IRON FILE SYSTEMSVijayan Prabhakaran Disk drives are widely used as a primary medium for storing information.While commodity file systems trust disks to either work or fail completely, modern disks exhibit complex failure modes such as latent sector faults and block corrup-tions, where only portions of a disk fail.

We propose and evaluate the concept of a semantically-smart disk system (SDS). As opposed to a traditional "smart" disk, an SDS has detailed knowledge of how the file system above is using the disk system, including information about the on-disk data structures of the file system. An SDS exploits this knowledge to transparently improve performance or enhance functionality beneath a standard block read/write interface. To automatically acquire this knowledge, we introduce a tool (EOF) that can discover file-system structure for certain types of file systems, and then show how an SDS can exploit this knowledge on-line to understand file-system behavior. We quantify the space and time overheads that are common in an SDS, showing that they are not excessive. We then study the issues surrounding SDS construction by designing and implementing a number of prototypes as case studies; each case study exploits knowledge of some aspect of the file system to implement powerful functionality beneath the standard SCSI interface. Overall, we find that a surprising amount of functionality can be embedded within an SDS, hinting at a future where disk manufacturers can compete on enhanced functionality and not simply cost-per-byte and performance.

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. Our study analyzes data collected from production storage systems over 32 months across 1.53 million disks (both nearline and enterprise class). We analyze factors that impact latent sector errors, observe trends, and explore their implications on the design of reliability mechanisms in storage systems. To the best of our knowledge, this is the first study of such large scale – our sample size is at least an order of magnitude larger than previously published studies – and the first one to focus specifically on latent sector errors and their implications on the design and reliability of storage systems.

We propose an I/O classification architecture to close the widening semantic gap between computer systems and storage systems. By classifying I/O, a computer system can request that different classes of data be handled with different storage system policies. Specifically, when a storage system is first initialized, we assign performance policies to predefined classes, such as the filesystem journal. Then, online, we include a classifier with each I/O command (e.g., SCSI), thereby allowing the storage system to enforce the associated policy for each I/O that it receives. Our immediate application is caching. We present filesystem prototypes and a database proof-of-concept that classify all disk I/O — with very little modification to the filesystem, database, and operating system. We associate caching policies with various classes (e.g., large files shall be evicted before metadata and small files), and we show that end-to-end file system performance can be improved by over a factor of two, relative to conventional caches like LRU. And caching is simply one of many possible applications. As part of our ongoing work, we are exploring other classes, policies and storage system mechanisms that can be used to improve end-to-end performance, reliability and security.

A fundamental piece of information required in intelligent storage systems is the liveness of data. We formalize the notion of liveness within storage, and present two classes of techniques for making storage systems liveness-aware. In the explicit notification approach, we present robust techniques by which a file system can impart liveness information to storage through a “free block ” command. In the implicit detection approach, we show that such information can be inferred by the storage system efficiently underneath a range of file systems, without changes to the storage interface. We demonstrate our techniques through a prototype implementation of a secure deleting disk. We find that while the explicit interface approach is desirable due to its simplicity, the implicit approach is easy to deploy and enables quick demonstration of new functionality, thus facilitating rapid migration to an explicit interface.

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 paper, 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. 1

Benchmarking is critical when evaluating performance, but is especially difficult for file and storage systems. Complex interactions between I/O devices, caches, kernel daemons, and other OS components result in behavior that is rather difficult to analyze. Moreover, systems have different features and optimizations, so no single benchmark is always suitable. The large variety of workloads that these systems experience in the real world also adds to this difficulty. In this article we survey 415 file system and storage benchmarks from 106 recent papers. We found that most popular benchmarks are flawed and many research papers do not provide a clear indication of true performance. We provide guidelines that we hope will improve future performance evaluations. To show how some widely used benchmarks can conceal or overemphasize overheads, we conducted a set of experiments. As a specific example, slowing down read operations on ext2 by a factor of 32 resulted in only a 2–5 % wall-clock slowdown in a popular compile benchmark. Finally, we discuss future work to improve file system and storage benchmarking.

We introduce a new reliability infrastructure for file systems called I/O shepherding. I/O shepherding allows a file system developer to craft nuanced reliability policies to detect and recover from a wide range of storage system failures. We incorporate shepherding into the Linux ext3 file system through a set of changes to the consistency management subsystem, layout engine, disk scheduler, and buffer cache. The resulting file system, CrookFS, enables a broad class of policies to be easily and correctly specified. We implement numerous policies, incorporating data protection techniques such as retry, parity, mirrors, checksums, sanity checks, and data structure repairs; even complex policies can be implemented in less than 100 lines of code, confirming the power and simplicity of the shepherding framework. We also demonstrate that shepherding is properly integrated, adding less than 5 % overhead to the I/O path. Categories and Subject Descriptors:

We present the notion of a type-safe disk (TSD). Unlike a traditional disk system, a TSD is aware of the pointer relationships between disk blocks that are imposed by higher layers such as the file system. A TSD utilizes this knowledge in two key ways. First, it enables active enforcement of invariants on data access based on the pointer relationships, resulting in better security and integrity. Second, it enables semantics-aware optimizations within the disk system. Through case studies, we demonstrate the benefits of TSDs and show that a TSD presents a simple yet effective general interface to build the next generation of storage systems. 1

This paper proposes and evaluates a novel dynamic data reconstruction optimization algorithm, called popularity-based multi-threaded reconstruction optimization (PRO), which allows the reconstruction process in a RAID-structured storage system to rebuild the frequently accessed areas prior to rebuilding infrequently accessed areas to exploit access locality. This approach has the salient advantage of simultaneously decreasing reconstruction time and alleviating user and system performance degradation. It can also be easily adopted in various conventional reconstruction approaches. In particular, we optimize the disk-oriented reconstruction (DOR) approach with PRO. The PRO-powered DOR is shown to induce a much earlier onset of response-time improvement and sustain a longer time span of such improvement than the original DOR. Our benchmark studies on read-only web workloads have shown that the PRO-powered DOR algorithm consistently outperforms the original DOR algorithm in the failurerecovery process in terms of user response time, with a 3.6%~23.9 % performance improvement and up to 44.7 % reconstruction time improvement simultaneously. 1.