Sampling Strategies for Epidemic-Style Information Dissemination

We consider epidemic-style information dissemination strategies that leverage the nonuniformity of host distribution over subnets (e.g., IP subnets) to optimize the information spread. Such epidemic-style strategies are based on random sampling of target hosts according to a sampling rule. In this paper, the objective is to optimize the information spread with respect to minimizing the total number of samplings to reach a target fraction of the host population. This is of general interest for the design of efficient information dissemination systems and more specifically, to identify requirements for the containment of worms that use subnet preference scanning strategies. We first identify the optimum number of samplings to reach a target fraction of hosts, given global information about the host distribution over subnets. We show that the optimum can be achieved by either a dynamic strategy for which the per host sampling rate over subnets is allowed to vary over time or by a static strategy for which the sampling over subnets is fixed. These results appear to be novel and are informative about (a) what best possible performance is achievable and (b) what factors determine the performance gain over oblivious strategies such as uniform random scanning. We then consider several simple, online sampling strategies that require only local knowledge, where each host biases sampling based on its observed sampling outcomes and keeps only O(1) state at any point in time. Using real datasets from several large-scale Internet measurements, we evaluate the significance of the factors revealed by our analytical results on the sampling efficiency.