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2018 | OriginalPaper | Buchkapitel

An Adaptive Eviction Framework for Anti-caching Based In-Memory Databases

verfasst von : Kaixin Huang, Shengan Zheng, Yanyan Shen, Yanmin Zhu, Linpeng Huang

Erschienen in: Database Systems for Advanced Applications

Verlag: Springer International Publishing

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Abstract

Current in-memory DBMSs suffer from the performance bottleneck when data cannot fit in memory. To solve such a problem, anti-caching system is proposed and with proper configuration, it can achieve better performance than state-of-the-art counterpart. However, in current anti-caching eviction procedure, all the eviction parameters are fixed while real workloads keep changing from time to time. Therefore, the performance of anti-caching system can hardly stay in the best state. We propose an adaptive eviction framework for anti-caching system and implement four tuning techniques to automatically tune the eviction parameters. In particular, we design a novel tuning technique called window-size adaption specialized for anti-caching system and embed it into the adaptive eviction framework. The experimental results show that with adaptive eviction, anti-caching based database system can outperform the traditional prototype by 1.2x–1.8x and 1.7x–4.5x under TPC-C benchmark and YCSB benchmark, respectively.

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Metadaten
Titel
An Adaptive Eviction Framework for Anti-caching Based In-Memory Databases
verfasst von
Kaixin Huang
Shengan Zheng
Yanyan Shen
Yanmin Zhu
Linpeng Huang
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-91458-9_15