Computation Partitioning in Mobile Cloud Applications: Modeling, Optimization and Evaluations

The proliferation of sensors on mobile devices and ubiquitous network access to clouds enable many mobile cloud applications such as augmented reality (e.g., Google Glass), voice recognition (e.g., iPhone Siri), real time translation and so on. Computation partitioning between the mobile device and the cloud for these applications is an important and challenging research topic. Although there are works done on some aspects of this study, how to provide a systematic approach to support the partitioning for various models of applications and systems is yet to be addressed. In this thesis, we classify computation partitioning into different models by considering two dimensional properties: application dimension and system dimension. On application dimension, we do partitioning for two types of applications: computation dependent application and computation independent application. On system dimension, we do the partitioning for two types of systems: single user system and multiple users system. In this thesis, we focus on the study of three models: 1) computation independent application and single user system, named as computation offloading, 2) computation dependent application and single user system, named as single user computation partitioning, 3) computation dependent application and multiple user system, named as multiple user computation partitioning. The thesis contains three parts, which tackle the most urgent and significant issues in terms of the thee models. The details are as follows. First, we study the simplest model of computation partitioning, where the application is composed of independent computations and the partitioning decision is done for one single user. We take the RFID tracking as a case study, and demonstrate that computation partitioning can significantly improve the application performance. In particular, we consider the RFID system that attaches the RFID reader on the moving object, and deploys passive RFID tags in the environment. The moving object collects the noisy RFID readings, and perform continuous estimation of its position in real time. Traditional approaches such as Particle Filter (PF) can achieve high accuracy, but require a large amount of computations