Zebra is a network file system that increases throughput by striping file data across multiple servers. Rather than striping each file separately, Zebra forms all the new data from each client into a single stream, which it then stripes using an approach similar to a log-structured file system. This provides high performance for writes of small files as well as for reads and writes of large files. Zebra also writes parity information in each stripe in the style of RAID disk arrays; this increases storage costs slightly but allows the system to continue operation even while a single storage server is unavailable. A prototype implementation of Zebra, built in the Sprite operating system, provides 4-5 times the throughput of the standard Sprite file system or NFS for large files and a 15 % to 300 % improvement for writing small files. 1

Decision support systems (DSS) and data warehousing workloads comprise an increasing fraction of the database market today. I/O capacity and associated processing requirements for DSS workloads are increasing at a rapid rate, doubling roughly every nine to twelve months [38]. In response to this increasing storage and computational demand, we present a computer architecture for decision support database servers that utilizes "intelligent" disks (IDISKs). IDISKs utilize low-cost embedded general-purpose processing, main memory, and high-speed serial communication links on each disk. IDISKs are connected to each other via these serial links and high-speed crossbar switches, overcoming the I/O bus bottleneck of conventional systems. By off-loading computation from expensive desktop processors, IDISK systems may improve cost-performance. More importantly, the IDISK architecture allows the processing of the system to scale with increasing storage demand.

This paper presents the TickerTAIP architecture and an evaluation of its behavior. We demonstrate the feasibility by an existence proof; describe a family of distributed algorithms for RAID parity calculation; discuss techniques for establishing request atomicity, sequencing and recovery; and provide a performance evaluation of the TickerTAIP design space in both absolute terms and by comparison to a centralized RAID implementation. We conclude that the TickerTAIP architectural approach is feasible, useful, and effective. *Princeton University, Princeton, NJ, **University of Illinois, Urbana-Champaign, IL, ***University of Wisconsin, Madison, WI Also published as Operating Systems Research Department report HPL-OSR-92-6 1

The functionality and performance innovations in file systems and storage systems have proceeded largely independently from each other over the past years. The result is an information gap: neither has information about how the other is designed or implemented, which can result in a high cost of maintenance, poor performance, duplication of features, and limitations on functionality. To bridge this gap, we introduce and evaluate a new division of labor between the storage system and the file system. We develop an enhanced storage layer known as Exposed RAID (ERAID), which reveals information to file systems built above; specifically, ERAID exports the parallelism and failure-isolation boundaries of the storage layer, and tracks performance and failure characteristics on a fine-grained basis. To take advantage of the information made available by ERAID, we develop an Informed Log-Structured File System (ILFS). ILFS is an extension of the standard logstructured file system (LFS) that has been altered to take advantage of the performance and failure information exposed by ERAID. Experiments reveal that our prototype implementation yields benefits in the management, flexibility, reliability, and performance of the storage system, with only a small increase in file system complexity. For example, ILFS/ERAID can incorporate new disks into the system on-the-fly, dynamically balance workloads across the disks of the system, allow for user control of file replication, and delay replication of files for increased performance. Much of this functionality would be difficult or impossible to implement with the traditional division of labor between file systems and storage.

A Federated Array of Bricks (FAB) is a logical disk system that provides the reliability and performance of enterprise-class disk arrays, at a fraction of the cost and with better scalability. The unit of deployment in FAB is a brick, a small rack-mounted storage appliance built from commodity components including disks, a CPU, NVRAM, and network cards. Bricks federate themselves in a completely decentralized manner to provide users with a set of logical volumes. This paper motivates FAB and introduces our data replication algorithm based on majority-voting. We argue that majority voting is practical for ultra-reliable, high-throughput storage systems like FAB, and present several techniques that improve both the performance and space overhead of our protocol.

Growth and usage trends for large decision support databases indicate that there is a need for architectures that scale the processing power as the dataset grows. To meet this need, several researchers have recently proposed Active Disk architectures which integrate substantial processing power and memory into disk units. In this paper, we evaluate Active Disks for decision support databases. First, we compare the performance of Active Disks with that of existing scalable server architectures: SMP-based conventional disk farms and commodity clusters of PCs. Second, we evaluate the impact of several design choices on the performance of Active Disks. We focus on the performance impact of interconnect bandwidth, amount of disk memory and disk-to-disk communication architecture on decision support workloads. Our results show that for identical disks, number of processors and I/O interconnect, Active Disks provide better price/performance than both SMP-based conventional disk farms and commo...

Computer Architecture Support for Database Applications by Kimberly Kristine Keeton Doctor of Philosophy in Computer Science University of California at Berkeley Professor David A. Patterson, Chair Database workloads are an important class of applications, responsible for one-third of the symmetric multiprocessor (SMP) server market. Despite their importance, they are seldom used in computer architecture performance evaluations, which favor technical applications, such as SPEC. Database applications are often avoided because they are difficult to study in fully-scaled configurations, for reasons including large hardware requirements and complicated software configuration and tuning issues. This dissertation addresses several of the challenges posed by database workloads. First, we characterize the architectural behavior of two standard database workloads, namely online transaction processing (OLTP) and decision support (DSS), running on a commercial database on a commodity Intel...

A network storage service is a long term data repository in a Distributed Computing Environment (DCE) through which users may collaborate and share information resources. Although distributed file systems are today's state-of-the-art network storage service, they have basic design assumptions that limit their applicability to newly emerged data types and applications. Continuous media, such as digital video and audio, are the latest members of information media being used in a DCE and have to be accommodated by a network storage service. There is also a growing need to store structured data, such as multi-media documents. Structured data objects may contain component objects that are stored separately. These types of data are radically different from conventional unstructured data, such as text and binary, that contemporary distributed file systems are built to support. Furthermore, clients of a network storage service, such as a persistent programming system, may provide functional en...

this report we discuss the strategic directions and challenges in the management and use of storage systems---those components of computer systems responsible for the storage and retrieval of data. Typical large-scale storage systems include the following components of secondary and tertiary storage:

We discuss the strategic directions and challenges in the management and use of storage systems -- those components of computer systems responsible for the storage and retrieval of data. The performance gap between main and secondary memories shows no imminent sign of vanishing, and thus continuing research into storage I/O will be essential to reap the full benefit from the advances occurring in many other areas of computer science. In this report we identify a few strategic research goals and possible thrusts to meet those goals.