Performance and resource modeling in highly-concurrent OLTP workloads
Name
sigmod_2013.pdf
Description
Accepted version
Size
657.14 KB
Format
Adobe PDF
Checksum (MD5)
abb2c4dd012d2138bef764e68523882e
Author(s)
Mozafari, Barzan
Curino, Carlo
Jindal, Alekh
Madden, Samuel
Date Issued
2013
Publisher
ACM Press
Citation
Mozafari, Barzan, Curino, Carlo, Jindal, Alekh and Madden, Samuel. 2013. "Performance and resource modeling in highly-concurrent OLTP workloads."
Version
Author's final manuscript
Abstract
Database administrators of Online Transaction Processing (OLTP) systems constantly face difcult questions. For example, "What is the maximum throughput I can sustain with my current hardware?", "How much disk I/O will my system perform if the requests per second double?", or "What will happen if the ratio of transactions in my system changes?". Resource prediction and performance analysis are both vital and difcult in this setting. Here the challenge is due to high degrees of concurrency, competition for resources, and complex interactions between transactions, all of which non-linearly impact performance. Although difcult, such analysis is a key component in enabling database administrators to understand which queries are eating up the resources, and how their system would scale under load. In this paper, we introduce our framework, called DBSeer, that addresses this problem by employing statistical models that provide resource and performance analysis and prediction for highly concurrent OLTP workloads. Our models are built on a small amount of training data from standard log information collected during normal system operation. Tese models are capable of accurately measuring several performance metrics, including resource consumption on a per-transaction-type basis, resource bottlenecks, and throughput at diferent load levels. We have validated these models on MySQL/Linux with numerous experiments on standard benchmarks (TPC-C) and real workloads (Wikipedia), observing high accuracy (within a few percent error) when predicting all of the above metrics. Copyright © 2013 ACM.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1145/2463676.2467800
