Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICDMW.2014.150 | ICDM Workshops |
Keywords | Field | DocType |
optimisation,overlay networks,expansion property,random processes,search heuristic,decentralised service,randomised communication,expander membership protocol,peer sampling service,fault-tolerant service,epidemic protocols,network overlay topology,telecommunication network topology,search problems,expander graphs,epidemic membership protocol,expander graph quality optimisation,graph theory,extreme-scale computing,distributed system,random graph,decentralised algorithms,indexes,network topology,protocols,topology,convergence | Convergence (routing),Data mining,Expander graph,Random graph,Computer science,Computer network,Robustness (computer science),Artificial intelligence,Distributed computing,Graph theory,Heuristic,Network topology,Machine learning,Overlay network | Conference |
ISBN | Citations | PageRank |
978-1-4799-4275-6 | 2 | 0.39 |
References | Authors | |
18 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pasu Poonpakdee | 1 | 17 | 2.76 |
| Giuseppe Di Fatta | 2 | 529 | 39.23 |