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PrefetchML: a Framework for Prefetching and Caching Models

Abstract : Prefetching and caching are well-known techniques integrated in database engines and file systems in order to speed-up data access. They have been studied for decades and have proven their efficiency to improve the performance of I/O intensive applications. Existing solutions do not fit well with scalable model persistence frameworks because the prefetcher operates at the data level, ignoring potential optimizations based on the information available at the metamodel level. Furthermore, prefetching components are common in rela-tional databases but typically missing (or rather limited) in NoSQL databases, a common option for model storage nowadays. To overcome this situation we propose PrefetchML, a framework that executes prefetching and caching strategies over models. Our solution embeds a DSL to precisely configure the prefetching rules to follow. Our experiments show that PrefetchML provides a significant execution time speedup. Tool support is fully available online.
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Submitted on : Thursday, October 6, 2016 - 11:44:28 AM
Last modification on : Wednesday, April 27, 2022 - 4:11:19 AM
Long-term archiving on: : Saturday, January 7, 2017 - 12:10:23 PM


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Gwendal Daniel, Gerson Sunyé, Jordi Cabot. PrefetchML: a Framework for Prefetching and Caching Models. MoDELS 2016, Oct 2016, Saint-Malo, France. ⟨10.1145/2976767.2976775⟩. ⟨hal-01362149⟩



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