Grid computing brings geographically dispersed computing power to meet the ever-increasing demands. However, representing new and future computing platforms, Grid is a globally shared, heterogeneous, and autonomous controlled platform. It is far more advanced, powerful, dynamic and complex than computing platforms in he past. Such complexity requires new system software technology to efficiently utilize its computing capacity. Most existing performance technologies are targeted for dedicated platforms. Recent performance facilities, such as NWS, only predict short-term (less than five minutes) resource availability, which is not appropriate for long-term applications. New software technologies are needed for long-term, application-level performance predication and task scheduling to alleviate the complexity of Grid. Preliminary results show that GHS is fundamentally better than existing systems for long-term applications and can lead to substantial decrease in computing cost. The PIs will collaborate with researchers at DOE national laboratories and IIT to demonstrate the great potential of GHS with important national interest applications. The research approach is based on the observation that Grid environments do not have central control and performance efficiency has to be based on resource availability. GHS will exploit this observation. In particular the project: 1) will design, implement, and validate stochastic and analytical models to predict the computing and communication resource availability and their influence on user applications. 2) will develop, implement, and validate practical and non-intrusive performance measurement technologies. 3) will design, implement, and validate task scheduling and rescheduling algorithms to utilize the prediction given in (1) to reduce user application run-times.
