Ranjita Bhagwan – Researcher, Microsoft Research – India
Networks and networked applications depend on several pieces of configuration information to operate correctly. Such information resides in routers, firewalls, and end hosts, among other places. Incorrect information, or misconfiguration, could interfere with the running of networked applications. This problem is particularly acute in consumer settings such as home networks, where there is a huge diversity of network elements and applications coupled with the absence of network administrators.
To address this problem, we present NetPrints, a system that leverages shared knowledge in a population of users to diagnose and resolve misconfigurations. Basically, if a user has a working network configuration for an application or has determined how to rectify a problem, we would like this knowledge to be made available automatically to another user who is experiencing the same problem. NetPrints accomplishes this task by applying decision tree based learning on working and non-working configuration snapshots and by using network traffic based problem signatures to index into configuration changes made by users to fix problems. We describe the design and implementation of NetPrints, and demonstrate its effectiveness in diagnosing a variety of home networking problems reported by users.
Ranjita Bhagwan is a researcher at Microsoft Research, Bangalore, India. Previously she was a Research Staff Member at IBM T.J. Watson Research Center. Her interests lie in the design and implementation of networked systems. Currently, her focus is on managing network-specific configuration both in the Home and the Enterprise. Ranjita has a PhD in Computer Engineering from UC San Diego and a BTech in Computer Science and Engineering from IIT Kharagpur.