NetSlicer: Automated and Traffic-Pattern Based Application Clustering in Datacenters.

Companies often have very limited information about the applications running in their datacenter or public/private cloud environments. As this can harm efficiency, performance, and security, many network administrators work hard to manually assign actionable description to (virtual) machines. This paper presents and evaluates NetSlicer, a machine-learning approach that enables an automated grouping of nodes into applications and their tiers. Our solution is based solely on the available network layer data which is used as part of a novel graph clustering algorithm, tailored toward the datacenter use case and accounting also for observed port numbers. For the sake of this paper, we also performed an extensive empirical measurement study, collecting actual workloads from different production datacenters (data to be released together with this paper). We find that our approach features a high accuracy.