A Light-weight and Unsupervised Method for Near Real-time Behavioral
  Analysis using Operational Data Measurement
Tom Richard Vargis Siavash Ghiasvand
Monitoring the status of large computing systems is essential to identify
unexpected behavior and improve their performance and uptime. However, due to
the large-scale and distributed design of such computing systems as well as a
large number of monitoring parameters, automated monitoring methods should be
applied. Such automatic monitoring methods should also have the ability to
adapt themselves to the continuous changes in the computing system. In
addition, they should be able to identify behavioral anomalies in useful time,
to perform appropriate reactions. This work proposes a general lightweight and
unsupervised method for near real-time anomaly detection using operational data
measurement on large computing systems. The proposed model requires as little
as 4 hours of data and 50 epochs for each training process to accurately
resemble the behavioral pattern of computing systems.