Enterprise storage systems are facing enormous challenges due to increasing growth and heterogeneity of the data stored. Designing future storage systems requires comprehensive insights that existing trace analysis methods are ill-equipped to supply. In this paper, we seek to provide such insights by using a new methodology that leverages an objective, multi-dimensional statistical technique to extract data access patterns from network storage system traces. We apply our method on two large-scale real-world production network storage system traces to obtain comprehensive access patterns and design insights at user, application, file, and directory levels. We derive simple, easily implementable, threshold-based design optimizations that enable efficient data placement and capacity optimization strategies for servers, consolidation policies for clients, and improved caching performance for both.

An important threat to reliable storage of data is silent data corruption. In order to develop suitable protection mechanisms against data corruption, it is essential to understand its characteristics. In this article, we present the first large-scale study of data corruption. We analyze corruption instances recorded in production storage systems containing a total of 1.53 million disk drives, over a period of 41 months. We study three classes of corruption: checksum mismatches, identity discrepancies, and parity inconsistencies. We focus on checksum mismatches since they occur the most. We find more than 400,000 instances of checksum mismatches over the 41-month period. We find many interesting trends among these instances, including: (i) nearline disks (and their adapters) develop checksum mismatches an order of magnitude more often than enterprise-class disk drives, (ii) checksum mismatches within the same disk are not independent events and they show high spatial and temporal locality, and (iii) checksum mismatches across different disks in the same storage system are not independent. We use our observations to derive lessons for corruption-proof system design.

The reliability measures in today's disk drive-based storage systems focus predominantly on protecting against complete disk failures. Previous disk reliability studies have analyzed empirical data in an attempt to better understand and predict disk failure rates. Yet, very little is known about the incidence of latent sector errors i.e., errors that go undetected until the corresponding disk sectors are accessed.

A fault-scalable service can be configured to tolerate increasing numbers of faults without significant decreases in performance. The Query/Update (Q/U) protocol is a new tool that enables construction of fault-scalable Byzantine fault-tolerant services. The optimistic quorum-based nature of the Q/U protocol allows it to provide better throughput and fault-scalability than replicated state machines using agreement-based protocols. A prototype service built using the Q/U protocol outperforms the same service built using a popular replicated state machine implementation at all system sizes in experiments that permit an optimistic execution. Moreover, the performance of the Q/U protocol decreases by only 36% as the number of Byzantine faults tolerated increases from one to five, whereas the performance of the replicated state machine decreases by 83%.

The focus of this work is to determine how to enhance storage systems to make search and indexing faster and better able to produce relevant answers. Enterprise search engines often run in appliances that must access the file system through standard network file system protocols (NFS, CIFS). As such, they are not able to take advantage of features that may be offered by the storage system. This work explores the types of APIs that a storage system can expose to a search engine to better enable it to do its job. We make the case that by exposing certain information we can make search faster and more relevant.

Verification of write operations is a crucial component of Byzantine fault-tolerant consistency protocols for storage. Lazy verification shifts this work out of the critical path of client operations. This shift enables the system to amortize verification effort over multiple operations, to perform verification during otherwise idle time, and to have only a subset of storage-nodes perform verification. This paper introduces lazy verification and describes implementation techniques for exploiting its potential. Measurements of lazy verification in a Byzantine fault-tolerant distributed storage system show that the cost of verification can be hidden from both the client read and write operation in workloads with idle periods. Furthermore, in workloads without idle periods, lazy verification amortizes the cost of verification over many versions and so provides a factor of four higher write bandwidth when compared to performing verification during each write operation.

This paper describes a decentralized consistency protocol for survivable storage that exploits local data versioning within each storage-node. Such versioning enables the protocol to efficiently provide linearizability and wait-freedom of read and write operations to erasure-coded data in asynchronous environments with Byzantine failures of clients and servers. By exploiting versioning storage-nodes, the protocol shifts most work to clients and allows highly optimistic operation: reads occur in a single round-trip unless clients observe concurrency or write failures. Measurements of a storage system prototype using this protocol show that it scales well with the number of failures tolerated, and its performance compares favorably with an efficient implementation of Byzantine-tolerant state machine replication.