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Advanced prefetching and caching of models with PrefetchML

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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 prefetching 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 plug-in.

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Notes

  1. https://wiki.eclipse.org/CDO/Tweaking_Performance.

  2. http://www.eclipse.org/modeling/mdt/?project=ocl.

  3. https://github.com/FTSRG/trainbenchmark.

  4. Details of the queries can be found at https://github.com/atlanmod/Prefetching_Caching_DSL.

  5. Note that this previous experiment embedded a primitive version of the Adaptative Monitoring component that was able to disable a rule if it did not generate any hit.

  6. Time-based monitoring is not considered in this evaluation.

  7. https://www.eclipse.org/emf/compare/.

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Acknowledgements

This work has been partially funded by the Electronic Component Systems for European Leadership Joint Undertaking under Grant Agreement No. 737494 (MegaM@Rt2 project) and the Spanish government (TIN2016-75944-R project).

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Authors and Affiliations

  1. AtlanMod Team - Inria, IMT-Atlantique & LS2N, 4, Rue Alfred Kastler, Nantes, France

    Gwendal Daniel & Gerson Sunyé

  2. ICREA, UOC, Av. Carl Friedrich Gauss, 5, Castelldefels, Spain

    Jordi Cabot

Authors
  1. Gwendal Daniel
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  2. Gerson Sunyé
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  3. Jordi Cabot
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Correspondence to Gwendal Daniel.

Additional information

Communicated by Dr. Jörg Kienzle and Alexander Pretschner.

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Daniel, G., Sunyé, G. & Cabot, J. Advanced prefetching and caching of models with PrefetchML. Softw Syst Model 18, 1773–1794 (2019). https://doi.org/10.1007/s10270-018-0671-8

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