Opportunities for data-driven cloud-based mobile optimization

Abstract

In this paper, we present our vision for cloud-based mobile computing using user profile information. Such information enables a series of data-driven optimizations: filtering, aggregation, and speculation, that go beyond the well-researched benefit of mobile outsourcing. These optimizations can improve performance, reliability, and energy usage. A novel aspect of our approach is to exploit the unique ability of the cloud to collect and analyze large amounts of user profile data, cache shared data, and even enable sharing of computations, across different mobile users. We present results for two exemplar mobile-cloud applications, driven by workload traces derived from Twitter feeds and Wikipedia document editing, to illustrate these opportunities.