Bottleneck detection using statistical intervention analysis

被引：0

作者：

Malkowski, Simon ^{[1
]}

Hedwig, Markus ^{[1
]}

Parekh, Jason ^{[1
]}

Pu, Calton ^{[1
]}

Sahai, Akhil ^{[2
]}

机构：

[1] Georgia Inst Technol, CERCS, 266 Ferst Dr, Atlanta, GA 30332 USA

[2] HP Lab, Palo Alto, CA USA

来源：

MANAGING VIRTUALIZATION OF NETWORKS AND SERVICES, PROCEEDINGS | 2007年 / 4785卷

基金：

美国国家科学基金会;

关键词：

bottleneck detection; statistical analysis; enterprise systems; performane analysis;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The complexity of today's large-scale enterprise applications demands system administrators to monitor enormous amounts of metrics, and reconfigure their hardware as well as software at run-time without thorough understanding of monitoring results. The Elba project is designed to achieve an automated iterative staging to mitigate the risk of violating Service Level Objectives (SLOs). As part of Elba we undertake performance characterization of system to detect bottlenecks in their configurations. In this paper, we introduce our concrete bottleneck detection approach used in Elba, and then show its robustness and accuracy in various configurations scenarios. We utilize a well-known benchmark application, RUBiS (Rice University Bidding System), to evaluate the classifier with respect to successful identification of different bottlenecks.

引用

页码：122 / +

页数：3