In this paper, we propose a new paradigm for network file system design, serverless network file systems. While traditional network file systems rely on a central server machine, a serverless system utilizes workstations cooperating as peers to provide all file system services. Any machine in the system can store, cache, or control any block of data. Our approach uses this location independence, in combination with fast local area networks, to provide better performance and scalability than traditional file systems. Further, because any machine in the system can assume the responsibilities of a failed component, our serverless design also provides high availability via redundant data storage. To demonstrate our approach, we have implemented a prototype serverless network file system called xFS. Preliminary performance measurements suggest that our architecture achieves its goal of scalability. For instance, in a 32-node xFS system with 32 active clients, each client receives nearly as much read or write throughput as it would see if it were the only active client.

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

Many scientific applications that run on today’s multiprocessors, such as weather forecasting and seismic analysis, are bottlenecked by their file-I/O needs. Even if the multiprocessor is configured with sufficient I/O hardware, the file-system software often fails to provide the available bandwidth to the application. Although libraries and enhanced file-system interfaces can make a significant improvement, we believe that fundamental changes are needed in the file-server software. We propose a new technique, disk-directed I/O, to allow the disk servers to determine the flow of data for maximum performance. Our simulations show that tremendous performance gains are possible. Indeed, disk-directed I/O provided consistent high performance that was largely independent of data distribution, obtained up to 93 % of peak disk bandwidth, and was as much as 16 times faster than traditional parallel file systems.

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Most current multiprocessor le systems are designed to use multiple disks in parallel, using the high aggregate bandwidth to meet the growing I/O requirements of parallel scienti c applications. Many multiprocessor le systems provide applications with a conventional Unix-like interface, allowing the application to access multiple disks transparently. Thisinterface conceals the parallelism within the le system, increasing the ease of programmability, but making it di cult or impossible for sophisticated programmers and libraries to use knowledge about their I/O needs to exploit that parallelism. In addition to providing an insu cient interface, most current multiprocessor le systems are optimized for a di erent workload than they are being asked to support. We introduce Galley, a new parallel le system that is intended to e ciently support realistic scienti c multiprocessor workloads. We discuss Galley's le structure and application interface, as well as the performance advantages o ered by that interface. 1

We present an I/O architecture, called Swift, that addresses the problem of data rate mismatches between the requirements of an application, storage devices, and the interconnection medium. The goal of Swift is to support high data rates in general purpose distributed systems. Swift uses a high-speed interconnection medium to provide high data rate transfers by using multiple slower storage devices in parallel. It scales well when using multiple storage devices and interconnections, and can use any appropriate storage technology, including high-performance devices such as disk arrays. To address the problem of partial failures, Swift stores data redundantly. Using the UNIX operating system, we have constructed a simplified prototype of the Swift architecture. The prototype provides data rates that are significantly faster than access to the local SCSI disk, limited by the capacity of a single Ethernet segment, or in the case of multiple Ethernet segments by the ability of the client to drive them. We have constructed a simulation model to demonstrate how the Swift architecture can exploit advances in processor, communication and storage technology. We consider the effects of processor speed, interconnection capacity, and multiple storage agents on the utilization of the components and the data rate of the system. We show that the data rates scale well in the number of storage devices, and that by replacing the most highly stressed components by more powerful ones the data rates of the entire system increase significantly.

Multiprocessors have permitted astounding increases in computational performance, but many cannot meet the intense I/O requirements of some scientific applications. An important component of any solution to this I/O bottleneck is a parallel file system that can provide high-bandwidth access to tremendous amounts of data in parallel to hundreds or thousands of processors. Most successful systems are based on a solid understanding of the expected workload, but thus far there have been no comprehensive workload characterizations of multiprocessor le systems. This paper presents the results of a three week tracing study in which all file-related activity on a massively parallel computer was recorded. Our instrumentation di ers from previous efforts in that it collects information about every I/O request and about the mix of jobs running in a production environment. We also present the results of a trace-driven caching simulation and recommendations for designers of multiprocessor file systems.

\We are developing a software system called PASSION: Parallel And Scalable Software for Input-Output which provides software support for high performance parallel I/O. PASSION provides support at the language, compiler, runtime as well as file system level. PASSION provides runtime procedures for parallel access to files (read/write), as well as for out-of-core computations. These routines can either be used together with a compiler to translate out-of-core data parallel programs written in a language like HPF, or used directly by application programmers. A number of optimizations such as Two-Phase Access, Data Sieving, Data Prefetching and Data Reuse have been incorporated in the PASSION Runtime Library for improved performance. PASSION also provides an initial framework for runtime support for out-of-core irregular problems. The goal of the PASSION compiler is to automatically translate out- of-core data parallel programs to node programs for distributed memory machines, with calls to the PASSION Runtime Library. At the language level, PASSION suggests extensions to HPF for out-of-core programs. At the file system level, PASSION provides support for buffering and prefetching data from disks. A portable parallel file system is also being developed as part of this project, which can be used across homogeneous or heterogeneous networks of workstations. PASSION also provides support for integrating data and task parallelism using parallel I/O techniques. We have used PASSION to implement a number of out-of-core applications such as a Laplace's equation solver, 2D FFT, Matrix Multiplication, LU Decomposition, image processing applications as well as unstructured mesh kernels in molecular dynamics and computational fluid dynamics. We are currently in the process of using PASSION in applications in CFD (3D turbulent flows), molecular structure calculations, seismic computations, and earth and space science applications such as Four-Dimensional Data Assimilation. PASSION is currently available on the Intel Paragon, Touchstone Delta and iPSC/860. Efforts are underway to port it to the IBM SP-1 and SP-2 using the Vesta Parallel File System.

Recently there has been a great deal of interest in prefetching from parallel disks, as a technique for enabling serial applications to improve I/O performance. Studies have also shown that for optimal performance, it is important to properly integrate prefetching and caching. In this paper, we study integrated prefetching and caching strategies for multiple disks. We present two algorithms, regular aggressive and reverse aggressive, and show that reverse aggressive is close to optimal. Using trace-driven simulation on a collection of file access traces, we evaluated these algorithms under a variety of data placement alternatives. Our results show that both algorithms can achieve near linear speedup when the load is distributed evenly on the disks, and reverse aggressive performs well even when the placement of blocks on disks distributes the load unevenly. Our simulations also show that, for file system traces, replicating data, even across all of the disks, offers little performance ...

PIOUS is a parallel file system architecture that provides cost-effective, scalable bandwidth in a network computing environment. PIOUS employs data declustering, to exploit the combined file I/O and buffer cache capacities of networked computing resources, and transaction-based concurrency control, to guarantee access consistency without explicit synchronization. This paper presents preliminary results from a prototype PIOUS implementation. 1 Introduction Parallel programming environments that exploit networked computing resources offer a cost-effective alternative to traditional parallel machines. Environments such as PVM [14] and Linda [1], among others [15], enable parallel-distributed application development by providing mechanisms for interprocess communication, synchronization and concurrency control, fault tolerance, and process management. However, many parallel applications require or could benefit from a unified parallel I/O system that such environments generally lack. PIO...