Quasirandom rumor spreading: An experimental analysis
Journal of Experimental Algorithmics (JEA), 2008•dl.acm.org
We empirically analyze two versions of the well-known “randomized rumor spreading”
protocol to disseminate a piece of information in networks. In the classical model, in each
round, each informed node informs a random neighbor. In the recently proposed
quasirandom variant, each node has a (cyclic) list of its neighbors. Once informed, it starts at
a random position of the list, but from then on informs its neighbors in the order of the list.
While for sparse random graphs a better performance of the quasirandom model could be …
protocol to disseminate a piece of information in networks. In the classical model, in each
round, each informed node informs a random neighbor. In the recently proposed
quasirandom variant, each node has a (cyclic) list of its neighbors. Once informed, it starts at
a random position of the list, but from then on informs its neighbors in the order of the list.
While for sparse random graphs a better performance of the quasirandom model could be …
We empirically analyze two versions of the well-known “randomized rumor spreading” protocol to disseminate a piece of information in networks. In the classical model, in each round, each informed node informs a random neighbor. In the recently proposed quasirandom variant, each node has a (cyclic) list of its neighbors. Once informed, it starts at a random position of the list, but from then on informs its neighbors in the order of the list.
While for sparse random graphs a better performance of the quasirandom model could be proven, all other results show that, independent of the structure of the lists, the same asymptotic performance guarantees hold as for the classical model.
In this work, we compare the two models experimentally. Not only does this show that the quasirandom model generally is faster, but it also shows that the runtime is more concentrated around the mean. This is surprising given that much fewer random bits are used in the quasirandom process.
These advantages are also observed in a lossy communication model, where each transmission does not reach its target with a certain probability, and in an asynchronous model, where nodes send at random times drawn from an exponential distribution. We also show that typically the particular structure of the lists has little influence on the efficiency.
ACM Digital Library