Database Summer Seminar

Monday, June 24, 2019 - 4:30pm to 5:30pm


8102 Gates Hillman Centers


YANGJUN SHENG, Masters Student (May19 graduate)

A Scheduling OLTP Transactions via Learned Abort Prediction

Current main memory database system architectures are still challenged by high contention workloads and this challenge will continue to grow as the number of cores in processors continues to increase. These systems schedule transactions randomly across cores to maximize concurrency and to produce a uniform load across cores. Scheduling never considers potential conflicts. Performance could be improved if scheduling balanced between concurrency to maximize throughput and scheduling transactions linearly to avoid conflicts. In this talk, we present the design of several intelligent transaction scheduling algorithms that consider both potential transaction conflicts and concurrency. To incorporate reasoning about transaction conflicts, we develop a supervised machine learning model that estimates the probability of conflict. This model is incorporated into several scheduling algorithms. In addition, we integrate an unsupervised machine learning algorithm into an intelligent scheduling algorithm. We then empirically measure the performance impact of different scheduling algorithms on OLTP and social networking workloads. Our results show that, with appropriate settings, intelligent scheduling can increase throughput by 54% and reduce the abort rate by 80% on a 20-core machine, relative to random scheduling. In summary, we provide preliminary evidence that intelligent scheduling significantly improves DBMS performance.

This is a practice talk for the SIGMOD/AiDM workshop

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