Transaction Scheduling: VATS and CATS
From transaction-latency diagnosis to contention-aware scheduling adopted in major database engines.
VATS
CATS
How should a database schedule lock contention to improve throughput and reduce latency variance?
Research connecting latency diagnosis and semantic profiling to VATS and CATS transaction-scheduling policies.
The problem
Lock contention can create unstable tail latency and throughput collapse under highly concurrent transactional workloads.
The approach
Profile latency sources and schedule waiting transactions using variance-aware and contention-aware policies.
Research connecting latency diagnosis and semantic profiling to VATS and CATS transaction-scheduling policies.
Main contributions
- Research framing and system design
- Methods, implementation, and empirical evaluation
- Open research artifacts and scholarly dissemination
Publications
Contention-Aware Lock Scheduling for Transactional Databases
PVLDB 2018 · Proceedings of the VLDB Endowment
Paper ↗Technical Report ↗Project ↗
Cite
@article{5d2dbe3f-c6e6-40c8-9a82-9f8e042ef4a2,
title = {Contention-Aware Lock Scheduling for Transactional Databases},
author = {Boyu Tian and Jiamin Huang and Barzan Mozafari and Grant Schoenebeck},
journal = {Proceedings of the VLDB Endowment},
year = {2018},
doi = {10.1145/3187009.3177740}
}Statistical Analysis of Latency Through Semantic Profiling
EuroSys 2017 · ACM European Conference on Computer Systems
Paper ↗Code ↗Project ↗
Cite
@inproceedings{593a1faa-d447-439a-aca4-9dc5a037630e,
title = {Statistical Analysis of Latency Through Semantic Profiling},
author = {Jiamin Huang and Barzan Mozafari and Thomas F. Wenisch},
booktitle = {ACM European Conference on Computer Systems},
year = {2017},
doi = {10.1145/3064176.3064179}
}Identifying the Major Sources of Variance in Transaction Latencies: Towards More Predictable Databases
CoRR 2016 · Computing Research Repository
Project ↗
Cite
@article{79a739c2-69af-4e7a-9fab-9e1ca9ca9730,
title = {Identifying the Major Sources of Variance in Transaction Latencies: Towards More Predictable Databases},
author = {Jiamin Huang and Barzan Mozafari and Thomas F. Wenisch},
journal = {Computing Research Repository},
year = {2016}
}SnappyData: A Unified Cluster for Streaming, Transactions and Interactice Analytics
CIDR 2017 · Conference on Innovative Data Systems Research
Paper ↗Project ↗
Cite
@inproceedings{e6de4a52-07a5-47c5-8196-ace46d2a49c2,
title = {SnappyData: A Unified Cluster for Streaming, Transactions and Interactice Analytics},
author = {Barzan Mozafari and Jags Ramnarayan and Sudhir Menon and Yogesh Mahajan and Soubhik Chakraborty and Hemant Bhanawat and Kishor Bachhav},
booktitle = {Conference on Innovative Data Systems Research},
year = {2017}
}DBSherlock: A Performance Diagnostic Tool for Transactional Databases
SIGMOD 2016 · ACM SIGMOD International Conference on Management of Data
Paper ↗Code ↗Data ↗Project ↗
Cite
@inproceedings{a5460b19-8bff-4fd2-ae5e-d0ce7477454b,
title = {DBSherlock: A Performance Diagnostic Tool for Transactional Databases},
author = {Dong Young Yoon and Ning Niu and Barzan Mozafari},
booktitle = {ACM SIGMOD International Conference on Management of Data},
year = {2016},
doi = {10.1145/2882903.2915218}
}SnappyData: A Hybrid Transactional Analytical Store Built On Spark
SIGMOD 2016 · ACM SIGMOD International Conference on Management of Data
Paper ↗Project ↗
Cite
@inproceedings{cea59a03-38c4-4acb-bd2d-a41ecabe154a,
title = {SnappyData: A Hybrid Transactional Analytical Store Built On Spark},
author = {Jags Ramnarayan and Barzan Mozafari and Sumedh Wale and Sudhir Menon and Neeraj Kumar and Hemant Bhanawat and Soubhik Chakraborty and Yogesh Mahajan and Rishitesh Mishra and Kishor Bachhav},
booktitle = {ACM SIGMOD International Conference on Management of Data},
year = {2016},
doi = {10.1145/2882903.2899408}
}