• Jeff Gensler (jgensl2@uic.edu, jgensler8@gmail.com)

Provide an easy (enough) to use platform for application developers and data scientists to deploy applications (Node.js/Python/Ruby) or “Big Data” (MapReduce/Spark/Flint) jobs without having to set anything up locally.

Use cases:

ACM member A creates a Node.js App that needs MongoDB. If ACM member A's app becomes a hit on campus, A needs to scale the database, load balancing, etc to accommodate the new traffic.

ACM member B takes a Machine Learning class and decides they will use Apache Spark for analysis. B realizes that one standalone container for testing Spark is not powerful enough for the job and wishes to deploy a set of standalone containers (as opposed to YARN/Mesos on top of CoreOS) to tackle their job.

https://www.docker.com/

https://coreos.com/

https://wiki.archlinux.org/index.php/systemd

http://deis.io/overview/

https://github.com/CenturyLinkLabs/fig2coreos

https://github.com/rancherio/rancher

https://github.com/rancherio/cattle

https://github.com/progrium/dokku

https://github.com/dokku-alt/dokku-alt

http://mesosphere.com/

http://mesosphere.github.io/marathon/

https://tsuru.io/

We have chosen Docker as the method of containment for such applications and CoreOS as a means of holding and scaling these containers. Typically, a user would write a systemd unit file that would typically download a docker container, name the container, and deploy it across the cluster, lettings CoreOS balance the load. While this granularity is fine for a select few, it will be too much work for someone that wants to quickly deploy their application.

Deis

Deis appears to be the easiest way for an single application to be deployed. Deis only focuses on the application container and not on the database container (something about the 12 factor app). Deis appears to have the most support out of all of the products looked at. This product would be great for a simple application developer but would fail for our fellow data scientists. With this solution, we might need to make use of CoreOS's Flannel.

Fig2CoreOS

Fig2CoreOS produces the exact end result of the project in that it allows a highly customize-able, highly-scale-able, and isolated deployment of an application. However, this still doesn't solve the ease of use for a non-sysadmin to deploy an application. I think fig is intuitive enough, but I am not sure about support from the outside world not.

Rancher

Rancher uses Github as an ACL but we can “simply” change the underlying technology to point to a (our) Gitlab server. This tool is really neat because it allows a group of people to modify a set of containers. This seems like a promising tool for monitoring and quick modification of containers, and interaction with the REST API would provide an easy way for scripts to manually scale applications. This seems like a solid fit for application with minimal one-time configurations and not so helpful for elastic services.

Shipyard

This product uses certs and publicly facing docker daemons which sounds pretty dangerous (check out docker's documentation for that one). The product doesn't really look all that different than Rancher and has a bit more logic (like RethinkDB over etcd).

Mesosphere

For running Docker containers on Mesos.

Marathon

This might be a suitable solution for the data scientist…

Dokku OR Dokku-alt

This will give up a Heroku like deployment for a simple application. Dokku-alt will give us the ease of deployment of MariaDB, PostgreSQL, MongoDB or Redis databases. However, the lack of a multi-tenant nature of Dokku is the problem that Deis solves. See ( this SO question)

Tsuru

Open Source PaaS solution. Might not have so much support later on but integrates Heroku buildpacks (like Dokku, etc) so ease of use for developers.

OpenStack w/ Heat