Consistent maintenance of distributed data is important in application areas like groupware and for runtime support for parallel computing. We examine the performance of different multicast based methods for maintaining the consistency of distributed data depending on the network topology and concurrency.

Our prototype software implements ordered, reliable multicasts on top of the unreliable IP broad- or multicast with three different methods (Master-Slave, Token Exchange on Demand, Totem Single Ring). This paper shows measurement results for the efficiency and scalability of the three methods in different topologies.

The measurements confirm earlier analytical results. Totem behaves well in large networks with many concurrent senders. The overhead of Token on Demand and of the Master-Slave algorithm is almost the same. Also we could not find an indication for the often-read opinion that the Master-Slave approach scales worse because of the central bottleneck.