Seamless interconnection of smart mobile devices and cloud services is a key goal in modern mobile computing. Mobile Cloud Computing is the holistic integration of contextually-rich mobile devices with computationally-powerful cloud services to create high value products for end users, such as Apple's Siri and Google's Google Now product. This coupling has enabled new paradigms and fields of research, such as crowdsourced data collection, and has helped spur substantial changes in research fields such as vehicular ad hoc networking. However, the growth of Mobile Cloud Computing has resulted in a number of new challenges, such as testing large-scale Mobile Cloud Computing systems, and increased the importance of established challenges, such as ensuring that a user's privacy is not compromised when interacting with a location-aware service. Moreover, the concurrent development of the Infrastructure as a Service paradigm has created inefficiency in how Mobile Cloud Computing systems are executed on cloud platforms. To address these gaps in the existing research, this dissertation presents a number of software and algorithmic solutions to 1) preserve user locational privacy, 2) improve the speed and effectiveness of deploying and executing Mobile Cloud Computing systems on modern cloud infrastructure, and 3) enable large-scale research on Mobile Cloud Computing systems without requiring substantial domain expertise. / Ph. D.

<p>This thesis presents data analysis and methodology for two prediction problems. The first problem is forecasting midlife credit ratings from personality information collected during early adulthood. The second problem is analysis of matrix multiplication in cloud computing.</p><p>The goal of the credit forecasting problem is to determine if there is a link between personality assessments of young adults with their propensity to develop credit in middle age. The data we use is from a long term longitudinal study of over 40 years. We do find an association between credit risk and personality in this cohort Such a link has obvious implications for lenders but also can be used to improve social utility via more efficient resource allocation</p><p>We analyze matrix multiplication in the cloud and model I/O and local computation for individual tasks. We established conditions for which the distribution of job completion times can be explicitly obtained. We further generalize these results to cases where analytic derivations are intractable.</p><p>We develop models that emulate the multiplication procedure, allowing job times for different deployment parameter settings to be emulated after only witnessing a subset of tasks, or subsets of tasks for nearby deployment parameter settings. </p><p>The modeling framework developed sheds new light on the problem of determining expected job completion time for sparse matrix multiplication.</p> / Dissertation

Thesis (Master').