Scalability and robustness of pull-based anti-entropy distribution model

Abstract

There are several alternative mechanisms for disseminating information among a group of participants in a distributed environment. An efficient model is to use epidemic algorithms that involve pair-wise propagation of information. These algorithms are based on the theory of epidemics which studies the spreading of infectious diseases through a population. Epidemic protocols are simple, scale well and robust again common failures, and provide eventual consistency as well. They have been mainly utilized in a large set of applications for resolving inconsistencies in distributed database updates, failure detection, reliable multicasting, network news distribution, scalable system management, and resource discovery. A popular distribution model based on the theory of epidemics is the anti-entropy. In this study, we focus on pull-based anti-entropy model used for multicast reliability as a case study, demonstrate its scalability and robustness, and give our comparative simulation results discussing the performance of the approach on a range of typical scenarios.