumption during offloadingprocess. However, the usage of offloading has been evolved to quench the thirstof mobile users who execute multiple Apps simultaneously and are in dire needof seamless connectivity but some dynamic algorithms are needed to decidewhether offloading is favorable or not for a mobile App. If the mobile Apptakes more time and consumes more battery if executed on cloud then it isrecommended to use mobile platform rather than using cloud services. In thispaper, we are presenting machine learning based techniques which would help the mobile users in decision making to execute the App on mobile devices or on cloud using cloud services.
Keywords: Offloading, Machine Learning, Handheld Devices, Mobile Computing, Mobile Apps.
Scope of the Article: Machine Learning.