Redundancy and Aging of Efficient Multidimensional MDS-Parity Protected Distributed Storage Systems

The effect of redundancy on the aging of an efficient Maximum Distance Separable (MDS) parity--protected distributed storage system that consists of multidimensional arrays of storage units is explored. In light of the experimental evidences and survey data, this paper develops generalized expressions for the reliability of array storage systems based on more realistic time to failure distributions such as Weibull. For instance, a distributed disk array system is considered in which the array components are disseminated across the network and are subject to independent failure rates. Based on such, generalized closed form hazard rate expressions are derived. These expressions are extended to estimate the asymptotical reliability behavior of large scale storage networks equipped with MDS parity-based protection. Unlike previous studies, a generic hazard rate function is assumed, a generic MDS code for parity generation is used, and an evaluation of the implications of adjustable redundancy level for an efficient distributed storage system is presented. Results of this study are applicable to any erasure correction code as long as it is accompanied with a suitable structure and an appropriate encoding/decoding algorithm such that the MDS property is maintained.Comment: 11 pages, 6 figures, Accepted for publication in IEEE Transactions on Device and Materials Reliability (TDMR), Nov. 201

HEC: Collaborative Research: SAM^2 Toolkit: Scalable and Adaptive Metadata Management for High-End Computing

The increasing demand for Exa-byte-scale storage capacity by high end computing applications requires a higher level of scalability and dependability than that provided by current file and storage systems. The proposal deals with file systems research for metadata management of scalable cluster-based parallel and distributed file storage systems in the HEC environment. It aims to develop a scalable and adaptive metadata management (SAM2) toolkit to extend features of and fully leverage the peak performance promised by state-of-the-art cluster-based parallel and distributed file storage systems used by the high performance computing community. There is a large body of research on data movement and management scaling, however, the need to scale up the attributes of cluster-based file systems and I/O, that is, metadata, has been underestimated. An understanding of the characteristics of metadata traffic, and an application of proper load-balancing, caching, prefetching and grouping mechanisms to perform metadata management correspondingly, will lead to a high scalability. It is anticipated that by appropriately plugging the scalable and adaptive metadata management components into the state-of-the-art cluster-based parallel and distributed file storage systems one could potentially increase the performance of applications and file systems, and help translate the promise and potential of high peak performance of such systems to real application performance improvements. The project involves the following components: 1. Develop multi-variable forecasting models to analyze and predict file metadata access patterns. 2. Develop scalable and adaptive file name mapping schemes using the duplicative Bloom filter array technique to enforce load balance and increase scalability 3. Develop decentralized, locality-aware metadata grouping schemes to facilitate the bulk metadata operations such as prefetching. 4. Develop an adaptive cache coherence protocol using a distributed shared object model for client-side and server-side metadata caching. 5. Prototype the SAM2 components into the state-of-the-art parallel virtual file system PVFS2 and a distributed storage data caching system, set up an experimental framework for a DOE CMS Tier 2 site at University of Nebraska-Lincoln and conduct benchmark, evaluation and validation studies

JOR: A Journal-guided Reconstruction Optimization for RAID-Structured Storage Systems

This paper proposes a simple and practical RAID reconstruction optimization scheme, called JOurnal-guided Reconstruction (JOR). JOR exploits the fact that significant portions of data blocks in typical disk arrays are unused. JOR monitors the storage space utilization status at the block level to guide the reconstruction process so that only failed data on the used stripes is recovered to the spare disk. In JOR, data consistency is ensured by the requirement that all blocks in a disk array be initialized to zero (written with value zero) during synchronization while all blocks in the spare disk also be initialized to zero in the background. JOR can be easily incorporated into any existing reconstruction approach to optimize it, because the former is independent of and orthogonal to the latter. Experimental results obtained from our JOR prototype implementation demonstrate that JOR reduces reconstruction times of two state-of-the-art reconstruction schemes by an amount that is approximately proportional to the percentage of unused storage space while ensuring data consistency

Impliance: A Next Generation Information Management Appliance

ably successful in building a large market and adapting to the changes of the last three decades, its impact on the broader market of information management is surprisingly limited. If we were to design an information management system from scratch, based upon today's requirements and hardware capabilities, would it look anything like today's database systems?" In this paper, we introduce Impliance, a next-generation information management system consisting of hardware and software components integrated to form an easy-to-administer appliance that can store, retrieve, and analyze all types of structured, semi-structured, and unstructured information. We first summarize the trends that will shape information management for the foreseeable future. Those trends imply three major requirements for Impliance: (1) to be able to store, manage, and uniformly query all data, not just structured records; (2) to be able to scale out as the volume of this data grows; and (3) to be simple and robust in operation. We then describe four key ideas that are uniquely combined in Impliance to address these requirements, namely the ideas of: (a) integrating software and off-the-shelf hardware into a generic information appliance; (b) automatically discovering, organizing, and managing all data - unstructured as well as structured - in a uniform way; (c) achieving scale-out by exploiting simple, massive parallel processing, and (d) virtualizing compute and storage resources to unify, simplify, and streamline the management of Impliance. Impliance is an ambitious, long-term effort to define simpler, more robust, and more scalable information systems for tomorrow's enterprises.Comment: This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but, you must attribute the work to the author and CIDR 2007. 3rd Biennial Conference on Innovative Data Systems Research (CIDR) January 710, 2007, Asilomar, California, US