Paper Info
Title
Design implications for enterprise storage systems via multi-dimensional trace analysis
Abstract
Enterprise storage systems are facing enormous challenges due to increasing growth and heterogeneity of the data stored. Designing future storage systems requires comprehensive insights that existing trace analysis methods are ill-equipped to supply. In this paper, we seek to provide such insights by using a new methodology that leverages an objective, multi-dimensional statistical technique to extract data access patterns from network storage system traces. We apply our method on two large-scale real-world production network storage system traces to obtain comprehensive access patterns and design insights at user, application, file, and directory levels. We derive simple, easily implementable, threshold-based design optimizations that enable efficient data placement and capacity optimization strategies for servers, consolidation policies for clients, and improved caching performance for both.
Year
DOI
Venue
2011
10.1145/2043556.2043562
SOSP
Keywords
Field
DocType
large-scale real-world production network,comprehensive access pattern,design insight,network storage system trace,future storage system,multi-dimensional trace analysis,efficient data placement,comprehensive insight,design implication,storage system trace,data access pattern,enterprise storage system,design optimization,data access,storage system,scalability
Converged storage,Enterprise storage,Computer science,Directory,Server,Real-time computing,Information repository,Data access,Capacity optimization,Database,Scalability,Distributed computing
Conference
Citations 
PageRank 
References 
35
1.46
15
Authors
4
Name
Order
Citations
PageRank
Yanpei Chen191741.46
Kiran Srinivasan22299.88
Garth Goodson326210.13
Randy H. Katz4168193018.89