A Light-weight and Unsupervised Method for Near Real-time Behavioral Analysis using Operational Data Measurement

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 …

analysis automated behavior behavioral analysis computing computing systems cs.dc cs.lg data design distributed identify light measurement monitoring near parameters performance real-time scale systems unsupervised