Scaling of multicast trees: comments on the Chuang-Sirbu scaling law
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
A random graph model for massive graphs
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
On network-aware clustering of Web clients
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
Topology modeling via cluster graphs
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Towards capturing representative AS-level Internet topologies
SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Infrastructure tradeoffs for sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Network topology generators: degree-based vs. structural
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
BRITE: A Flexible Generator of Internet Topologies
BRITE: A Flexible Generator of Internet Topologies
The relationship between topology and protocol performance: case studies
The relationship between topology and protocol performance: case studies
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 2
On multicast trees: structure and size estimation
IEEE/ACM Transactions on Networking (TON)
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An important component of simulation-based network research is the selection of nodes to a member group, such as receivers in a multicast group or web clients in a content delivery network. In a seminal paper, Philips et al. introduce an algorithm for generating member groups with different degrees of affinity (clusteredness) and show that affinity can have a significant effect on multicast efficiency. Subsequent studies applying this algorithm have all used the algorithm's input parameter as a method for classifying and comparing affinity groups. In this paper, we propose several distance- and expansion-based analysis metrics and find them to be better measurements of the true affinity of member groups. In three separate case studies (multicast, replica placement, and sensor networks), we demonstrate the benefit of classifying member groups by their true affinity in order to predict network performance variation. By systematizing techniques for measuring affinity, we open the door for more realistic and reproducible research in studies employing affinity-based member selection techniques.