A slightly off-topic comment, but being an early-stage PhD in theoretical CS with my thesis topic on approximation algorithms for scheduling, I would like to know whether there are some theoretical problems related to these VM schedulers used in practice. If there is somebody knowledgeable about what is theoretically open (unknown tight approximation ratio, for instance) AND very useful to people building Kubernetes…
This is because while you can try to produce an optimal distribution, it's a fool's errand in a distributed system. A perfect solver, given imperfect data, produces nonsense. GIGO, as they used to say.
Amit Gupta wrote some notes last year about it[2].
Disclaimer: I work for Pivotal.
[2] http://blog.pivotal.io/pivotal-cloud-foundry/products/app-pl...