An experimental evaluation of the REE SIFT environment for spaceborne applications

Whisnant, K.; Iyer, R.K.; Jones, Phillip H.; Some, Raphael; Rennels, D. A.

doi:10.1109/dsn.2002.1029004

Cited by 18 publications

(12 citation statements)

References 31 publications

(20 reference statements)

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“…We now apply the sampling based uncertainty propagation method, to the reliability analysis of the NASA Remote Exploration and Experimentation (REE) system [40,13,90]. The NASA Jet Propulsion Laboratory REE Project was a large multi-year technology demonstration project to develop a low-power, scalable, faulttolerant, high-performance computing platform for use in space and to demonstrate that significant on-board computing enables a new class of scientific missions.…”

Section: Reliability Of Remote Exploration and Experimentation Systemmentioning

confidence: 99%

“…The system provided fault detection and recovery mechanisms so that applications could operate in the presence of faults. Availability and reliability of the REE system was analyzed in [13,90], with the help of several models, to assess its fault-tolerance features. These models were specified and solved using the SHARPE [87] software package.…”

Section: Reliability Of Remote Exploration and Experimentation Systemmentioning

confidence: 99%

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Uncertainty Propagation through Software Dependability Models

Mishra

Trivedi

2011

2011 IEEE 22nd International Symposium on Software Reliability Engineering

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Systems in critical applications employ various hardware and software fault-tolerance techniques to ensure high dependability. Stochastic models are often used to analyze the dependability of these systems and assess the effectiveness of the fault-tolerance techniques employed. Measures like performance and performability of systems are also analyzed using stochastic models. These models take into account randomness in various events in the system (known as aleatory uncertainty) and are solved at fixed parameter values to obtain the measures of interest. However, in real life, the parameters of the stochastic models themselves are uncertain as they are derived from a finite (limited) number of observations or are simply based on expert opinions.Solving the stochastic models at fixed values of the model input parameters result in estimates of model output metrics which do not take into account the uncertainty in model input parameters (known as epistemic uncertainty).In this research work, we address the computation of uncertainty in output metrics of stochastic models due to epistemic uncertainty in model input parameters, with a focus on dependability and performance models of current computer and communication systems. We develop an approach for propagation of epistemic uncertainty in input parameters through stochastic dependability and performance models of varying complexity, to compute the uncertainty in the model output measures. and provides more robust estimates of uncertainty in the model output metrics than previous sampling based methods.We demonstrate the applicability of the uncertainty propagation approach by applying it to analytic stochastic dependability and performance models of computer systems, ranging from simple non-state-space models with a few input parameters to large state-space models and even hierarchical models with more than fifty input parameters. We further apply the uncertainty propagation approach to stochastic models with not only analytic or analytic-numeric solutions but also those with simulative solutions. We also consider a wide range of model output metrics including reliability and availability of computer systems, response time of a web service, capacity oriented availability of a communication system, security (probability of successful attack) of a network routing session, expected number of jobs in a queueing system with breakdown and repair of servers and call handoff probability of a cellular wireless communication cell.

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Section: Reliability Of Remote Exploration and Experimentation Systemmentioning

confidence: 99%

Section: Reliability Of Remote Exploration and Experimentation Systemmentioning

confidence: 99%

Uncertainty Propagation through Software Dependability Models

Mishra

Trivedi

2011

2011 IEEE 22nd International Symposium on Software Reliability Engineering

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“…An important challenge in checkpoint-based recovery is ensuring that the checkpoint is taken before the state has been corrupted [56]. Another challenge is deciding whether to checkpoint transparently, in which case recovery rarely suceeds for generic applications [35], or non-transparently, in which case source code modifications are required.…”

Section: Recoverymentioning

confidence: 99%

Autonomous recovery in componentized Internet applications

et al. 2006

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In this paper we show how to reduce downtime of J2EE applications by rapidly and automatically recovering from transient and intermittent software failures, without requiring application modifications. Our prototype combines three applicationagnostic techniques: macroanalysis for fault detection and localization, microrebooting for rapid recovery, and external management of recovery actions. The individual techniques are autonomous and work across a wide range of componentized Internet applications, making them well-suited to the rapidly changing software of Internet services.The proposed framework has been integrated with JBoss, an open-source J2EE application server. Our prototype provides an execution platform that can automatically recover J2EE applications within seconds of the manifestation of a fault. Our system can provide a subset of a system's active end users with the illusion of continuous uptime, in spite of failures occurring behind the scenes, even when there is no functional redundancy in the system.

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“…An FTM-application correlated failure. The error injections also revealed a correlated failure in which the FTM failure caused the application to restart in 2 of the 178 runs (see [32] for description of correlated failure scenarios).…”

Section: Application Recoverymentioning

confidence: 99%

“…More details on the applications and the full dependability analysis can be found in [31] and [32], respectively.…”

Section: Fault/error Injection Characterization Of the Sift Environmementioning

confidence: 99%

Measurement-Based Analysis of System Dependability Using Fault Injection and Field Failure Data

Iyer

Kalbarczyk

2002

Performance Evaluation of Complex Systems: Techniques and Tools

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Abstract. The discussion in this paper focuses on the issues involved in analyzing the availability of networked systems using fault injection and the failure data collected by the logging mechanisms built into the system. In particular we address: (1) analysis in the prototype phase using physical fault injection to an actual system. We use example of fault injection-based evaluation of a software-implemented fault tolerance (SIFT) environment (built around a set of self-checking processes called ARMORS) that provides error detection and recovery services to spaceborne scientific applications and (2) measurement-based analysis of systems in the field. We use example of LAN of Windows NT based computers to present methods for collecting and analyzing failure data to characterize network system dependability. Both, fault injection and failure data analysis enable us to study naturally occurring errors and to provide feedback to system designers on potential availability bottlenecks. For example, the study of failures in a network of Windows NT machines reveals that most of the problems that lead to reboots are software related and that though the average availability evaluates to over 99%, a typical machine, on average, provides acceptable service only about 92% of the time.

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An experimental evaluation of the REE SIFT environment for spaceborne applications

Cited by 18 publications

References 31 publications

Uncertainty Propagation through Software Dependability Models

Uncertainty Propagation through Software Dependability Models

Autonomous recovery in componentized Internet applications

Measurement-Based Analysis of System Dependability Using Fault Injection and Field Failure Data

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