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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Harizopoulos, S., et al.: OLTP through the looking glass, and what we found there. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. ACM (2008)
Kallman, R., et al.: H-store: a high-performance, distributed main memory transaction processing system. Proc. VLDB Endow. 1(2), 1496–1499 (2008)
Zhang, H., et al.: Anti-caching based elastic memory management for big data. In: 2015 IEEE 31st International Conference on Data Engineering (ICDE). IEEE (2015)
DeBrabant, J., et al.: Anti-caching: a new approach to database management system architecture. Proc. VLDB Endow. 6(14), 1942–1953 (2013)
Diaconu, C., et al.: Hekaton: SQL server’s memory-optimized OLTP engine. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data. ACM (2013)
Eldawy, A., Levandoski, J., Larson, P.-Å.: Trekking through Siberia: managing cold data in a memory-optimized database. Proc. VLDB Endow. 7(11), 931–942 (2014)
Levandoski, J.J., Larson, P.-Å., Stoica, R.: Identifying hot and cold data in main-memory databases. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE). IEEE (2013)
Alexiou, K., Kossmann, D., Larson, P.-Å.: Adaptive range filters for cold data: avoiding trips to Siberia. Proc. VLDB Endow. 6(14), 1714–1725 (2013)
Tanenbaum, A.S.: Modern Operating System. Pearson Education Inc., Upper Saddle River (2009)
Stoica, R., Ailamaki, A.: Enabling efficient OS paging for main-memory OLTP databases. In: Proceedings of the Ninth International Workshop on Data Management on New Hardware. ACM (2013)
Funke, F., Kemper, A., Neumann, T.: Compacting transactional data in hybrid OLTP&OLAP databases. Proc. VLDB Endow. 5(11), 1424–1435 (2012)
Storm, A.J., et al.: Adaptive self-tuning memory in DB2. In: Proceedings of the 32nd International Conference on Very Large Data Bases. VLDB Endowment (2006)
Duan, S., Thummala, V., Babu, S.: Tuning database configuration parameters with iTuned. Proc. VLDB Endow. 2(1), 1246–1257 (2009)
Pavlo, A., et al.: Self-driving database management systems. In: CIDR (2017)
Benoit, D.G.: Automatic diagnosis of performance problems in database management systems. In: Proceedings of the Second International Conference on Autonomic Computing, ICAC 2005. IEEE (2005)
Tran, D.N., et al.: A new approach to dynamic self-tuning of database buffers. ACM Trans. Storage (TOS) 4(1), 3 (2008)
Chen, A.N.K.: Robust optimization for performance tuning of modern database systems. Eur. J. Oper. Res. 171(2), 412–429 (2006)
Xu, J.: Rule-based automatic software performance diagnosis and improvement. Perform. Eval. 69(11), 525–550 (2012)
Jeong, J., Dubois, M.: Cache replacement algorithms with nonuniform miss costs. IEEE Trans. Comput. 55(4), 353–365 (2006)
Debnath, B.K., Lilja, D.J., Mokbel, M.F.: SARD: a statistical approach for ranking database tuning parameters. In: IEEE 24th International Conference on Data Engineering Workshop, ICDEW 2008. IEEE (2008)
Melcher, B., Mitchell, B.: Towards an autonomic framework: self-configuring network services and developing autonomic applications. Intel Technol. J. 8(4), 279–290 (2004)
Wiese, D., Rabinovitch, G.: Knowledge management in autonomic database performance tuning. In: Fifth International Conference on Autonomic and Autonomous Systems, ICAS 2009. IEEE (2009)
Fitzpatrick, B.: Distributed caching with memcached. Linux J. 2004(124), 5 (2004)
DeWitt, D.J., et al.: Implementation techniques for main memory database systems. 14(2) (1984)
Acknowledgment
This research is supported in part by 863 Program (no. 2015AA015303), NSFC (no. 61772341, 61472254, 61170238, 61602297 and 61472241), Singapore NRF (CREATE E2S2), and 973 Program (no. 2014CB340303). This work is also supported by the Program for Changjiang Young Scholars in University of China, and the Program for Shanghai Top Young Talents.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Huang, K., Zheng, S., Shen, Y., Zhu, Y., Huang, L. (2018). An Adaptive Eviction Framework for Anti-caching Based In-Memory Databases. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10828. Springer, Cham. https://doi.org/10.1007/978-3-319-91458-9_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-91458-9_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91457-2
Online ISBN: 978-3-319-91458-9
eBook Packages: Computer ScienceComputer Science (R0)