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We present Roots, a full-stack monitoring and analysis system for performance anomaly detection and bottleneck identification in cloud platform-as-a-service (PaaS) systems. Roots facilitates application performance monitoring as a core capability of PaaS clouds, and relieves the developers from having to instrument application code. Roots tracks HTTP/S requests to hosted cloud applications and their use of PaaS services. To do so it employs lightweight monitoring of PaaS service interfaces. Roots processes this data in the background using multiple statistical techniques that in combination detect performance anomalies (i.e. violations of service-level objectives). For each anomaly, Roots determines whether the event was caused by a change in the request workload or by a performance bottleneck in a PaaS service. By correlating data collected across different layers of the PaaS, Roots is able to trace high-level performance anomalies to bottlenecks in specific components in the cloud platform. We implement Roots using the AppScale PaaS and evaluate its overhead and accuracy.
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Strategies for Crisis-Responsive Governance: Automated Anomaly Identification in Public Services
This paper introduces a machine learning tool for service systems, focusing on accurate classification of service requests and swift anomaly detection, particularly crucial during emergencies. Employing a Support Vector Machine model, this tool automatically classifies service calls into predefined categories with high accuracy, while effectively detecting irregular requests that require specific attention from operators. This approach streamlines resource management by reducing the manuaI categorization workload and enables early detection of emerging service needs. Examining Orange County, Florida 311 System data, with a specific focus on the COVID-19 period, we illustrate the tool's success in automatic request categorization and anomaly detection. Overall, this tool presents an effective automation approach to help with efficient resource management of service systems and proactive assessment of public service needs, promising to revolutionize service request management during crises. Future work will explore additional classification models for enhanced accuracy and integrate automated alerts for proactive disaster management.
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- Award ID(s):
- 1735139
- PAR ID:
- 10534469
- Editor(s):
- Penkert, B; Hellingrath, B; Rode, M; Widera, A; Middelhoff, M; Boersma, K; Kalthoner, M
- Publisher / Repository:
- https://ojs.iscram.org/index.php/Proceedings
- Date Published:
- Subject(s) / Keyword(s):
- 311 services classification anomaly detection supervised learning disaster management
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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