Type of Document Master's Thesis Author Rao, Deepak Author's Email Address email@example.com URN etd-05232001-154928 Title Efficient and Portable Middleware for Application-level Adaptation Degree Master of Science Department Computer Science Advisory Committee
Advisor Name Title Nance, Richard E. Committee Chair Keller, Benjamin J. Committee Member Midkiff, Scott F. Committee Member Keywords
- Mobile Computing
- Application-aware Adaptation
Date of Defense 2001-05-16 Availability unrestricted AbstractSoftware-intensive systems operating in a shared environment must utilize a “request, acquire and release” protocol. In the popular client-server architecture resource-poor clients rely on servers for the needed capabilities. With mobile clients using wireless connectivity, the disparity in resource needs can force the consideration of adaptation by clients, leading to a strategy of self-reliance. Achieving self-reliance through adaptation becomes even more attractive in environments, which are dynamic and continually changing. A more comprehensive strategy is for the mobile client to recognize the changing resource levels and plan for any such degradation; that is, the applications in the mobile client need to adapt to the changing environment and availability of resources.
Portable adaptation middleware that is sensitive to architecture and context changes in network operations is designed and implemented. The Adaptation Middleware not only provides the flexibility for the client applications to adapt to changing resources around them, but also to changing resource levels within the client applications. Further, the Adaptation Middleware imposes few changes on the structure of the client application. The Adaptation Middleware creates the adaptations; the client remains unaware and unconcerned with these adaptations.
The Adaptation Middleware in this study also enables a more informative cost estimation with regard to applications such as mobile agents. A sample application developed using the Adaptation Middleware shows performance improvements in the range of 31% to 54%. A limited set of experiments show an average response time of 68 milliseconds, which seems acceptable for most applications. Further, the Adaptation Middleware permits increased stability for applications demonstrating demand levels subject to high uncertainty.
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