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Automated Diagnosis of Chronic Perfo...

Kavulya, Soila P.

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Automated Diagnosis of Chronic Performance Problems in Production Systems.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Automated Diagnosis of Chronic Performance Problems in Production Systems./
作者:	Kavulya, Soila P.
面頁冊數:	150 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-12(E), Section: B.
Contained By:	Dissertation Abstracts International74-12B(E).
標題:	Engineering, Computer. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3573469
ISBN:	9781303436550

Automated Diagnosis of Chronic Performance Problems in Production Systems.
Kavulya, Soila P.

Automated Diagnosis of Chronic Performance Problems in Production Systems. - 150 p.

Source: Dissertation Abstracts International, Volume: 74-12(E), Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2013.

Large production systems are susceptible to chronic performance problems where the system still works, but with degraded performance. Chronic performance problems occur intermittently or affect a subset of end-users. Traditional approaches for diagnosis typically rely on a bottom-up approach that localizes problems by correlating low-level alarms (such as resource utilization indicators or network packet loss) across components in a production system. However, these alarm-correlation approaches fall short when diagnosing chronics because they fail to provide the necessary application-level visibility to detect chronics effectively. Due to the scale and complexity of production systems, there can be multiple unresolved chronics at any given time---their symptoms often overlap with each other, and they are sometimes triggered by complex corner cases.

ISBN: 9781303436550Subjects--Topical Terms:

1669061
Engineering, Computer.

Automated Diagnosis of Chronic Performance Problems in Production Systems.
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500 $a Adviser: Priya Narasimhan.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2013.
520 $a Large production systems are susceptible to chronic performance problems where the system still works, but with degraded performance. Chronic performance problems occur intermittently or affect a subset of end-users. Traditional approaches for diagnosis typically rely on a bottom-up approach that localizes problems by correlating low-level alarms (such as resource utilization indicators or network packet loss) across components in a production system. However, these alarm-correlation approaches fall short when diagnosing chronics because they fail to provide the necessary application-level visibility to detect chronics effectively. Due to the scale and complexity of production systems, there can be multiple unresolved chronics at any given time---their symptoms often overlap with each other, and they are sometimes triggered by complex corner cases.
520 $a This dissertation presents a top-down diagnostic framework for diagnosing chronic performance problems in production systems. The framework comprises of four components. First, an extensible log-analysis framework that extracts end-to-end causal flows using common white-box (i.e., application) logs in the production system; these end-to-end flows capture the user's experience with the system. Second, anomaly-detection tools exploit heuristics and a peer-comparison approach to label each end-to-end flow as successful or failed. Third, a top-down statistical diagnostic tool combines white-box metrics with black-box metrics ( e.g., CPU usage) to localize the source of the problem by identifying attributes that are more correlated with failed flows than successful ones. Fourth, a visualization tool that uses peer-comparison to highlight anomalous nodes in a parallel-computing cluster.
520 $a The diagnostic framework has been used to localize real incidents at an academic cloud-computing cluster that runs the Hadoop parallel-processing framework, and a production Voice-over-IP system at a major Internet Services Provider. Our approach is not limited to these two systems and is applicable to systems such as Internet Services that serve users via independent interactions.
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