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Monitorización de Microservicios

Microservices Monitoring
Authors: Grau I Pujol, Marc;

Monitorización de Microservicios

Abstract

En este proyecto se ha llevado a cabo el diseño y despliegue de un clúster de Kubernetes, donde existen varios microservicios que son monitorizados mediante Prometheus y Grafana. Kubernetes es una potente herramienta que nos permite automatizar la implementación, escalado y administración de aplicaciones en contenedores. El hecho de monitorizar el entorno de Kubernetes haciendo uso de Prometheus, permitirá extraer diferentes métricas de los diferentes microservicios en tiempo real y crear alertas de eventos. Por otra parte, lo que Grafana nos proporciona es poder plasmar y analizar gráficamente y en tiempo real todas aquellas métricas que Prometheus ha recogido y almacenado. De este modo, se podrá realizar un seguimiento constante y preciso del estado de los microservicios, desde el uso de CPU o memoria, hasta el número de peticiones. Por tanto, todo ello permitirá hacer un diagnóstico de errores y problemas de la aplicación y solucionarlos antes de llegar al estado de producción, es decir, durante la fase de desarrollo del proyecto.

En aquest projecte s’ha dut a terme el disseny i desplegament d’un clúster de Kubernetes, on existeixen varis microserveis els quals són monitoritzats mitjançant Prometheus i Grafana. Kubernetes és una potent eina que ens permet automatitzar la implementació, l’escalat i l’administració d’aplicacions en contenidors. El fet de monitoritzar l’entorn de Kubernetes fent ús de Prometheus, permetrà extreure diferents mètriques dels diferents microserveis a temps real i crear alertes d’esdeveniments. Per altra banda, el que Grafana ens proporciona és poder plasmar i analitzar gràficament i a temps real totes aquelles mètriques que Prometheus ha recollit i emmagatzemat. D’aquesta manera, es podrà fer un seguiment constant i precís de l’estat dels microserveis, des de l’ús de CPU o memòria, fins al número de peticions. Per tant, tot això permetrà fer un diagnòstic d’errors i problemes de l’aplicació i solucionar-los abans d’arribar a l’estat de producció, és a dir, durant la fase de desenvolupament del projecte.

In this project it has been carried out the design and deployment of a Kubernetes cluster, where there are several microservices that are monitored by Prometheus and Grafana. Kubernetes is a powerful tool that allows us to automate the deployment, scaling and management of containerised applications. By monitoring the Kubernetes environment using Prometheus, we can extract different metrics from the different microservices in real time and create event alerts. On the other hand, what Grafana provides us with is the ability to capture and analyse graphically and in real time all the metrics that Prometheus has collected and stored. In this way, it will be possible to carry out a constant and precise monitoring of the state of the microservices, from the use of CPU or memory, to the number of requests. Therefore, all this will allow a diagnosis of errors and problems of the application and solve them before reaching the production state, that means, during the development phase of the project.

Country
Spain
Keywords

Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors, Monitoring, System analysis, Monitorización, Systems analysis, Telematics, Telemàtica, Telemática, Anàlisi de sistemes, Análisis de sistemas

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download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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