Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning

By Archana Ganapathi, Harumi A. Kuno, Umeshwar Dayal, Janet L. Wiener, Armando Fox, Michael I. Jordan, David A. Patterson. IEEE International Conference on Data Engineering, 2009

360 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/86da6a1bf62ce8c94252c7772246f20106d8caf2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. John Wilkes
2. Viktor Leis
3. Peter A. Boncz
4. Andreas Kipf
5. Thomas Kipf
6. Bernhard Radke
7. Alfons Kemper
8. Xiaohui Gu

In Management Science and Operations Research

1. Thomas Kipf

In Signal Processing

1. Alfons Kemper
2. Peter A. Boncz
3. Thomas Kipf

Citations in the corpus

• Learned Cardinalities: Estimating Correlated Joins with Deep Learning (2018, background): “The same holds for approaches that used machine learning to predict overall resource consumption: running time, memory footprint, I/O, network traffic [6, 19], although these models did include course-grained features (the estimated cardinality) based on statistics into the features.”
• PRESS: PRedictive Elastic ReSource Scaling for cloud systems (2010, methodology): “Those approaches use queueing theory [4] or statistical learning methods [20], [21], [22], [23] to build models that allow the system to predict the impact of different resource allocation policies on the application performance.”
• CloudScale: elastic resource scaling for multi-tenant cloud systems (2011, methodology): “Those approaches use statistical learning methods [34, 37, 20, 35] or queueing theory [17] to build models that allow the system to predict the impact of different resource allocation policies on the application performance.”