The paper presents the rationale for a functional simulation tool, called DEPEND, which provides an integrated design and fault injection environment for system level dependability analysis. The paper discusses the issues and problems of developing such a tool, and describes how DEPEND tackles them. Techniques developed to simulate realistic fault scenarios, reduce simulation time explosion, and handle the large fault model and component domain associated with system level analysis are presented. Examples are used to motivate and illustrate the benefits of this tool. To further illustrate its capabilities, DEPEND is used to simulate the Unix-based Tandem triple-modular-redundancy (TMR) based prototype fault-tolerant system and to evaluate how well it handles near-coincident errors caused by correlated and latent faults. Issues such as memory scrubbing, re-integration policies, and workload dependent repair times, which affect how the system handles near-coincident errors, are also evaluated. Unlike any other simulation-based dependability studies, the accuracy of the simulation model is validated by comparing the results of the simulations with measurements obtained from fault injection experiments conducted on a production Tandem machine.

Presents dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30% better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50% improvements relative to their nonpredictive counterpart.>