Hi-Clust: Unsupervised Analysis of Cloud Latency Measurements Through Hierarchical Clustering

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00325942" target="_blank" >RIV/68407700:21230/18:00325942 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8549558" target="_blank" >https://ieeexplore.ieee.org/document/8549558</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CloudNet.2018.8549558" target="_blank" >10.1109/CloudNet.2018.8549558</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hi-Clust: Unsupervised Analysis of Cloud Latency Measurements Through Hierarchical Clustering

Popis výsledku v původním jazyce

Latency is nowadays one of the most relevant network and service performance metrics reflecting end-user experience. With the wide adoption and deployment of delay-sensitive applications in the Cloud (e.g., gaming, interactive video conferencing, corporate services, etc.), monitoring and analysis of Cloud service latency is becoming increasingly relevant for Cloud service providers, tenants and even users. Traditional network monitoring approaches based on time-series analysis and thresholding are capable of raising alarms when anomalous events arise, but are not applicable to detect correlations among multiple monitored dimensions, necessary to provide an adequate interpretation of an anomaly. In this paper we present Hi-Clust, an unsupervised-based approach for analyzing and interpreting anomalies in multi-dimensional network data, through the application of hierarchical clustering techniques. While Hi-Clust is applicable to the analysis of different types of nested or hierarchically structured data, we particularly focus on the analysis of Cloud service latency, using active measurements collected from geographically distributed vantage points. We implement and benchmark multiple density-based clustering approaches for Hi-Clust over four weeks of real multidimensional Cloud service latency measurements. Using the most robust underlying clustering algorithm from the benchmark, we show how to automatically extract and interpret anomalous Cloud service behavior with Hi-Clust. In addition, we show the advantages of Hi-Clust over traditional threshold-based approaches for detecting and interpreting anomalous behavior, through practical examples over the collected measurements.

Název v anglickém jazyce

Hi-Clust: Unsupervised Analysis of Cloud Latency Measurements Through Hierarchical Clustering

Popis výsledku anglicky

Latency is nowadays one of the most relevant network and service performance metrics reflecting end-user experience. With the wide adoption and deployment of delay-sensitive applications in the Cloud (e.g., gaming, interactive video conferencing, corporate services, etc.), monitoring and analysis of Cloud service latency is becoming increasingly relevant for Cloud service providers, tenants and even users. Traditional network monitoring approaches based on time-series analysis and thresholding are capable of raising alarms when anomalous events arise, but are not applicable to detect correlations among multiple monitored dimensions, necessary to provide an adequate interpretation of an anomaly. In this paper we present Hi-Clust, an unsupervised-based approach for analyzing and interpreting anomalies in multi-dimensional network data, through the application of hierarchical clustering techniques. While Hi-Clust is applicable to the analysis of different types of nested or hierarchically structured data, we particularly focus on the analysis of Cloud service latency, using active measurements collected from geographically distributed vantage points. We implement and benchmark multiple density-based clustering approaches for Hi-Clust over four weeks of real multidimensional Cloud service latency measurements. Using the most robust underlying clustering algorithm from the benchmark, we show how to automatically extract and interpret anomalous Cloud service behavior with Hi-Clust. In addition, we show the advantages of Hi-Clust over traditional threshold-based approaches for detecting and interpreting anomalous behavior, through practical examples over the collected measurements.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2018 IEEE 7th International Conference on Cloud Networking (CloudNet)

ISBN

978-1-5386-6831-3

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Tokyo

Datum konání akce

22. 10. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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