Understanding Data Centers from Logs: Leveraging External Knowledge for Distant Supervision

Data centers are a crucial component of modern IT ecosystems. Their size and complexity present challenges in terms of maintain- ing and understanding knowledge about them. In this work we propose a novel methodology to create a semantic representation of a data cen- ter, leveraging graph-based data, external semantic knowledge, as well as continuous input and refinement captured with a human-in-the-loop interaction. Additionally, we specifically demonstrate the advantage of leveraging external knowledge to bootstrap the process. The main moti- vation behind the work is to support the task of migrating data centers, logically and/or physically, where the subject matter expert needs to identify the function of each node - a server, a virtual machine, a printer, etc - in the data center, which is not necessarily directly available in the data and to be able to plan a safe switch-off and relocation of a cluster of nodes. We test our method against two real-world datasets and show that we are able to correctly identify the function of each node in a data center with high performance.