Richard Mortier, Rebecca Isaacs, and Paul Barham
Enterprise networks contain hundreds, if not thousands, of cooperative end-systems. This paper advocates devoting a small fraction of their idle cycles, free disk space and network bandwidth to create Anemone , a rich platform for network management. This contrasts with current approaches that rely on traffic statistics provided by network devices. Anemone infers network-wide traffic patterns by synthesizing end-system flow statistics with dynamic topology information obtained by passive snooping of IP routing protocols. Understanding the effect of the network on individual applications' end-to-end performance requires data only available in the end-systems actually hosting applications. Consequently, we claim that augmenting end-systems with in-band monitoring, creating an overlay of real-time `traffic sensors' will provide a more complete view of the network, support sophisticated network management queries,and supply the global statistics necessary to automate network control. This paper describes Anemone, discusses potential benefits and challenges, and presents an initial simulation of the platform.
In Proceedings of the ACM SIGCOMM Workshop on Mining Network Data (MineNet'05)