With increasing development of applications for heterogeneous, distributed computing grids, the focus of performance analysis has shifted from a posteriori optimization on homogeneous parallel systems to application tuning for heterogeneous resources with time varying availability. This shift has profound implications for performance instrumentation and analysis techniques. Autopilot is a new infrastructure for dynamic performance tuning of heterogeneous computational grids based on closed loop control. The paper describes the Autopilot model of distributed sensors, actuators, and decision procedures, reports preliminary performance benchmarks, and presents a case study in which the Autopilot library is utilized in the development of an adaptive parallel input/output system.

We describe how to investigate collections of trace data representing network delivery of multimedia information with CHITRA95, a tool that allows a user to visualize, query, statistically analyze and test, transform, and model collections of trace data. CHITRA95 is applied to characterize World Wide Web (WWW) traffic from three workloads: students in a classroom of network-connected workstations, graduate students browsing the Web, undergraduates browsing educational and other materials, as well as traffic on a courseware repository server. We explore the inter-access time of files on a server (i.e., recency), the hit rate from a proxy server cache, and the distributions of file sizes and media types requested. The traffic study also yields statistics on the effectiveness of caching to improve transfer rates. In contrast to past WWW traffic studies, we analyze client as well as server traffic; we compare three workloads rather than drawing conclusions from one workload; and we analyze tcpdump logs to calculate the performance improvement in throughput that an end user sees due to caching.