International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / This paper discusses the design and development of the EUVE Virtual Environment (EVE) system. The EVE system is being developed as an interactive virtual reality (VR) viewing tool for NASA's Extreme Ultraviolet Explorer (EUVE) satellite. EVE will serve as a predictive tool for forecasting spacecraft constraint violations and will provide a capability for spacecraft problem analysis and resolution in realtime through visualization of the problem components in the spacecraft. EVE will animate, in three-dimensional realtime, the spacecraft dynamics and thermal characteristics of the EUVE spacecraft. EVE will also display the field of view for the science instrument detectors, star trackers, sun sensors, and both the omni and high-gain antennas for NASA's Tracking and Data Relay Satellite System (TDRSS) and for possible ground station contact. EVE will display other vital spacecraft information to support the routine operations of the EUVE spacecraft. The EVE system will provide three quick-look visualization functions: (1) to model in-orbit data for realtime spacecraft problem analysis and resolution, (2) to playback data for post-pass data analysis, and training exercises, and (3) to simulate data in the science planning process for optimum attitude determination and to predict spacecraft and thermal constraint violations. We present our preliminary design for a telemetry server, providing both realtime and post pass data, that uses standard Unix utilities. We also present possibilities for future integration of the EVE system with other software to automate the science planning and command generation functions of the satellite operations.

One of the important design criteria for distributed systems and their applications is their reliability and robustness to hardware and software failures. The increase in complexity, interconnectedness, dependency and the asynchronous interactions between the components that include hardware resources (computers, servers, network devices), and software (application services, middleware, web services, etc.) makes the fault detection and tolerance a challenging research problem. In this dissertation, we present a self healing methodology based on the principles of autonomic computing, statistical and data mining techniques to detect faults (hardware or software) and also identify the source of the fault. In our approach, we monitor and analyze in real-time all the interactions between all the components of a distributed system using two software modules: Component Fault Manager (CFM) to monitor all set of measurement attributes for applications and nodes and Application Fault Manager (AFM) that is responsible for several activities such as monitoring, anomaly analysis, root cause analysis and recovery. We used three-dimensional array of features to capture spatial and temporal features to be used by an anomaly analysis engine to immediately generate an alert when abnormal behavior pattern is detected due to a software or hardware failure. We use several fault tolerance metrics (false positive, false negative, precision, recall, missed alarm rate, detection accuracy, latency and overhead) to evaluate the effectiveness of our self healing approach when compared to other techniques. We applied our approach to an industry standard web e-commerce application to emulate a complex e-commerce environment. We evaluate the effectiveness of our approach and its performance to detect software faults that we inject asynchronously, and compare the results for different noise levels. Our experimental results showed that by applying our anomaly based approach, false positive, false negative, missed alarm rate and detection accuracy can be improved significantly. For example, evaluating the effectiveness of this approach to detect faults injected asynchronously shows a detection rate of above 99.9% with no false alarms for a wide range of faulty and normal operational scenarios.

In the information age of today, vast amounts of sensitive and confidential data is exchanged over an array of different mediums. Accompanied with this phenomenon is a comparable increase in the number and types of attacks to acquire this information. Information security and data consistency have hence, become quintessentially important. Log file analysis has proven to be a good defense mechanism as logs provide an accessible record of network activities in the form of server generated messages. However, manual analysis is tedious and prohibitively time consuming. Traditional log analysis techniques, based on pattern matching and data mining approaches, are ad hoc and cannot readily adapt to different kinds of log files. The goal of this research is to explore the use of grammar inference for log file analysis in order to build a more adaptive, flexible and generic method for message categorization, anomaly detection and reporting. The grammar inference process employs robust parsing, islands grammars and source transformation techniques. We test the system by using three different kinds of log file training sets as input and infer a grammar and generate message categories for each set. We detect anomalous messages in new log files using the inferred grammar as a catalog of valid traces and present a reporting program to extract the instances of specified message categories from the log files. / Thesis (Master, Computing) -- Queen's University, 2008-05-22 14:12:30.199

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