Linda Marie Duyanovich

IBM Storage Tank-- A heterogeneous scalable SAN file system

As the amount of data being stored in the open systems environment continues to grow, new paradigms for the attachment and management of data and the underlying storage of the data are emerging. One of the emerging technologies in this area is the storage area network (SAN). Using a SAN to connect large amounts of storage to large numbers of computers gives us the potential for new approaches to accessing, sharing, and managing our data and storage. However, existing operating systems and file systems are not built to exploit these new capabilities. IBM Storage Tankâ?¢ is a SAN-based distributed file system and storage management solution that enables many of the promises of SANs, including shared heterogeneous file access, centralized management, and enterprise-wide scalability. In addition, Storage Tank borrows policy-based storage and data management concepts from mainframe computers and makes them available in the open systems environment. This paper explores the goals of the Storage Tank project, the architecture used to achieve these goals, and the current and future plans for the technology.

A fast, highly reliable data compression chip and algorithm for storage systems

Data compression allows more efficient use of storage media and communication bandwidth, and standard compression offerings for tape storage have been well established since the late 1980s. Compression technology lowers the cost of storage without changing applications or data access methods. The desire to extend these cost/performance benefits to higher-data-rate media and broader media forms, such as DASD storage subsystems, motivated the design and development of the IBMLZ1 compression algorithm and its implementing technology. The IBMLZ1 compression algorithm was designed not only for robust and highly efficient compression, but also for extremely high reliability. Because compression removes redundancy in the source, the compressed data become extremely vulnerable to data corruption. Key design objectives for the IBMLZ1 development team were efficient hardware execution, efficient use of silicon technology, and minimum system-integration overhead. Through new observations of pattern matching, match-length distribution, and the use of graph vertex coloring for evaluating data flows, the IBMLZ1 compression algorithm and the chip family achieved the above objectives.

Archive storage system design for long-term storage of massive amounts of data

A dramatic shift is underway in how organizations use computer storage. This shift will have a profound impact on storage system design. The requirement for storage of traditional transactional data is being supplemented by the necessity to store information for long periods. In 2005, a total of 2,700 petabytes of storage was allocated worldwide for information that required long-term retention, and this amount is expected to grow to an estimated 27,200 petabytes by 2010. In this paper, we review the requirements for long-term storage of data and describe an innovative approach for developing a highly scalable and flexible archive storage system using commercial off-the-shelf (COTS) components. Such a system is expected to be capable of preserving data for decades, providing efficient policy-based management of the data, and allowing efficient search and access to data regardless of data content or location.

Security vs performance: tradeoffs using a trust framework

We present an architecture of a trust framework that can be used to intelligently tradeoff between security and performance in a SAN file system. The primary idea is to differentiate between various clients in the system based on their trustworthiness and provide them with differing levels of security and performance. Client trustworthiness reflects its expected behavior and is evaluated in an online fashion using a customizable trust model. We also describe the interface of the trust framework with an example block level security solution for an out-of-band virtualization based SAN file system (SAN FS). The proposed framework can be easily extended to provide differential treatment based on data sensitivity, using a configurable parameter of the trust model. This allows associating stringent security requirements for more sensitive data, while trading off security for better performance for less critical data, a situation regularly desired in an enterprise.