With the advent of pervasive cloud computing technologies, service reliability and availability are becoming major concerns,especially as we start to integrate cyber-physical systems with the cloud networks. A number of smart and connected community systems such as emergency response systems utilize cloud networks to analyze real-time data streams and provide context-sensitive decision support.Improving overall system reliability requires us to study all the aspects of the end-to-end of this distributed system,including the backend data servers. In this paper, we describe a bi-layered prognostic architecture for predicting the Remaining Useful Life (RUL) of components of backend servers,especially those that are subjected to degradation. We show that our architecture is especially good at predicting the remaining useful life of hard disks. A Deep LSTM Network is used as the backbone of this fast, data-driven decision framework and dynamically captures the pattern of the incoming data. In the article, we discuss the architecture of the neural network and describe the mechanisms to choose the various hyper-parameters. We describe the challenges faced in extracting effective training sets from highly unorganized and class-imbalanced big data and establish methods for online predictions with extensive data pre-processing, feature extraction and validation through test sets with unknown remaining useful lives of the hard disks. Our algorithm performs especially well in predicting RUL near the critical zone of a device approaching failure.The proposed architecture is able to predict whether a disk is going to fail in next ten days with an average precision of 0.8435.In future, we will extend this architecture to learn and predict the RUL of the edge devices in the end-to-end distributed systems of smart communities, taking into consideration context-sensitive external features such as weather.
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 wearout 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.