Automatically classifying database workloads

Said Elnaffar; Pat Martin; Randy Horman

doi:10.1145/584792.584898

Automatically classifying database workloads

Said Elnaffar, Pat Martin, Randy Horman

Research output: Contribution to conference › Paper › peer-review

36 Citations (Scopus)

Abstract

The type of the workload on a database management system (DBMS) is a key consideration in tuning the system. Allocations for resources such as main memory can be very different depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). In this paper, we present an approach to automatically identifying a DBMS workload as either OLTP or DSS. We build a classification model based on the most significant workload characteristics that differentiate OLTP from DSS, and then use the model to identify any change in the workload type. We construct a workload classifier from the Browsing and Ordering profiles of the TPC-W benchmark. Experiments with an industry-supplied workload show that our classifier accurately identifies the mix of OLTP and DSS work within an application workload.

Original language	English
Pages	622-624
Number of pages	3
DOIs	https://doi.org/10.1145/584792.584898
Publication status	Published - 2002
Externally published	Yes
Event	Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM 2002) - McLean, VA, United States Duration: Nov 4 2002 → Nov 9 2002

Other

Other	Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM 2002)
Country/Territory	United States
City	McLean, VA
Period	11/4/02 → 11/9/02

Keywords

Autonomic databases,
Classification
DSS
Data mining
OLTP
Self-managed DBMSs
Workload characterization

ASJC Scopus subject areas

General Decision Sciences
General Business,Management and Accounting

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10.1145/584792.584898

Cite this

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abstract = "The type of the workload on a database management system (DBMS) is a key consideration in tuning the system. Allocations for resources such as main memory can be very different depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). In this paper, we present an approach to automatically identifying a DBMS workload as either OLTP or DSS. We build a classification model based on the most significant workload characteristics that differentiate OLTP from DSS, and then use the model to identify any change in the workload type. We construct a workload classifier from the Browsing and Ordering profiles of the TPC-W benchmark. Experiments with an industry-supplied workload show that our classifier accurately identifies the mix of OLTP and DSS work within an application workload.",

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author = "Said Elnaffar and Pat Martin and Randy Horman",

year = "2002",

doi = "10.1145/584792.584898",

language = "English",

pages = "622--624",

note = "Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM 2002) ; Conference date: 04-11-2002 Through 09-11-2002",

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AU - Martin, Pat

AU - Horman, Randy

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AB - The type of the workload on a database management system (DBMS) is a key consideration in tuning the system. Allocations for resources such as main memory can be very different depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). In this paper, we present an approach to automatically identifying a DBMS workload as either OLTP or DSS. We build a classification model based on the most significant workload characteristics that differentiate OLTP from DSS, and then use the model to identify any change in the workload type. We construct a workload classifier from the Browsing and Ordering profiles of the TPC-W benchmark. Experiments with an industry-supplied workload show that our classifier accurately identifies the mix of OLTP and DSS work within an application workload.

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KW - Classification

KW - DSS

KW - Data mining

KW - OLTP

KW - Self-managed DBMSs

KW - Workload characterization

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Automatically classifying database workloads

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Keywords

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