(Created page with " == Abstract == Gossiping is a widely known and successful approach to reliable communications, tolerating packet losses and link crashes. It has been extensively used in sev...") |
m (Scipediacontent moved page Draft Content 641198945 to Esposito et al 2017a) |
(No difference)
| |
Latest revision as of 11:06, 16 February 2021
Abstract
Gossiping is a widely known and successful approach to reliable communications, tolerating packet losses and link crashes. It has been extensively used in several middleware kinds, such as event notification services and application domains, like infrastructures for air traffic management, power grid control, health information exchange, just to cite some of them. Despite achieving a high loss-tolerance and scalability degrees, gossiping is affected by degraded performances and heavy traffic loads on the network. For this reason, it may be not optimal in applications where reliability must be provided jointly with timeliness and/or in congestion-prone networks. The crucial aspect for improving a gossiping scheme is deciding which nodes should receive a gossiping message, and our driving idea is to adopt a distributed strategic learning logic to determine such nodes in an efficient manner. This is able to resolve gossiping's weakness points and to achieve better performance and reduced traffic loads. This paper describes how to introduced strategic learning in a gossip scheme so as to determine the best set of nodes that can be used to send gossip messages and to optimize their utility. Such a solution has been experimentally assessed through a set of simulations demonstrating the effectiveness of the proposal.
Original document
The different versions of the original document can be found in:
Document information
Published on 01/01/2017
Volume 2017, 2017
DOI: 10.1016/j.future.2016.11.006
Licence: Other
Share this document
Keywords
claim authorship
Are you one of the authors of this document?