Shankar Pasupathy

An analysis of latent sector errors in disk drives

The reliability measures in todays 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 anorder 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.

An empirical study on configuration errors in commercial and open source systems

Configuration errors (i.e., misconfigurations) are among the dominant causes of system failures. Their importance has inspired many research efforts on detecting, diagnosing, and fixing misconfigurations; such research would benefit greatly from a real-world characteristic study on misconfigurations. Unfortunately, few such studies have been conducted in the past, primarily because historical misconfigurations usually have not been recorded rigorously in databases. In this work, we undertake one of the first attempts to conduct a real-world misconfiguration characteristic study. We study a total of 546 real world misconfigurations, including 309 misconfigurations from a commercial storage system deployed at thousands of customers, and 237 from four widely used open source systems (CentOS, MySQL, Apache HTTP Server, and OpenLDAP). Some of our major findings include: (1) A majority of misconfigurations (70.0%~85.5%) are due to mistakes in setting configuration parameters; however, a significant number of misconfigurations are due to compatibility issues or component configurations (i.e., not parameter-related). (2) 38.1%~53.7% of parameter mistakes are caused by illegal parameters that clearly violate some format or rules, motivating the use of an automatic configuration checker to detect these misconfigurations. (3) A significant percentage (12.2%~29.7%) of parameter-based mistakes are due to inconsistencies between different parameter values. (4) 21.7%~57.3% of the misconfigurations involve configurations external to the examined system, some even on entirely different hosts. (5) A significant portion of misconfigurations can cause hard-to-diagnose failures, such as crashes, hangs, or severe performance degradation, indicating that systems should be better-equipped to handle misconfigurations.

High-performance metadata indexing and search in petascale data storage systems

Large-scale storage systems used for scientific applications can store petabytes of data and billions of files, making the organization and management of data in these systems a difficult, time-consuming task. The ability to search file metadata in a storage system can address this problem by allowing scientists to quickly navigate experiment data and code while allowing storage administrators to gather the information they need to properly manage the system. In this paper, we present Spyglass, a file metadata search system that achieves scalability by exploiting storage system properties, providing the scalability that existing file metadata search tools lack. In doing so, Spyglass can achieve search performance up to several thousand times faster than existing database solutions. We show that Spyglass enables important functionality that can aid data management for scientists and storage administrators.

Designing a fast file system crawler with incremental differencing

Search engines for storage systems rely on crawlers to gather the list of files that need to be indexed. The recency of an index is determined by the speed at which this list can be gathered. While there has been a substantial amount of literature on building efficient web crawlers, there is very little literature on file system crawlers. In this paper we discuss the challenges in building a file system crawler. We then present the design of two file system crawlers: the first uses the standard POSIX file system API but carefully controls the amount of memory and CPU that it uses. The second leverages modifications to the file systemss internals, and a new API called SnapDiff, to detect modified files rapidly. For both crawlers we describe the incremental differencing design; the method to produce a list of changes between a previous crawl and the current point in time.

Making enterprise storage more search-friendly

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.