0000000000185711
AUTHOR
Ivan Popov
showing 3 related works from this author
Towards Dynamic Scripted pNFS Layouts
2012
Today's network file systems consist of a variety of complex subprotocols and backend storage classes. The data is typically spread over multiple data servers to achieve higher levels of performance and reliability. A metadata server is responsible for creating the mapping of a file to these data servers. It is hard to map application specific access patterns to storage system specific features, which can result in a degraded IO performance. We present an NFSv4.1/pNFS protocol extension that integrates the client's ability to provide hints and I/O advices to metadata servers. We define multiple storage classes and allow the client to choose which type of storage fits best for its desired ac…
On the Influence of PRNGs on Data Distribution
2012
The amount of digital information produced grows rapidly and constantly. Storage systems use clustered architectures designed to store and process this information efficiently. Their use introduces new challenges in storage systems development, like load-balancing and data distribution. A variety of randomized solutions handling data placement issues have been proposed and utilized. However, to the best of our knowledge, there has not yet been a structured analysis of the influence of pseudo random number generators (PRNGs) on the data distribution. In the first part of this paper we consider Consistent Hashing [1] as a combination of two consecutive phases: distribution of bins and distrib…
Random Slicing: Efficient and Scalable Data Placement for Large-Scale Storage Systems
2014
The ever-growing amount of data requires highly scalable storage solutions. The most flexible approach is to use storage pools that can be expanded and scaled down by adding or removing storage devices. To make this approach usable, it is necessary to provide a solution to locate data items in such a dynamic environment. This article presents and evaluates the Random Slicing strategy, which incorporates lessons learned from table-based, rule-based, and pseudo-randomized hashing strategies and is able to provide a simple and efficient strategy that scales up to handle exascale data. Random Slicing keeps a small table with information about previous storage system insert and remove operations…