Development of an Intelligent Monitoring and Control System for a Heterogeneous Numerical Propulsion System Simulation

The NASA Numerical Propulsion System Simulation (NPSS) project is exploring the use of computer simulation to facilitate the design of new jet engines. Several key issues raised in this research are being examined in an NPSS-related research project: zooming, monitoring and control, and support for heterogeneity. The design of a simulation executive that addresses each of these issues is described. In this work, the strategy of zooming, which allows codes that model at different levels of fidelity to be integrated within a single simulation, is applied to the fan component of a turbofan propulsion system. A prototype monitoring and control system has been designed for this simulation to support experimentation with expert system techniques for active control of the simulation. An interconnection system provides a transparent means of connecting the heterogeneous systems that comprise the prototype

Sampling benchmarks : methods for extracting intersecting segments of programs

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 46-49).by Peter D. Finch.S.B.and M.Eng

A Hierarchical Filtering-Based Monitoring Architecture for Large-scale Distributed Systems

On-line monitoring is essential for observing and improving the reliability and performance of large-scale distributed (LSD) systems. In an LSD environment, large numbers of events are generated by system components during their execution and interaction with external objects (e.g. users or processes). These events must be monitored to accurately determine the run-time behavior of an LSD system and to obtain status information that is required for debugging and steering applications. However, the manner in which events are generated in an LSD system is complex and represents a number of challenges for an on-line monitoring system. Correlated events axe generated concurrently and can occur at multiple locations distributed throughout the environment. This makes monitoring an intricate task and complicates the management decision process. Furthermore, the large number of entities and the geographical distribution inherent with LSD systems increases the difficulty of addressing traditional issues, such as performance bottlenecks, scalability, and application perturbation. This dissertation proposes a scalable, high-performance, dynamic, flexible and non-intrusive monitoring architecture for LSD systems. The resulting architecture detects and classifies interesting primitive and composite events and performs either a corrective or steering action. When appropriate, information is disseminated to management applications, such as reactive control and debugging tools. The monitoring architecture employs a novel hierarchical event filtering approach that distributes the monitoring load and limits event propagation. This significantly improves scalability and performance while minimizing the monitoring intrusiveness. The architecture provides dynamic monitoring capabilities through: subscription policies that enable applications developers to add, delete and modify monitoring demands on-the-fly, an adaptable configuration that accommodates environmental changes, and a programmable environment that facilitates development of self-directed monitoring tasks. Increased flexibility is achieved through a declarative and comprehensive monitoring language, a simple code instrumentation process, and automated monitoring administration. These elements substantially relieve the burden imposed by using on-line distributed monitoring systems. In addition, the monitoring system provides techniques to manage the trade-offs between various monitoring objectives. The proposed solution offers improvements over related works by presenting a comprehensive architecture that considers the requirements and implied objectives for monitoring large-scale distributed systems. This architecture is referred to as the HiFi monitoring system. To demonstrate effectiveness at debugging and steering LSD systems, the HiFi monitoring system has been implemented at the Old Dominion University for monitoring the Interactive Remote Instruction (IRI) system. The results from this case study validate that the HiFi system achieves the objectives outlined in this thesis

Analyzing the Combined Effects of Measurement Error and Perturbation Error on Performance Measurement

Dynamic performance analysis of executing programs commonly relies on statistical profiling techniques to provide performance measurement results. When a program execution is sampled we learn something about the examined program, but also change, to some extent, the program's interaction with the underlying system and thus its behavior. The amount we learn diminishes (statistically) with each sample taken, while the change we affect with the intrusive sampling risks growing larger. Effectively sampling programs is challenging largely because of the opposing effects of the decreasing sampling error and increasing perturbation error. Achieving the highest overall level of confidence in measurement results requires striking an appropriate balance between the tensions inherent in these two types of errors. Despite the popularity of statistical profiling, published material typically only explains in general qualitative terms the motivation of the systematic sampling rates used. Given the importance of sampling, we argue in favor of the general principle of deliberate sample size selection and have developed and tested a technique for doing so. We present our idea of sample rate selection based on abstract and mathematical performance measurement models we developed that incorporate the effect of sampling on both measurement accuracy and perturbation effects. Our mathematical model predicts the sampling size at which the combination of the residual measurement error and the accumulating perturbation error is minimized. Our evaluation of the model with simulation, calibration programs, and selected programs from the SPEC CPU 2006 and SPEC OMP 2001 benchmark suites indicates that this idea has promise. Our results show that the predicted sample size is generally close to the best sampling rate and effectively avoids bad choices. Most importantly, adaptive sample rate selection is shown to perform better than a single selected rate in most cases