The ever-increasing volume of archival data that needs to be reliably retained for long periods of time and the decreasing costs of disk storage, memory, and processing have motivated the design of low-cost, high-efficiency disk-based storage systems. However, managed disk storage is still expensive. To further lower the cost, redundancy can be eliminated with the use of interfile and intrafile data compression. However, it is not clear what the optimal strategy for compressing data is, given the diverse collections of data. To create a scalable archival storage system that efficiently stores diverse data, we present PRESIDIO, a framework that selects from different space-reduction efficent storage methods (ESMs) to detect similarity and reduce or eliminate redundancy when storing objects. In addition, the framework uses a virtualized content addressable store (VCAS) that hides from the user the complexity of knowing which space-efficient techniques are used, including chunk-based deduplication or delta compression. Storing and retrieving objects are polymorphic operations independent of their content-based address. A new technique, harmonic super-fingerprinting, is also used for obtaining successively more accurate (but also more costly) measures of similarity to identify the existing objects in a very large data set that are most similar to an incoming new object. The PRESIDIO design, when reported earlier, had comprehensively introduced for the first time the notion of deduplication, which is now being offered as a service in storage systems by major vendors. As an aid to the design of such systems, we evaluate and present various parameters that affect the efficiency of a storage system using empirical data.

data-center environment, the administrator needs to understand the root-cause of the issue. The growing trend of system virtualization, combined with the need to support end-to-end performance goals for enterprise applications, have made root-cause analysis a nontrivial problem - administrators are required to manually parse all hardware events, configuration modifications, and changes in access characteristics, across all tiers of the IO path from application servers to the disks. We propose a framework that assists storage administrators with root-cause analysis in distributed systems. GENESIS consists of three key modules: Abnormality Detection, Snapshot Generation, and Diagnosis. The Abnormality Detection module uses clustering algorithms to create and constantly evolve the normality models of measurable parameters in components. The Snapshot Generator is triggered by a combination of abnormality detection and policies to take compact snapshots of the system state for analysis whenever a significant change occurs. The Diagnosis module parses the snapshots and shortlists the root-cause for the administrator using knowledge about the impact of the run-time changes on IO performance. We have implemented an initial proof-of-concept of GENESIS in GPFS (a high performance distributed file-system) and validated its operation for several interesting real-world scenarios. Encouraged by the results, we are currently deploying our prototype in an existing data center environment.

Inter-file compression techniques store files as sets of references to data objects or chunks that can be shared among many files. While these techniques can achieve much better compression ratios than conventional intra-file compression methods such as Lempel-Ziv compression, they also reduce the reliability of the storage system because the loss of a few critical chunks can lead to the loss of many files. We show how to eliminate this problem by choosing for each chunk a replication level that is a function of the amount of data that would be lost if that chunk were lost. Experiments using actual archival data show that our technique can achieve significantly higher robustness than a conventional approach combining data mirroring and intra-file compression while requiring about half the storage space.

We present the Deep Store archival storage architecture, a large-scale storage system that stores immutable data efficiently and reliably for long periods of time. Archived data is stored across a cluster of nodes and recorded to hard disk. The design differentiates itself from traditional file systems by eliminating redundancy within and across files, distributing content for scalability, associating rich metadata with content, and using variable levels of replication based on the importance or degree of dependency of each piece of stored data. We evaluate the foundations of our design, including PRESIDIO, a virtual content-addressable storage framework with multiple methods for interfile and intra-file compression that effectively addresses the data-dependent variability of data compression. We measure content and metadata storage efficiency, demonstrate the need for a variable-degree replication model, and provide preliminary results for storage performance.

To determine whether a user can access a file in a hierarchical file system a traversal of the directory hierarchy is required in order to check access control for all the parent directories. This traversal can be especially expensive in a distributed system where the files may be on separate devices. We present two approaches for representing the complete access control for a file and its parent directories such that it can be stored locally with each file in order to avoid traversal. We use the well-known CNF and DNF (conjunctive and disjunctive normal form) formats to store permission and ownership information compactly for the entire path to a file. An examination of the structure of an existing large shared file system demonstrates the efficacy of our solution.

In petabyte-scale distributed file systems that decouple read and write from metadata operations, behavior of the metadata server cluster will be critical to overall system performance and scalability. We present a dynamic subtree partitioning and adaptive metadata management system designed to efficiently manage hierarchical metadata workloads that evolve over time. We examine the relative merits of our approach in the context of traditional workload partitioning strategies, and demonstrate the performance, scalability and adaptability advantages in a simulation environment.