Disk Failures in the Real World: What Does an MTTF of 1, 000, 000 Hours Mean to You?

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. 1 Motivation Despite major efforts, both in industry and in academia, high reliability remains a major challenge in running large-scale IT systems, and disaster prevention and cost of actual disasters make up a large fraction of the total cost of ownership. With ever larger server clusters, maintaining high levels of reliability and availability is a growing problem for many sites, including high-performance computing systems and internet service providers. A particularly big concern is the reliability of storage systems, for several reasons. First, failure of storage can not only cause temporary data unavailability, but in the worst case it can lead to permanent data loss. Second, technology trends and market forces may combine to make storage system failures occur more frequently in the future [24]. Finally, the size of storage systems in modern, large-scale IT installations has grown to an unprecedented scale with thousands of storage devices, making component failures the norm rather than the exception [7]. Large-scale IT systems, therefore, need better system design and management to cope with more frequent failures. One might expect increasing levels of redundancy designed for specific failure modes [3, 7], for example. Such designs and management systems are based on very simple models of component failure and repair processes [22]. Better knowledge about the statistical properties of storage failure processes, such as the distribution of time between failures, may empower researchers and designers to develop new, more reliable and available storage systems. Unfortunately, many aspects of disk failures in real systems are not well understood, probably because the owners of such systems are reluctant to release failure data or do not gather such data. As a result, practitioners usually rely on vendor specified parameters, such as mean-time-to-failure (MTTF), to model failure processes, although many are skeptical of the accuracy of In FAST'07: 5th USENIX Conference on File and Storage Technologies, San Jose, CA, Feb. 14-16, 2007.