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Xen and the art of virtualization
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
PlanetLab: an overlay testbed for broad-coverage services
ACM SIGCOMM Computer Communication Review
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Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Adaptive distributed monitoring with accuracy objectives
Proceedings of the 2006 SIGCOMM workshop on Internet network management
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ACM SIGCOMM Computer Communication Review
Design and Validation of an Analytical Model to Evaluate Monitoring Frameworks Limits
ICN '09 Proceedings of the 2009 Eighth International Conference on Networks
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Proceedings of the 1st ACM workshop on Virtualized infrastructure systems and architectures
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A-GAP: An Adaptive Protocol for Continuous Network Monitoring with Accuracy Objectives
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A scalable monitoring approach based on aggregation and refinement
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INOX: a managed service platform for inter-connected smart objects
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Future Generation Computer Systems
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Virtual Networks are characterised as highly dynamic network environments, where topologies and nodes adapt rapidly to changes in user and service demands, user location and context changes, or resource constraints. This paper presents a rigorous assessment of an Information Management Overlay (IMO) suitable for monitoring virtual networks. The IMO system allows complex monitoring tasks to be performed in a scalable manner in order that the network manager can monitor the desired network properties in a way which is accurate, scalable, and low-bandwidth. The monitoring architecture is decentralised, not resource intensive (in terms of memory or CPU), and adaptable to a wide range of virtual topologies. The IMO architecture uses Information Aggregation Points and Information Collection Points to scalably aggregate, filter, and collect data. The IMO is tested for scalability in both simulation (with over 35,000 thousand nodes) and on a custom-made virtual network testbed (with over 700 virtual routers), where we show that it performs well at a variety of monitoring tasks. This paper shows that the system is scalable and that intelligent configuration of the monitoring system greatly improves its efficiency. Furthermore, filtering can help reduce the monitoring load, however, the improvement brought in by filtering is greatly dependent on the nature of the monitoring task.