Abstract
Analysis of network logs collected from network devices is presented. The objective is to understand and determine the important network events and infer the possible root causes of those network events. The volume of network data is very high and often it can be quite challenging to filter out only the key important messages. We have developed ML / NLP based techniques to extract the underlying statistical templates of the SYSLOG messages, and secondly identify anomalous patterns observed in the SYSLOG events which can be useful to recommend suitable remedial actions. The proposed solution is under evaluation by network operations. Mouli Chandramouli
Mouli Chandramouli is currently working as a Data Scientist at Cisco Systems, Bangalore in the area of application of Machine Learning algorithms for analytics of Network Telemetry and Network Inference. He is also a Visiting Professor at the RBCCPS, IISc. He received his M. S. and Ph.D. from University of Arizona, Tuscon, AZ in the area of Stochastic Process and Queueing Theory. Prior work experience at AT&T Bell Laboratories, Holmdel, NJ, Bell Communications Research, NJ in the area of network performance modelling and Dynamicsoft, NJ a startup company focussed on VOIP products based on SIP Protocol which was acquired by Cisco Systems. At Cisco Systems, his work has been is in the areas of MPLS networks, Energy Management for networking devices and distributed embedded network analytics algorithms.