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Related Concept Videos

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Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
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Related Experiment Video

Updated: Jul 10, 2025

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Detecting Performance Anomalies in Cloud Platform Applications.

Hiranya Jayathilaka1, Chandra Krintz1, Rich Wolski1

  • 1Computer Science Department, Univ. of California, Santa Barbara.

IEEE Transactions on Cloud Computing
|November 20, 2023
PubMed
Summary
This summary is machine-generated.

Roots is a new system for cloud Platform-as-a-Service (PaaS) that detects performance anomalies and identifies bottlenecks. It monitors applications without requiring code changes, pinpointing issues to workload changes or PaaS service problems.

Keywords:
Cloud computingPerformance anomaly detectionRoot cause analysis

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Area of Science:

  • Cloud Computing
  • System Monitoring
  • Performance Analysis

Background:

  • Cloud Platform-as-a-Service (PaaS) systems require robust monitoring for performance.
  • Developers often face the burden of instrumenting application code for performance monitoring.
  • Identifying root causes of performance anomalies in complex PaaS environments is challenging.

Purpose of the Study:

  • To introduce Roots, a full-stack system for performance anomaly detection and bottleneck identification in PaaS.
  • To provide application performance monitoring as a core PaaS capability, eliminating the need for manual code instrumentation.
  • To correlate performance data across PaaS layers to trace anomalies to specific cloud platform components.

Main Methods:

  • Developed Roots, a system that tracks HTTP/S requests and PaaS service usage.
  • Employed lightweight monitoring of PaaS service interfaces.
  • Utilized multiple statistical techniques for anomaly detection and root cause analysis (workload vs. bottleneck).

Main Results:

  • Roots effectively detects performance anomalies and identifies bottlenecks in PaaS systems.
  • The system differentiates between anomalies caused by workload changes and those caused by PaaS service bottlenecks.
  • Correlation of data across PaaS layers successfully traces high-level anomalies to specific components.

Conclusions:

  • Roots offers a comprehensive solution for performance monitoring and anomaly detection in PaaS clouds.
  • The system significantly reduces the developer's effort by automating code instrumentation.
  • Roots enables precise identification of performance bottlenecks within the cloud platform infrastructure.