Satnam Singh
Abstract
This article shows how cluster-level logging infrastructure can be implemented using open source tools and deployed using the very same abstractions that are used to compose and manage the software systems being logged. Collecting and analyzing log information is an essential aspect of running production systems to ensure their reliability and to provide important auditing information. Many tools have been developed to help with the aggregation and collection of logs for specific software components (e.g., an Apache web server) running on specific servers (e.g., Fluentd and Logstash.) They are accompanied by tools such as Elasticsearch for ingesting log information into persistent storage and tools such as Kibana7 for querying log information.
- Burns, B., Grant, B., Oppenheimer, D., Brewer, E., Wilkes, J. 2016. Borg, Omega, and Kubernetes. acmqueue 14(1); http://queue.acm.org/detail.cfm?id=2898444. Google Scholar
Digital Library
- Docker; www.docker.com.Google Scholar
- Elasticsearch. Elastic; https://www.elastic.co/products/elasticsearch.Google Scholar
- Fluentd; http://www.fluentd.org/.Google Scholar
- Google Compute Engine; https://cloud.google.com/compute/.Google Scholar
- Google Container Engine; https://cloud.google.com/container-engine/.Google Scholar
- Kibana. Elastic; https://www.elastic.co/products/kibana.Google Scholar
- Kubernetes; http://kubernetes.io/.Google Scholar
- Logstash. Elastic; https://www.elastic.co/products/logstash.Google Scholar
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