A Self-Cloning Agents Based Model for High-Performance Mobile-Cloud Computing

Abstract

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 this paper, we present a dynamic computation offloading model for mobile-cloud computing, based on autonomous agents. Our 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. We 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 makespan estimation model integrated into autonomous agents to enhance them with selfperformance evaluation in addition to self-cloning capabilities. The proposed performance profiling model is used in conjunction with a cloud resource optimization scheme to ensure optimal performance. Experiments with two mobile applications demonstrate the effectiveness of the proposed approach for high-performance mobile-cloud computing.

An IoT Platform for Epilepsy Monitoring and Supervising

“…Offloading varies from static partitioning of the data [74], aiming to reduce the battery consumption, to the dynamic partitioning [75,76], where the partitioning is adjusted according to the network availability and the computing capacity of the mobile device. A hybrid partitioning is proposed using two defined data bunch sizes (NSRegular and NSDelayed).…”

Providing Useful Data Reliably to Mobile Cloud Users from Random Wireless Sensor Network

“…Based on that the appropriate WSN sensors are chosen in particular clusters and put into wake state for sensing and forwarding the data to the gateway. This ensures the energy efficiency of WSN and minimizes the network usage and congestion [7]. Each sensor senses the information based on the time occurrence and stores in the cloud for further processing.…”

“…The type of services used in developing such mobile applications is preferably java.  Pelin Angin, Bharat Bhargava and Zhongjun Jin [7], explained how we attain difficult jobs in real time we use mobile cloud computing in latest years with the limited usage of battery and power. Mobile cloud computing is used to overcome the drawbacks of mobile devices for recent years.…”

“…Each sensor senses the information based on the time occurrence and stores in the cloud for further processing. Based on the priority user gets the data using the timeslot [13].…”

An Experimental Analysis on Performance and Energy Saving in Mobile Cloud Computing

“…The application when executed for the first time generates profile files containing data on memory used, network bandwidth to be used in subsequent execution and processing time. Angin et al [13] proposes an autonomous agent based dynamic computation offloading model where the application is partitioned into autonomous agent based modules and sent to the cloud and native application module are executed on mobile statically. The framework consists of an execution manager which resides on mobile and responsible for deciding whether the module of an application should be migrated to cloud or not.…”