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.
CITATION STYLE
Huang, K., Zheng, S., Shen, Y., Zhu, Y., & Huang, L. (2018). An adaptive eviction framework for anti-caching based in-memory databases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10828 LNCS, pp. 247–263). Springer Verlag. https://doi.org/10.1007/978-3-319-91458-9_15
Mendeley helps you to discover research relevant for your work.