Unix’s lack of a robust and expandable file system has become a significant problem with the growth of UNIX in large commercial environments. The HAMFS (Highly Available Multi-server File System) is a cluster file system designed to address this need. HAMFS offers disk-pooling, supports off-the-shelf disks, and automatically balances file load across disks dynamically. Data residing in a disk pool is directly accessible from every node in a HAMFS cluster. As user’s capacity requirements grow, HAMFS provides easy disk pool expansion. Finally, HAMFS provides uniform scaling of file system performance from a single node configuration to large multi-node clusters, offering significant performance advantage over traditional file systems. For example, in short file access situations, HAMFS provide a factor of five performance improvement over NFS, and a factor of two improvement over conventional local file systems. Technologies developed for HAMFS are applied to Fujitsu’s file system product SafeFILE. 1.

Data grids are middleware systems that offer secure shared storage of massive scientific datasets over wide area networks. Main challenge in their design is to provide reliable storage, search and transfer of numerous or large files over geographically dispersed heterogeneous platforms. The Storage Resource Broker (SRB) is an example of such a system that has been deployed in multiple high-performance scientific projects during the past few years. In the present paper, we take a detailed look at several of its functional features, and examine its scalability using synthetic and tracebased workloads. Unlike traditional file systems, SRB uses a commodity database to manage both system and user-defined metadata. We quantitatively evaluate this decision, and draw insightful conclusions about its implications to the system architecture and performance characteristics. We find that the bulk transfer facilities of SRB demonstrate good scalability properties, and we identify the bottleneck resources across different data search and transfer tasks. We examine the sensitivity to several configuration parameters, and provide details about how different internal operations contribute to the overall performance.

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Abstract — The XML and XQuery language capabilities of modern databases can provide a powerful and flexible method of data management within a mass storage system. Within the High Performance Storage System (HPSS), the implementation of XML and XQuery capabilities for user metadata is called “Userdefined Attributes ” or UDA. The UDA feature provides a method for user applications to associate arbitrary metadata with HPSS namespace objects and store it in an organized, scalable, and searchable manner using XML. The implementation includes a simple key-value interface as well as exposure of the database’s XQuery interface to allow for highly customized and atomic update, retrieval, and namespace-wide search requests. Using this architecture enables HPSS to provide client applications a high degree of flexibility in the storage, management, and access of user-defined metadata. This paper describes a brief history of data management within HPSS as well as the architectural decisions, implementation, and results of the UDA feature. Also discussed are considerations for planning and management of the UDA feature, current and in development UDA solutions created by developers and customers, and possible future data management work within HPSS. data management; xml; xquery; HPSS; user-defined attributes; extended attributes I.