Constellation is a system that uses packet information monitored at endsystems in conjunction with machine learning techniques to construct models of network service behaviour and dependency, which can be queried to determine likely causes for service misbehaviour. This is a joint project involving researchers at Cambridge and Silicon Valley.
Research in the Constellation project focuses on innovative methods to discover dependencies between services and hosts in enterprise networks and on end-user machines. The objective is to enhance the ability of network administrators and end-users to diagnose faults, detect changes, and be alerted to configuration problems. The approach is based on discovering local dependencies between the input and output packets in each computer in the network, relying only on passive observation of these packets, and on information such as source/destination, request/response, and time stamps. The project is a joint effort between the Cambridge Lab and the Silicon Valley Lab.
- Aleksander Simma, Moises Goldszmidt, John MacCormick, Paul Barham, Richard Black, Rebecca Isaacs, and Richard Mortier, CT-NOR: Representing and reasoning about events in continuous time, in International Conference on Uncertainty in Artificial Intelligence (UAI), Helsinki, Finland, July 2008.
- Paul Barham, Richard Black, Moises Goldszmidt, Rebecca Isaacs, John MacCormick, Richard Mortier, and Aleksandr Simma, Constellation: automated discovery of service and host dependencies in networked systems, no. MSR-TR-2008-67, April 2008.
- Paramvir Bahl, Paul Barham, Richard Black, Ranveer Chandra, Moises Goldszmidt, Rebecca Isaacs, Srikanth Kandula, Lun Li, John MacCormick, David A. Maltz, Richard Mortier, Mike Wawrzoniak, and Ming Zhang, Discovering Dependencies for Network Management, in Workshop on Hot Topics in Networks (HotNets-V), Association for Computing Machinery, Inc., Irvine, California, November 2006.