Issues in Bottleneck Detection in Multi-Tier Enterprise Applications

Parekh, Jason; Jung, Gueyoung; Swint, Galen; Pu, Calton; Sahai, Akhil

doi:10.1109/iwqos.2006.250489

Cited by 14 publications

(10 citation statements)

References 1 publication

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“…Such components often exhibit frequent congestion of load [18]. Also, application or system metrics correlating with an observed performance limitation are referred to as bottleneck metrics [19].…”

Section: Performance Bottlenecksmentioning

confidence: 99%

Performance Anomaly Detection and Bottleneck Identification

2015

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In order to meet stringent performance requirements, system administrators must e↵ectively detect undesirable performance behaviours, identify potential root causes and take adequate corrective measures. The problem of uncovering and understanding performance anomalies and their causes (bottlenecks) in di↵erent system and application domains is well studied. In order to assess progress, research trends and identify open challenges, we have reviewed major contributions in the area and present our findings in this survey. Our approach provides an overview of anomaly detection and bottleneck identification research as it relates to the performance of computing systems. By identifying fundamental elements of the problem, we are able to categorize existing solutions based on multiple factors such as the detection goals, nature of applications and systems, system observability, and detection methods.

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Section: Performance Bottlenecksmentioning

confidence: 99%

Performance Anomaly Detection and Bottleneck Identification

2015

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“…They further used this learned Bayesian network to identify performance bottlenecks during SLA violations. In their Elba project, Parekh et al [12] compared the TAN Bayesian network with other classifiers like decision trees in performance bottle detection. Chen et al [2] modified the JBoss middleware to trace user requests in the J2EE platform, and developed two methods to use collected traces for fault detection and diagnosis.…”

Section: Related Workmentioning

confidence: 99%

Ranking the importance of alerts for problem determination in large computer systems

Jiang¹,

Chen²,

Yoshihira³

et al. 2010

Cluster Comput

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The complexity of large computer systems has raised unprecedented challenges for system management. In practice, operators often collect large volume of monitoring data from system components and set up many rules to check data and trigger alerts. However, the alerts from various rules usually have different problem reporting accuracy because their thresholds are often manually set based on operators' experience and intuition. Meantime, due to system dependencies, a single problem may trigger many alerts at the same time in large systems and the critical question is which alert should be analyzed first in the following problem determination process. In this paper, we propose a novel peer review mechanism to rank the importance of alerts and the top ranked alerts are more likely to be true positives. After comparing a metric value against its threshold to generate alerts, we also compare the value with the equivalent thresholds from many other rules to determine the importance of alerts. Our approach is evaluated with a real test bed system and experimental results are also included to demonstrate its effectiveness.

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“…Cohen et al proposed to apply Tree Augmented Bayesian Networks to correlate system-level metrics with high-level performance metrics for performance diagnosis [8]. Parekh et al compared different classification methods to discover bottleneck metrics in the configuration of multi-tier enterprise applications [24]. Zhang et al proposed to use ensembles of models for diagnosing performance problems [33].…”

Section: Related Workmentioning

confidence: 99%

Online Anomaly Prediction for Robust Cluster Systems

Wang

2009

2009 IEEE 25th International Conference on Data Engineering

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Abstract-In this paper, we present a stream-based mining algorithm for online anomaly prediction. Many real-world applications such as data stream analysis requires continuous cluster operation. Unfortunately, today's large-scale cluster systems are still vulnerable to various software and hardware problems. System administrators are often overwhelmed by the tasks of correcting various system anomalies such as processing bottlenecks (i.e., full stream buffers), resource hot spots, and service level objective (SLO) violations. Our anomaly prediction scheme raises early alerts for impending system anomalies and suggests possible anomaly causes. Specifically, we employ Bayesian classification methods to capture different anomaly symptoms and infer anomaly causes. Markov models are introduced to capture the changing patterns of different measurement metrics. More importantly, our scheme combines Markov models and Bayesian classification methods to predict when a system anomaly will appear in the foreseeable future and what are the possible anomaly causes. To the best of our knowledge, our work provides the first stream-based mining algorithm for predicting system anomalies. We have implemented our approach within the IBM System S distributed stream processing cluster, and conducted case study experiments using fully implemented distributed data analysis applications processing real application workloads. Our experiments show that our approach efficiently predicts and diagnoses several bottleneck anomalies with high accuracy while imposing low overhead to the cluster system.

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Issues in Bottleneck Detection in Multi-Tier Enterprise Applications

Cited by 14 publications

References 1 publication

Performance Anomaly Detection and Bottleneck Identification

Performance Anomaly Detection and Bottleneck Identification

Ranking the importance of alerts for problem determination in large computer systems

Online Anomaly Prediction for Robust Cluster Systems

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