Scalable and Manageable Storage Systems
Carnegie Mellon University Ph.D Dissertation CMU-CS-00-178: December, 2000.
Khalil S. Amiri
Dept. Electical and Computer Engineering
Carnegie Mellon University
Pittsburgh, PA 15213
Emerging applications such as data warehousing, multimedia content distribution, electronic commerce and medical and satellite databases have substantial storage requirements that are growing at 3X to 5X per year. Such applications require scalable, highly-available and cost-effective storage systems. Traditional storage systems rely on a central controller (file server, disk array controller) to access storage and copy data between storage devices and clients which limits their scalability. This dissertation describes an architecture, network-attached secure disks (NASD), that eliminates the single controller bottleneck allowing throughput and bandwidth of an array to scale with increasing capacity up to the largest sizes desired in practice. NASD enables direct access from client to shared storage devices, allowing aggregate bandwidth to scale with the number of nodes. In a shared storage system, each client acts as its own storage (RAID) controller, performing all the functions required to manage redundancy and access its data. As a result, multiple controllers can be accessing and managing shared storage devices concurrently. Without proper provisions, this concurrency can corrupt redundancy codes and cause hosts to read incorrect data. This dissertation proposes a transactional approach to ensure correctness in highly concurrent storage device arrays. It proposes distributed device-based protocols that exploit trends towards increased device intelligence to ensure correctness while scaling well with system size. Emerging network-attached storage arrays consist of storage devices with excess cycles in their on-disk controllers, which can be used to execute filesystem function traditionally executed on the host. Programmable storage devices increase the flexibility in partitioning filesystem function between clients and storage devices. However, the heterogeneity in resource availability among servers, clients and network links causes optimal function partitioning to change across sites and with time. This dissertation proposes an automatic approach which allows function partitioning to be changed and optimized at run-time by relying only on the black-box monitoring of functional components and of resource availability in the storage system.