0000000000774978
AUTHOR
Sascha Effert
Random Slicing: Efficient and Scalable Data Placement for Large-Scale Storage Systems
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…
Design of an exact data deduplication cluster
Data deduplication is an important component of enterprise storage environments. The throughput and capacity limitations of single node solutions have led to the development of clustered deduplication systems. Most implemented clustered inline solutions are trading deduplication ratio versus performance and are willing to miss opportunities to detect redundant data, which a single node system would detect. We present an inline deduplication cluster with a joint distributed chunk index, which is able to detect as much redundancy as a single node solution. The use of locality and load balancing paradigms enables the nodes to minimize information exchange. Therefore, we are able to show that, …