Following the series that we started with the Vamp Blog post, we proceed to take a look of one more of the container management tools which includes running a simple practical example while we pay attention to the main advantages and limitations. This series happens in the context of the work on cloud-native applications in the Service Prototyping Lab to explore how easily developers can decompose their applications and fit them into the emerging platforms.
On this occasion, we inspect Kubernetes, one of the most popular open-source container orchestration tool for production environments. Kubernetes builds upon 15 years of experience of running production workloads at Google. Moreover the community of Kubernetes appears to be the biggest among all the open source container management communities. Kubernetes provides a Slack channel with more than 8000 users who share ideas and are often Kubernetes engineers. Also, one can find community support in Stack Overflow using the tag kubernetes. Inside the Github repository, we can see more than 970 contributors, 1500 watches, 18500 starts and 6000 forks. In the community it is popular to abbreviate the system as K8s.
Following our previous blog post, we are still looking at tools for collecting metrics from an Openstack deployment in order to understand its resource utilization. Although Monasca has a comprehensive set of metrics and alarm definitions, the complex installation process combined with a lack of documentation makes it a frustrating experience to get it up and running. Further, although it is complex, with many moving parts, it was difficult to configure it to obtain the analysis we wanted from the raw data, viz how many of our servers are overloaded over different timescales in different respects (cpu, memory, disk io, network io). For these reasons we decided to try Prometheus with Grafana which turned out to be much easier to install and configure (taking less than an hour to set up!). This blog post covers the installation process and configuration of Prometheus and Grafana in a Docker container and how to install and configure Canonical’s Prometheus Openstack exporter to collect a small set of metrics related to an Openstack deployment.
In one of our projects we are making contributions to an Openstack project called Watcher, this project focuses on optimizing resource utilization of a cloud according to a given strategy. As part of this work it is important to understand the resource utilization of the cloud beforehand in order to make a meaningful contribution. This requires collection of metrics from the system and processing them to understand how the system is performing. The Ceilometer project was our default choice for collecting metrics in an Openstack deployment but as work has evolved we are also exploring alternatives – specifically Monasca. In this blog post I will cover my personal experience installing Monasca (which was more challenging than expected) and how we hacked the monasca/demo docker image to connect it to our Openstack deployment. Continue reading →
Among the demos, one particularly appealing was UAV-NET, related to “Mobile-Mobile Networks”, consisting of a drone that could distribute a network over an area, it proposed the extension of the communication coverage area dynamically, dynamic wireless backhaul management, dynamic data acquisition when and where really needed, dynamic data acquisition when and where really needed, provision of appropriate crowd protection and emergency services; and, support for public event logistics and security. Continue reading →
The 7th Fokus FUSECO Forum, 2016, brought together international technology experts to present and debate about the latests developments. The Forum welcomed 32 nations in Berlin, Germany and split into two days of activities.
In the world of containerized architectures, there are different and new container deployment and orchestration tools which help turning monolithic applications into running composite microservices. Some of them are intended to be used in a development environment like Docker-Compose or in a production environment like Kubernetes, Docker-Swarm or Marathon. Also, we can observe some tools executing atop other container schedulers, like Rancher or Vamp. In this blog post, we take a look at the latter while at the same time we continue to inspect the alternatives in order to compare all solutions eventually.