The consensus problem has become a key issue in the field of collaborative telemedicine systems because of the need to guarantee the consistency of shared data. In this paper, we focus on the performance of consensus algorithms. First, we studied, in the literature, the most well-known algorithms in the domain. Experiments on these algorithms allowed us to propose a new algorithm that enhances the performance of consensus in different situations. During 2014, we presented our very first initial thoughts to enhance the performance of the consensus algorithms, but the proposed solution gave very moderate results. The goal of this paper is to present a new enhanced consensus algorithm, named Fouad, Lionel and J.-Christophe (FLC). This new algorithm was built on the architecture of the Mostefaoui-Raynal (MR) consensus algorithm and integrates new features and some known techniques in order to enhance the performance of consensus in situations where process crashes are present in the system. The results from our experiments running on the simulation platform Neko show that the FLC algorithm gives the best performance when using a multicast network model on different scenarios: in the first scenario, where there are no process crashes nor wrong suspicion, and even in the second one, where multiple simultaneous process crashes take place in the system.

中文翻译：

---

达成更快的容错共识以在协作环境中保持数据一致性

由于需要保证共享数据的一致性，共识问题已经成为协同远程医疗系统领域的一个关键问题。在本文中，我们关注共识算法的性能。首先，我们在文献中研究了该领域最知名的算法。对这些算法的实验使我们能够提出一种新算法，以提高在不同情况下的共识性能。在 2014 年，我们提出了提高共识算法性能的第一个初步想法，但提出的解决方案给出了非常温和的结果。本文的目标是提出一种新的增强共识算法，名为 Fouad、Lionel 和 J.-Christophe (FLC)。这种新算法建立在 Mostefaoui-Raynal (MR) 共识算法的架构之上，并集成了新特性和一些已知技术，以在系统中存在进程崩溃的情况下提高共识的性能。我们在仿真平台 Neko 上运行的实验结果表明，在不同场景下使用多播网络模型时，FLC 算法提供了最佳性能：在第一个场景中，没有进程崩溃或错误怀疑，甚至在第二个场景中一，系统中同时发生多个进程崩溃。