Fundamental Techniques for Incentive-aware, Efficient, and Reliable Cloudlet Management and Services

The goal of this work is to develop some fundamental techniques that can facilitate efficient management of cloudlet resources for high cloud service performance while minimizing the system cost. These techniques are to enable efficient, reliable and low cost mobile cloud services, which will in turn support high-performance wireless applications in an energy- and resource efficient manner. With this research, a cloud service provider can exploit sparse signal processing technique to remotely monitor one or multiple cloudlet clusters running in different location sites with high accuracy but low cost. To reduce the service initialization delay and operational cost, the system can cache application components, and a user can exploit the proposed rate dependent Bloom filter (RDBF) to quickly determine of some cloudlets have its required application components cached. Rather than being constrained by a limited number of options for virtual machine (VM) configurations, the system will be equipped with multi-dimensional pricing and resource negotiation capabilities for a user to flexibly negotiate multiple types of resources to maximize the benefits of both users and cloudlets while taking into account user application preference, performance requirements, and the system resource conditions.

Complementary to the research agenda, the project will carry out a broad range of education and outreach activities and facilitate technology transfer through the industrial partners and industry affiliate programs. The proposed research is part of a global effort in exploiting (mobile) cloud computing for advancing some critical fields for national economy and security, including health-care, transportation, smart-grid, homeland security, and education.

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