LISA '07 – Abstract
Pp. 275–289 of the Proceedings
Network Patterns in Cfengine and Scalable Data Aggregation
Mark Burgess and Matt Disney, Oslo University College; Rolf Stadler, KTH Royal Institute of Technology, Stockholm
Abstract
Network patterns are based on generic algorithms that execute on
tree-based overlays. A set of such patterns has been developed at KTH
to support distributed monitoring in networks with non-trivial
topologies. We consider the use of this approach in logical peer
networks in cfengine as a way of scaling aggregation of data to large
organizations. Use of `deep' network structures can lead to temporal
anomalies. We show how to minimize temporal fragmentation during data
aggregation by using time offsets and what effect these choices might
have on power consumption. We offer proof of concept for this
technology to initiate either multicast or inverse multicast pulses
through sensor networks.
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