Understanding large system failures-a fault injection experiment

Chillarege, Ram; Bowen, N.S.

doi:10.1109/ftcs.1989.105592

Cited by 112 publications

(31 citation statements)

References 6 publications

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“…Failure acceleration occurs when the fault → error → failure process is accelerated, by decreasing the fault and error latencies, and increasing the probability that a fault causes a failure [Chillarege and Bowen, 1989]. This makes experiments faster to perform and allows for estimations of the transition probabilities (fault → error and error → failure), which is typically not possible from field data (which focus mostly on failures).…”

Section: Fault Injectionmentioning

confidence: 99%

“…Logs are sent and stored on a different machine (the Host Computer) and a minimal set of configuration information is stored in flash memory. This process is conservative and common for fault injection experiments, e.g., [Chillarege and Bowen, 1989;Gu et al, 2003]. However, the restart before for each injection incurs a substantial run-time overhead when errors are masked or overwritten.…”

Section: Injection Setupmentioning

confidence: 99%

“…used a five grade scale for their robustness benchmark [Siewiorek et al, 1993]. Many fault injection tools have used similar scales as well, like MAFALDA , NFTAPE [Gu et al, 2003] and others, for instance [Chillarege and Bowen, 1989;Barton et al, 1990;Marsden and Fabre, 2001;Durães and Madeira, 2006]. As a representative example of failure modes defined from an application perspective, the CRASH severity scale presented in [Koopman et al, 1997] is shown in Table 5.1.…”

Section: Failure Mode Analysismentioning

confidence: 99%

“…However, this is not the case in this thesis. In general, one wants to achieve an acceleration of the fault → error → failure process [Chillarege and Bowen, 1989].…”

Section: Pre-profilingmentioning

confidence: 99%

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Robustness Evaluation of Operating Systems

Johansson

2008

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SummaryThe premise behind this thesis is the observation that Operating Systems (OS), being the foundation behind operations of computing systems, are complex entities and also subject to failures. Consequently, when they do fail, the impact is the loss of system service and the applications running thereon. While a multitude of sources for OS failures exist, device drivers are often identified as a prominent cause behind failures.In order to characterize the impact of driver failures, at both the OS and application levels, this thesis develops a framework for error propagation-based robustness profiling for an OS. The framework is first developed conceptually and then experimentally validated on a real OS, namely Windows CE .Net. The choice of Windows CE is driven by its representativeness for a multitude of OS's, as well as the ability to customize the OS components for particular needs.For experimental validation, fault injection is a prominent technique that can be used to simulate faults (or errors) in the system by inserting synthetic ones and study their effect. Three key questions with such a technique are where, what and when to inject faults. This thesis shows how injecting errors at the interface between drivers and the OS can be very effective in evaluating the effects driver faults can have.To quantify the OS's robustness, this thesis defines a series of error propagation measures, specifically tailored for device drivers. These measures allow for quantifying and comparing both individual services and device drivers on their susceptibility and diffusing abilities.This thesis compares three contemporary error models on their suitability for robustness evaluation. The classical bit-flip model is found to identify a higher number of severe failures in the system. It also identifies failures for more services than both other models, data type and fuzzing. However, its main drawback is that it requires substantially more injections than the other two. Fuzzing, even though not giving rise to as many failures is able to find new additional services with severe failures.A careful study of the injections performed with the bit-flip model shows that only a few bits are generally useful for identifying new services with robustness weaknesses. Consequently, a new composite model is proposed, combining the most effective bits of the bit-flip model with the fuzzing model's ability to identify new services, giving rise to new model without loss of important information and at the same time incurring a moderate number of injections.To answer the question of when to inject an error this thesis proposes a novel model of a driver's usage profile, focusing on high-level operations being carried out. It guides the injection of errors to instances when the driver is carrying out specific operations. Results from extensive fault injection experiments show that more service vulnerabilities can be discovered. Furthermore, a priori profiling of the drivers can show how effective the proposed approach will be. vi

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Section: Fault Injectionmentioning

confidence: 99%

Section: Injection Setupmentioning

confidence: 99%

Section: Failure Mode Analysismentioning

confidence: 99%

“…However, this is not the case in this thesis. In general, one wants to achieve an acceleration of the fault → error → failure process [Chillarege and Bowen, 1989].…”

Section: Pre-profilingmentioning

confidence: 99%

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Robustness Evaluation of Operating Systems

Johansson

2008

Information Assurance

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show abstract

“…In [Mitr88], this technique was extended to a shared-memory multiprocessing system and used to calculate the risk of encountering multiple latent errors. A failure acceleration method for determining fault detection characteristics is discussed in [Chil89]. Because this study used periodic sampling, the discovery times of only per manent faults could be measured.…”

Section: Latency Studiesmentioning

confidence: 99%