ML Case Study | OpenStack Cluster Monitoring System

Client: A USA-based, privately held computer software company founded to market commercial support and related services for Linux and related projects.

Domain: Computer Software Compnay

Offering: Implementing Machine Learning algorithms to discover errors in the cluster makes it possible to save time by drawing the administrator’s attention to the problem as soon as it happens. System faults can even be predicted in advance.

Challenge: The main problems were the huge amount of data, the complicated layer structure and a wide array of file formats. Therefore, the DataArt team decided to train a classifier using a model in which the parameters were words from a test sample with a value equal to the number of keywords in the text.

The Solution: The Borgos team developed an intricate solution, which was aimed at predicting potential failures as quickly as possible, basically as they occur. Therefore, it provides our client much more time to fix issues.The technology stack includes Apache Spark, Kafka, WEKA, Scala, Java languages.