Unleashing the Power of Mobile Cloud Computing using ThinkAir

TL;DR

ThinkAir is a cloud-based framework that enhances mobile app performance by enabling scalable, parallelized computation offloading using virtualization, significantly reducing execution time and energy consumption.

Contribution

It introduces a scalable, elastic mobile cloud computing framework with method-level offloading and parallel execution capabilities, improving performance over previous solutions.

Findings

Execution time and energy consumption decreased significantly with cloud offloading.

Parallel VM execution further reduces execution time and energy use.

Dynamic VM provisioning meets varying computational demands effectively.

Abstract

Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method level computation offloading. Advancing on previous works, it focuses on the elasticity and scalability of the server side and enhances the power of mobile cloud computing by parallelizing method execution using multiple Virtual Machine (VM) images. We evaluate the system using a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for the N-queens puzzle and one order of magnitude for a face detection and a virus scan application, using cloud offloading. We then show that if a task is parallelizable, the user can request more than one VM to execute it, and these VMs will be provided dynamically. In fact, by exploiting parallelization, we achieve a greater reduction on the execution time and energy consumption for the previous applications. Finally, we use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements.