Cluster-based storage systems are popular for data-intensive applications and it is desirable yet challenging to provide incremental expansion and high availability while achieving scalability and strong consistency. This paper presents the design and implementation of a self-organizing storage cluster called Sorrento, which targets data-intensive workload with highly parallel requests and low write-sharing patterns. Sorrento automatically adapts to storage node joins and departures, and the system can be configured and maintained incrementally without interrupting its normal operation. Data location information is distributed across storage nodes using consistent hashing and the location protocol differentiates small and large data objects for access efficiency. It adopts versioning to achieve single-file serializability and replication consistency. In this paper, we present experimental results to demonstrate features and performance of Sorrento using microbenchmarks, application benchmarks, and application trace replay.

Fundamental advances in high-level storage architectures and low-level storage-device interfaces greatly improve the performance and scalability of storage systems. Specifically, the decoupling of storage control (i.e., file system policy) from datapath operations (i.e., read, write) allows client applications to leverage the readily available bandwidth of storage devices while continuing to rely on the rich semantics of today’s file systems. Further, the evolution of storage interfaces from block-based devices with no protection to object-based devices with per-command access control enables storage to become secure, first-class IP-based network citizens. This paper examines how the Panasas ActiveScale Storage Cluster leverages distributed storage and object-based devices to achieve linear scalability of storage bandwidth. Specifically, we focus on implementation issues with our Object-based Storage Device, aggregation algorithms across the collection of OSDs, and the close coupling of networking and storage to achieve scalability.