LTE Based High-Performance Mobile-Cloud Computing


Authors : Deepika Kaushik , Sonu Mittal .

Volume/Issue : Volume 1 - 2016, Issue 6 - September

Google Scholar : https://goo.gl/js9m2M

Scribd : https://goo.gl/3c0x1s

The rise of the mobile-cloud computing paradigm in recent years has enabled mobile devices with processing power and battery life limitations to achieve complex tasks in real-time. While mobile-cloud computing is promising to overcome the limitations of mobile devices for real-time computing, the lack of frameworks compatible with standard technologies and techniques for dynamic performance estimation and program component relocation makes it harder to adopt mobile-cloud computing at large. Most of the available frameworks rely on strong assumptions such as the availability of a full clone of the application code and negligible execution time in the cloud. In the base paper [21] ,they present a dynamic computation offloading model for mobile-cloud computing, based on autonomous agents. Their approach does not impose any requirements on the cloud platform other than providing isolated execution containers, and it alleviates the management burden of offloaded code by the mobile platform using stateful, autonomous application partitions. They also investigate the effects of different cloud runtime environment conditions on the performance of mobile-cloud computing, and present a simple and low-overhead dynamic make span estimation model integrated into autonomous agents to enhance them with self performance evaluation in addition to self-cloning capabilities. In this research , we present the concept of LTE . LTE (Long-Term Evolution, commonly marketed as 4G LTE) is a standard for wireless communication of high-speed data for mobile phones and data terminals. It is based on the GSM/EDGE and UMTS/HSPA network technologies, increasing the capacity and speed using a different radio interface together with core network improvements. We are introducing LTE and reducing the execution time of different cloud runtime environment conditions on the performance of mobile-cloud computing, and present a simple and low-overhead dynamic make span estimation model.

Keywords : Mobile-Cloud Computing; Autonomous Agents; Context; Performance, LTE.

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