Wah This research targets efficient distributed computing through intelligent scheduling of application programs. The approach has three components: compiler development, measurement of system loads, and automated learning of optimal scheduling policies. Compilers are modified to extract control and data dependencies from applications programs, emit performance monitoring code, and allow partitioning into processes with predictable resource requirements. A neural network model is being developed to characterize system loads based on ready list lengths, I/O traffic, and network congestion. Finally, an automated learning system will tune scheduling policies to balance system loads using the predicted and measured application program requirements.
