Network and system analytics as a Docker microservice
Microservices describes why the industry standard sFlow instrumentation embedded within cloud infrastructure is uniquely able to provide visibility into microservice deployments.The sFlow-RT analytics engine is well suited to deployment as a Docker microservice since the application is stateless and presents network and system analytics as a RESTful service.
The following steps demonstrate how to create a containerized deployment of sFlow-RT.
First, create a directory for the project and edit the Dockerfile:
mkdir sflow-rtAdd the following contents to Dockerfile:
cd sflow-rt
vi Dockerfile
FROM centos:centos6Build the project:
RUN yum install -y java-1.7.0-openjdk
RUN rpm -i http://www.inmon.com/products/sFlow-RT/sflow-rt-2.0-1072.noarch.rpm
EXPOSE 8008 6343/udp
CMD /etc/init.d/sflow-rt start && tail -f /dev/null
docker build -t sflow-rt .Run the service:
docker run -p 8008:8008 -p 6343:6343/udp -d sflow-rtAccess the API at http://docker_host:8008/ to verify that the service is running.
Now configure sFlow agents to send data to the docker_host on port 6343:
- Switch configurations
- Install Host sFlow agents to monitor hosts, hypervisors, Docker, etc.
The diagram shows how new and existing cloud based or locally hosted orchestration, operations, and security tools can leverage sFlow-RT's analytics service to gain real-time visibility. The solution is extremely scaleable, a single sFlow-RT instance can monitor thousands of servers and the network devices connecting them.

It already has a built-in customer base for this offering.

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SDxCentral recently interviewed Red Hat's General Manager of OpenStack, Radhesh Balakrishnan, and discussed the meaning of carrier grade in the NFV world and the importance of working with the open source communities.