Expander Graph Quality Optimisation in Randomised Communication

Epidemic protocols provide a randomised communication and computation paradigm for large and extreme-scale networked systems and can be adopted to build decentralised and fault-tolerant services. They have recently been proposed for the formulation of knowledge discovery algorithms in extreme scale environments. In distributed systems they rely on membership protocols to provide a peer sampling service. Epidemic membership protocols induce a network overlay topology that continuously evolves over time, quickly converging to random graphs. This work investigates the expansion property of the series of network overlay topologies induced by epidemic membership protocols. A search heuristic is adopted for the design of a novel epidemic membership protocol. The proposed Expander Membership Protocol explicitly aims at improving the expansion quality of the overlay topologies and incorporates a connectivity recovery mechanism to overcome the known issue of multiple connected components. In the comparative analysis the proposed protocol shows a faster convergence to random graphs and greater topology connectivity robustness than the state of the art protocols, resulting in an overall better performance of global aggregation tasks.