Classification and Computation of Dependencies for Distributed Management
ISCC '00 Proceedings of the Fifth IEEE Symposium on Computers and Communications (ISCC 2000)
Path-based faliure and evolution management
NSDI'04 Proceedings of the 1st conference on Symposium on Networked Systems Design and Implementation - Volume 1
Using magpie for request extraction and workload modelling
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Towards highly reliable enterprise network services via inference of multi-level dependencies
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
What's going on?: learning communication rules in edge networks
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Macroscope: end-point approach to networked application dependency discovery
Proceedings of the 5th international conference on Emerging networking experiments and technologies
Dependency detection using a fuzzy engine
DSOM'07 Proceedings of the Distributed systems: operations and management 18th IFIP/IEEE international conference on Managing virtualization of networks and services
Automating network application dependency discovery: experiences, limitations, and new solutions
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
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The automated identification of network service dependencies remains a challenging problem in the administration of large distributed systems. Advances in developing solutions for this problem have immediate and tangible benefits to operators in the field. When the dependencies of the services in a network are better-understood, planning for and responding to system failures becomes more efficient, minimizing downtime and managing resources more effectively. This paper introduces three novel techniques to assist in the automatic identification of network service dependencies through passively monitoring and analyzing network traffic, including a logarithm-based ranking scheme aimed at more accurate detection of network service dependencies with lower false positives, an inference technique for identifying the dependencies involving infrequently used network services, and an approach for automated discovery of clusters of network services configured for load balancing or backup purposes. This paper also presents the experimental evaluation of these techniques using real-world traffic collected from a production network. The experimental results demonstrate that these techniques advance the state of the art in automated detection and inference of network service dependencies.