Khune, Aditya Dilip, authorPasricha, Sudeep, advisorJayasumana, Anura P., committee memberGesumaria, Bob, committee member2017-06-092017-06-092017http://hdl.handle.net/10217/181389Offloading mobile computations is an innovative technique that is being explored by researchers for reducing energy consumption in mobile devices and for achieving better application response time. Offloading refers to the act of transferring computations from a mobile device to servers in the cloud. There are many challenges in this domain that are not dealt with effectively yet, and thus offloading is far from being adopted in the design of current mobile architectures. We believe that there is a need to verify the effectiveness of computation offloading in terms of both response time and energy consumption, to highlight its potential in real smartphone applications. The effect of varying network technologies such as 3G, 4G, and Wi-Fi on the performance of offloading systems is also a major concern that needs to be addressed. In this thesis, we study the behavior of a set of real smartphone applications, in both local and offload processing modes. Our experiments identify the advantages and disadvantages of offloading for various mobile networks. Further, we propose a middleware framework that uses Reinforcement Learning to make reward-based offloading decisions effectively. Our framework allows a smartphone to consider suitable contextual information to determine when it makes sense to offload, and to select between available networks (3G, 4G, or Wi-Fi) when offloading mode is active. We tested our framework in both simulated and real environments, across various applications, to demonstrate how energy consumption can be minimized in mobile systems that are capable of supporting offloading.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.offloadingmobile computingreinforcement learningText