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2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (2018)
Washington, DC, USA
May 1, 2018 to May 4, 2018
ISBN: 978-1-5386-5815-4
pp: 203-212
ABSTRACT
Our analysis of the key-value activity generated by the ParSplice molecular dynamics simulation demonstrates the need for more complex cache management strategies. Baseline measurements show clear key access patterns and hot spots that offer significant opportunity for optimization. We use the data management language and policy engine from the Mantle system to dynamically explore a variety of techniques, ranging from basic algorithms and heuristics to statistical models, calculus, and machine learning. While Mantle was originally designed for distributed file systems, we show how the collection of abstractions effectively decomposes the problem into manageable policies for a different application and storage system. Our exploration of this space results in a dynamically sized cache policy that does not sacrifice any performance while using 32-66% less memory than the default ParSplice configuration.
INDEX TERMS
cache storage, learning (artificial intelligence)
CITATION

M. A. Sevilla et al., "Programmable Caches with a Data Management Language and Policy Engine," 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Washington, DC, USA, 2018, pp. 203-212.
doi:10.1109/CCGRID.2018.00035
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