Object-based storage for Linux clusters

Computerworld - Linux cluster computing has transformed the architecture of high-performance computing applications. High-cost supercomputers are being replaced by low-cost Linux clusters to solve the most challenging computing problems. To complement the performance potential of these Linux compute clusters, a new storage paradigm is needed. Object-based storage clustering is the foundation for a new class of storage systems that scale in capacity and performance to meet the demands of the most powerful Linux-based clusters.

For years, high-performance cluster computing has delivered solutions to the world's most challenging technical computing problems. More recently, these successes have been replicated in high-performance commercial applications using Linux clusters. Geophysicists are developing more capable seismic-analysis techniques to create images of the Earth's substructure and guide oil-field drilling and extraction operations. Pharmaceutical companies mine massive genomic data sets to provide better insight into human disease and develop more effective therapies. And Internet portals such as Yahoo Inc. and Google Inc. index and serve the content of the Internet.

An increasing appetite for shared storage performance

In addition to hefty computational requirements, these applications are characterized by high-performance I/O needs. Rapid access to shared data sets, often multiple terabytes in size, is critical for ensuring optimal use of compute cluster assets. Without it, already scant resources sit idle. These data sets need to be made globally available to all processes executing on the compute cluster in order to simplify development and systems management activities. Traditional networked storage systems are incapable of providing the necessary performance to serve the aggressive shared-access requirements of these expanding clusters.

For example, animation-rendering applications distribute scene generation tasks to hundreds of cluster compute nodes—each generating an individual frame of the final segment. Shared-scene and character information and per-frame rendering instructions must be accessed by each of the participating compute nodes, and each node generates as much as 50MB of output per frame. The individual frames are then sequenced and assembled into their final form for review. This is a common data-access scenario across many cluster computing applications.

Shortcomings of traditional shared storage

The natural inclination of cluster computing developers is to deploy shared storage that can be accessed by all nodes in the cluster. However, standard shared-storage technologies provided by file servers built from direct-attached storage are only sufficient for small clusters. Larger clusters require more scalable storage. Storage-area networks (SAN) and optimized network-attached storage (NAS) architectures have been employed for modest-sized clusters, however, these architectures have severe limitations as clusters become larger. Neither SAN nor NAS architectures support the aggressive concurrency and high per-client throughput requirements of these cluster computing applications.