Advanced Prefetching and Caching of Models with PrefetchML

Abstract : Caching and prefetching techniques have been used for decades in database engines and file systems to improve the performance of I/O intensive application. A prefetching algorithm typically benefits from the system's latencies by loading into main memory elements that will be needed in the future, speeding-up data access. While these solutions can bring a significant improvement in terms of execution time, prefetching rules are often defined at the data-level, making them hard to understand, maintain, and optimize. In addition, low-level prefetching and caching components are difficult to align with scalable model persistence frameworks because they are unaware of potential optimizations relying on the analysis of metamodel-level information, and are less present in NoSQL databases, a common solution to store large models. To overcome this situation we propose PrefetchML, a framework that executes prefetching and caching strategies over models. Our solution embeds a DSL to configure precisely the prefetching rules to follow, and a monitoring component providing insights on how the prefetch-ing execution is working to help designers optimize his performance plans. Our experiments show that PrefetchML is a suitable solution to improve query execution time on top of scalable model persistence frameworks. Tool support is fully available online as an open-source Eclipse plugin.
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https://hal.inria.fr/hal-01725030
Contributor : Gwendal Daniel <>
Submitted on : Wednesday, March 7, 2018 - 2:34:05 AM
Last modification on : Wednesday, October 16, 2019 - 9:46:58 AM
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Gwendal Daniel, Gerson Sunyé, Jordi Cabot. Advanced Prefetching and Caching of Models with PrefetchML. Software and Systems Modeling, Springer Verlag, 2018, pp.1-35. ⟨10.1007/s10270-018-0671-8⟩. ⟨hal-01725030⟩

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