Component failure in large-scale IT installations is becoming an ever larger problem as the number of components in a single cluster approaches a million. In this paper, we present and analyze field-gathered disk replacement data from a number of large production systems, including high-performance computing sites and internet services sites. About 100,000 disks are covered by this data, some for an entire lifetime of five years. The data include drives with SCSI and FC, as well as SATA interfaces. The mean time to failure (MTTF) of those drives, as specified in their datasheets, ranges from 1,000,000 to 1,500,000 hours, suggesting a nominal annual failure rate of at most 0.88%. We find that in the field, annual disk replacement rates typically exceed 1%, with 2-4 % common and up to 13% observed on some systems. This suggests that field replacement is a fairly different process than one might predict based on datasheet MTTF. We also find evidence, based on records of disk replacements in the field, that failure rate is not constant with age, and that, rather than a significant infant mortality effect, we see a significant early onset of wear-out degradation. That is, replacement rates in our data grew constantly with age, an effect often assumed not to set in until after a nominal lifetime of 5 years. Interestingly, we observe little difference in replacement rates between SCSI, FC and SATA drives, potentially an indication that disk-independent factors, such as operating conditions, affect replacement rates more than component specific factors. On the other hand, we see only one instance of a customer rejecting an entire population of disks as a bad batch, in this case because of media error rates, and this instance involved SATA disks. Time between replacement, a proxy for time between failure, is not well modeled by an exponential distribution and exhibits significant levels of correlation, including autocorrelation and long-range dependence.

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This paper presents a measurement-based dependability study of a Networked Windows NT system based on field data collected from NT System Logs from 503 servers running in a production environment over a four-month period. The event logs at hand contains only system reboot information. We study individual server failures and domain behavior in order to characterize failure behavior and explore error propagation between servers. The key observations from this study are: (1) system software and hardware failures are the two major contributors to the total system downtime (22% and 10%), (2) recovery from application software failures are usually quick, (3) in many cases, more than one reboots are required to recover from a failure, (4) the average availability of an individual server is over 99%,(5) there is a strong indication of error dependency or error propagation across the network, (6) most (58%) reboots are unclassified indicating the need for better logging techniques, (7) maintenance and configuration contribute to 24% of system downtime. 1

The growing complexity of hardware and software mandates the recognition of fault occurrence in system deployment and management. While there are several techniques to prevent and/or handle faults, there continues to be a growing need for an in-depth understanding of system errors and failures and their empirical and statistical properties. This understanding can help evaluate the effectiveness of different techniques for improving system availability, in addition to developing new solutions. In this paper, we analyze the empirical and statistical properties of system errors and failures from a network of nearly 400 heterogeneous servers running a diverse workload over a year. While improvements in system robustness continue to limit the number of actual failures to a very small fraction of the recorded errors, the failure rates are significant and highly variable. Our results also show that the system error and failure patterns are comprised of time-varying behavior containing long stationary intervals. These stationary intervals exhibit various strong correlation structures and periodic patterns, which impact performance but also can be exploited to address such performance issues. 1.

With petascale computers only a year or two away there is a pressing need to anticipate and compensate for a probable increase in failure and application interruption rates. Researchers, designers and integrators have available to them far too little detailed information on the failures and interruptions that even smaller terascale computers experience. The information that is available suggests that application interruptions will become far more common in the coming decade, and the largest applications may surrender large fractions of the computer’s resources to taking checkpoints and restarting from a checkpoint after an interruption. This paper reviews sources of failure information for compute clusters and storage systems, projects failure rates and the corresponding decrease in application effectiveness, and discusses coping strategies such as application-level checkpoint compression and system level process-pairs fault-tolerance for supercomputing. The need for a public repository for detailed failure and interruption records is particularly concerning, as projections from one architectural family of machines to another are widely disputed. To this end, this paper introduces the Computer Failure Data Repository and issues a call for failure history data to publish in it. 1.

The growing computational and storage needs of several scientific applications mandate the deployment of extreme-scale parallel machines, such as IBM’s BlueGene/L which can accommodate as many as 128K processors. One of the challenges when designing and deploying these systems in a production setting is the need to take failure occurrences, whether it be in the hardware or in the software, into account. Earlier work has shown that conventional runtime faulttolerant techniques such as periodic checkpointing are not effective to the emerging systems. Instead, the ability to predict failure occurrences can help develop more effective checkpointing strategies. Failure prediction has long been regarded as a challenging research problem, mainly due to the lack of realistic failure data from actual production systems. In this study, we have collected RAS event logs from BlueGene/L over a period of more than 100 days. We have investigated the characteristics of fatal failure events, as well as the correlation between fatal events and non-fatal events. Based on the observations, we have developed three simple yet effective failure prediction methods, which can predict around 80 % of the memory and network failures, and 47 % of the application I/O failures. 1

In large-scale networked computing systems, component failures become norms instead of exceptions. Failure prediction is a crucial technique for self-managing resource burdens. Failure events in coalition systems exhibit strong correlations in time and space domain. In this paper, we develop a spherical covariance model with an adjustable timescale parameter to quantify the temporal correlation and a stochastic model to describe spatial correlation. We further utilize the information of application allocation to discover more correlations among failure instances. We cluster failure events based on their correlations and predict their future occurrences. We implemented a failure prediction framework, called PREdictor of Failure Events Correlated Temporal-Spatially (hPrefects), which explores correlations among failures and forecasts the time-between-failure of future instances. We evaluate the performance of hPrefects in both offline prediction of failure by using the Los Alamos HPC traces and online prediction in an institute-wide clusters coalition environment. Experimental results show the system achieves more than 76 % accuracy in offline prediction and more than 70 % accuracy in online prediction during the time from May 2006 to April 2007.