TY - GEN
T1 - Quantitative Contention Generation for Performance Evaluation on OLTP Databases
AU - Zhang, Chunxi
AU - Zhang, Rong
AU - Qian, Weining
AU - Shu, Ke
AU - Zhou, Aoying
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Although we have achieved significant progress in improving the scalability of transactional database systems (OLTP), the presence of contention operations in workloads is still the fundamental limitation in improving throughput. The reason is that the overhead of managing conflict transactions with concurrency control mechanism is proportional to the amount of contentions. As a consequence, contention workload generation is urgent to evaluate performance of modern OLTP database systems. Though we have kinds of standard benchmarks which provide some ways in simulating resource contention, e.g. skew distribution control of transactions, they can not control the generation of contention quantitatively; even worse, the simulation effectiveness of these methods is affected by the scale of data. So in this paper we design a scalable quantitative contention generation method with fine contention granularity control, which is expected to generate resource contention specified by contention ratio and contention intensity.
AB - Although we have achieved significant progress in improving the scalability of transactional database systems (OLTP), the presence of contention operations in workloads is still the fundamental limitation in improving throughput. The reason is that the overhead of managing conflict transactions with concurrency control mechanism is proportional to the amount of contentions. As a consequence, contention workload generation is urgent to evaluate performance of modern OLTP database systems. Though we have kinds of standard benchmarks which provide some ways in simulating resource contention, e.g. skew distribution control of transactions, they can not control the generation of contention quantitatively; even worse, the simulation effectiveness of these methods is affected by the scale of data. So in this paper we design a scalable quantitative contention generation method with fine contention granularity control, which is expected to generate resource contention specified by contention ratio and contention intensity.
UR - https://www.scopus.com/pages/publications/85093848635
U2 - 10.1007/978-3-030-60290-1_34
DO - 10.1007/978-3-030-60290-1_34
M3 - 会议稿件
AN - SCOPUS:85093848635
SN - 9783030602895
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 441
EP - 456
BT - Web and Big Data - 4th International Joint Conference, APWeb-WAIM 2020, Proceedings
A2 - Wang, Xin
A2 - Zhang, Rui
A2 - Lee, Young-Koo
A2 - Sun, Le
A2 - Moon, Yang-Sae
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020
Y2 - 18 September 2020 through 20 September 2020
ER -