Next-generation parallel and distributed computing must be dependable and have predictable performance in order to meet the requirements of increasingly complex scientific and commercial applications. The large-scale nature and changing user requirements of such applications, coupled with the changing fault environment and workloads in which they must operate, dictate that their dependability and performance must be managed in an online fashion, reacting to changes in anticipated and observed faults, demands placed on the system, and changes in specified dependability, performance, and/or functional requirements. This project will create a compiler-enabled model- and measurement-driven adaptation environment that allows distributed applications to perform as expected despite faults that may occur. Achieving those capabilities will require fundamental advances in and synergistic combinations between 1) compiler-based flexible dependability mechanisms, 2) efficient online model-based prediction and control, and 3) measurement-driven and compiler-enabled early error detection. The project will validate and apply the adaptation environment by using it for two important applications from the scientific and commercial domains: the CARMA (Combined Array for Research in Millimeter-Wave Astronomy) image pipeline, a data-intensive Grid application for radio astronomy, and iMobile, an enterprise-scale mobile services platform.
