NDLI: Oracle Workload Intelligence

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Oracle Workload Intelligence

Content Provider	ACM Digital Library
Author	Morfonios, Konstantinos Tran, Quoc Trung Polyzotis, Neoklis
Abstract	Analyzing and understanding the characteristics of the incoming workload is crucial in unraveling trends and tuning the performance of a database system. In this work, we present Oracle Workload Intelligence (WI), a tool for workload modeling and mining, as our attempt to infer the processes that generate a given workload. WI consists of two main functionalities. First, WI derives a model that captures the main characteristics of the workload without overfitting, which makes it likely to generalize well to unseen instances of the workload. Such a model provides insights into the most frequent code paths in the application that drives the workload, and also enables optimizations inside the database system that target sequences of query statements. Second, WI can compare the models of different snapshots of the workload to detect whether the workload has changed. Such changes might indicate new trends, regressions, problems, or even security issues. We demonstrate the effectiveness of WI with an experimental study on synthetic workloads and customer-provided application benchmarks.
Starting Page	1669
Ending Page	1681
Page Count	13
File Format	PDF
ISBN	9781450327589
DOI	10.1145/2723372.2742791
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Workload intelligence Workload modeling Significant pattern
Content Type	Text
Resource Type	Article

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