Leila Meshkat

GriPhyN and LIGO, building a virtual data Grid for gravitational wave scientists

Many Physics experiments today generate large volumes of data. That data is then processed in a variety of ways in order to achieve the understanding of fundamental physical phenomena. The goal of the NSF-funded GriPhyN project (Grid Physics Network) is to enable scientists to seamlessly access data whether it is raw experimental data or a data product which is a result of further processing. GriPhyN provides a new degree of transparency in how data-handling and processing capabilities are integrated to deliver data products to end-users or applications, so that requests for such products are easily mapped into computation and/or data access at multiple locations. GriPhyN refers to the set of all data products available to the user as virtual data. Among the physics applications participating in the project is the Laser Interferometer Gravitational-wave Observatory (LIGO), which is being built to observe the gravitational waves predicted by general relativity. We describe our initial design and prototype of a virtual data Grid for LIGO.

Multi-phase reliability analysis for dynamic and static phases

Two different classes of approaches have been suggested for the reliability analysis of multi-phase systems: BDD-based solution approach and Markov chain based approach. These approaches either assume that every phase is static, and thus can be solved with combinatorial methods, or that every phase must be modeled via Markov methods. If every phase is indeed static, then the combinatorial approach is much more efficient than the Markov chain approach. But in a multi-phased system, using currently available techniques, if the failure criteria in even one phase is dynamic, then a Markov approach must be used for every phase. At best this approach is inefficient; at worst it renders solution of multi-phased missions infeasible. In this paper, the authors consider a combination of both approaches, and develop a methodology for combining the analysis of some phases via combinatorial method with the analysis of other phases using Markov methods. The key to this combination is a careful definition of the interfaces and dependencies across phases.

Maintenance modelling for computer-based systems

Abstract A framework is presented for incorporating maintenance into a dependability analysis methodology for computer-based systems. Two types of maintenance are considered: failure-driven maintenance and time-driven maintenance. Failure-driven maintenance or repair is carried out when the system (or component) performance deviates from its expected performance and consists of all tasks performed to restore the functional capabilities of failed items, principally diagnosis and repair. Time-driven or scheduled maintenance is conducted on a specific time schedule in order to prevent system failure. There may be dependencies between different components of a system with regard to their maintenance plans. These dependencies arise either because a component has maintenance priority over one or more components or because the maintenance of a certain component implies the maintenance of other components. Constructs are presented for modelling these dependencies in the context of dynamic fault tree analysis and a methodology is developed for solving the fault tree. The dynamic fault tree constructs effectively capture the failure dependencies between components. The approach is illustrated with an example based on a water deluge system.

Modeling to Improve the Risk Reduction Process for Command File Errors

The Jet Propulsion Laboratory has learned that even innocuous errors in the spacecraft command process can have significantly detrimental effects on a space mission. Consequently, such Command File Errors (CFE), regardless of their effect on the spacecraft, are treated as significant events for which a root cause is identified and corrected. A CFE during space mission operations is often the symptom of imbalance or inadequacy within the system that encompasses the hardware and software used for command generation as well as the human experts and processes involved in this endeavor. As we move into an era of increased collaboration with other NASA centers and commercial partners, these systems become more and more complex. Consequently, the ability to thoroughly model and analyze CFEs formally in order to reduce the risk they pose is increasingly important. In this paper, we summarize the results of applying modeling techniques previously developed to the DAWN flight project. The original models were built with the input of subject matter experts from several flight projects. We have now customized these models to address specific questions for the DAWN flight project and formulating use cases to address their unique mission needs. The goal of this effort is to enhance the projects ability to meet commanding reliability requirements for operations and to assist them in managing their Command File Errors.

Managing the Risk of Command File Errors

Command File Error (CFE), as defined by the Jet Propulsion Laboratory’s (JPL) Mission Operations Assurance (MOA) is, regardless of the consequence on the spacecraft, either: an error in a command file sent to the spacecraft, an error in the process for developing and delivering a command file to the spacecraft, or the omission of a command file that should have been sent to the spacecraft. The risk consequence of a CFE can be mission ending and thus a concern to space exploration projects during their mission operations. A CFE during space mission operations is often the symptom of some kind of imbalance or inadequacy within the system that comprises the hardware & software used for command generation and the human experts involved in this endeavour. As we move into an era of enhanced collaboration with other NASA centers and commercial partners, these systems become more and more complex and hence it is all the more important to formally model and analyze CFEs in order to manage the risk of CFEs. Here we will provide a summary of the ongoing efforts at JPL in this area and also explain some more recent developments in the area of developing quantitative models for the purpose of managing CFE’s. I. Introduction There has been much effort directed at reducing command file related errors at JPL over the last decade. These efforts have included the identification, classification, tracking, recording and root cause determiniation of these errors for all flight projects. The effort described in this paper is a recent endeavour to use the existing knowledge and body of work within the institution to develop compact, executable stochastic models that are re-usable and can be tweaked for the purposes of sensitivity analysis for the effectiveness of error reduction measures. In the background section below, the on going effort at JPL over the last decade is explained. In the modeling section, the overall development of the model and some of the analyses conducted with it to date are explained. We conclude by synthesizing the results obtained to date and describing the expected future directions for this endeavour.

Soft Factors and Space Mission Failures: Quantifying the Effects of Management Decisions

Model Bayesian Belief Network w/Probabilities 7 National Aeronautics and Space Administration The Future • Soft factors play an enormous role in mission success or failure. • Possible to use quantitative modeling techniques to make informed decisions regarding risk related to command file errors • Future directions: – Calibrate models, customize & exercise on forensic case studies to improve the CFE rates – enhance the management and organizational factors sub-model to account for qualities of successful management

Addressing the Hard Factors for Command File Errors by Probabilistic Reasoning

Command File Errors (CFE) are managed using standard risk management approaches at the Jet Propulsion Laboratory. Over the last few years, more emphasis has been made on the collection, organization, and analysis of these errors for the purpose of reducing the CFE rates. More recently, probabilistic modeling techniques have been used for more in depth analysis of the perceived error rates of the DAWN mission and for managing the soft factors in the upcoming phases of the mission. We broadly classify the factors that can lead to CFEs as soft factors, which relate to the cognition of the operators and hard factors which relate to the Mission System which is composed of the hardware, software and procedures used for the generation, verification & validation and execution of commands. The focus of this paper is to use probabilistic models that represent multiple missions at JPL to determine the root cause and sensitivities of the various components of the mission system and develop recommendations and techniques for addressing them. The customization of these multi-mission models to a sample interplanetary spacecraft is done for this purpose.

Data Analysis & Statistical Methods for Command File Errors

This paper explains current work on modeling for managing the risk of command file errors. It is focused on analyzing actual data from a JPL spaceflight mission to build models for evaluating and predicting error rates as a function of several key variables. We constructed a rich dataset by considering the number of errors, the number of files radiated, including the number commands and blocks in each file, as well as subjective estimates of workload and operational novelty. We have assessed these data using different curve fitting and distribution fitting techniques, such as multiple regression analysis, and maximum likelihood estimation to see how much of the variability in the error rates can be explained with these. We have also used goodness of fit testing strategies and principal component analysis to further assess our data. Finally, we constructed a model of expected error rates based on the what these statistics bore out as critical drivers to the error rate. This model allows project management to evaluate the error rate against a theoretically expected rate as well as anticipate future error rates.