Scalable multicast forwarding

Abstract

The goal of this work is to develop efficient algorithms and data structures suitable for software-based forwarding of muRicast datagrams. The algorithms should scale to very large numbers of simultaneously active multicast groups. In an example configuration with 32 interfaces a new algorithm can forward 20,000 simultaneously active multicast groups using only 64Kbytes of memory and a leak probability of 2.4x10 -s. This algorithm can perform almost 10 million forwarding decisions per second on a 800MHz Penfium III processorThe task of a multicast-forwarding engine is to forward packets along branches of a distribution tree. The branches of the tree consists of network segments and the nodes in the tree are routers. There are a few different classes of distribution trees. For simpficity only bidirectional trees will be considered in this discussion.Abstractly, a bidirectional-distribetion tree is a bidirectional acyclic graph. R has a root called center, core or, rendez-vous point. Forwarding is performed by having traffic flow up the tree towards the center, and down the tree towards the receivers.A router must for each distribution tree maintain a list of interfaces that connect the tree. To forward a packet, first find the interface list corresponding to the group. If the packet arrived on an interface in the list, then, forward the packet over the remaining interfaces in the list.It is possible to perform this forwarding function using probabilistic methods. Observe that forwarding will be successfuUy performed if the interface list includes at least all interfaces that the packet must be forwarded over. Since the interface list can potentially be to long, a packet will occasionally leak out the wrong interface.However, at a minimum, packets will always be forwarded over the distribution tree. Since the leak probability can be made arbitrarily small, this may not be a problem in practice.Assume a network configuration where packets are allowed to leak with a maximum probability P. The probability that a leaked packet will leak through the next router is p2, and through a third p3. Thus, leak probabifity decays exponentially as we travel down (or up) the tree. Also, hosts configure their network adaptors to filter out unwanted multicasts. If a packet should sneak through this hardware filter, then software filtering sets in. Thus, hosts on leaf networks are already prepared to handle the problem with traffic leaks.The suggested probabilisfic-forwardin g algorithm has an efficient implementation based on Bloom filters. The algorithm uses a fixed small number of memory references (e.g eight) and a small number of multiplications and shift instructions. Memory requirements scale finearly with the maximum allowed number of groups and is also independent of the number of interfaces. Surprisingly, memory requirements are independent of group address length. This is a result of that addresses are not stored in the data structure. Thus, 112 bit IPv6 addresses use no more forwarding memory than 28 bit IPv4 addresses.A...

Theory and Practice of Bloom Filters for Distributed Systems

“…A multicast router maps an incoming multicast packet to outgoing interfaces based on the multicast address. Initially, Grönvall suggests an alternative multicast forwarding technique using Bloom filters [81]. In this technique, a router has a Bloom filter for each outgoing interface.…”

Improving the Detection of On-Line Vertical Port Scan in IP Traffic

“…We can for example mention the distributed web cache sharing proposed by Fan et al in (Fan, 2000) or the detecting loops algorithm introduced by Whitaker and Wetherall (Whitaker, 2002), in the context of packet switching. Bloom filters are well adapted to many other applications such as multicast (Gronvall, 2002) resource routing in peer-to-peer networks (Druschel, 2001), (Stoica, 2001) and queue management (Dilip, 2001). Bloom filters have been used by Estan and Varghese (Estan, 2002) to detect large flows.…”

Improving the detection of on-line vertical port scan in IP traffic

“…We can for example mention the distributed web cache sharing proposed by Fan et al in (Fan, 2000) or the detecting loops algorithm introduced by Whitaker and Wetherall (Whitaker, 2002), in the context of packet switching. Bloom filters are well adapted to many other applications such as multicast (Gronvall, 2002) resource routing in peer-to-peer networks (Druschel, 2001), (Stoica, 2001) and queue management (Dilip, 2001). Bloom filters have been used by Estan and Varghese (Estan, 2002) to detect large flows.…”