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RPECA-Rumor Propagation Based Eventual Consistency Assessment Algorithm

机译:基于RPECA - 谣言的传播基于最终一致性评估算法

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Replicating data across servers or storages in different data centers allows using data closer to the client and reducing latency for applications, In addition, it also increases the availability in the event of one or some datacenters failure. Hence, replica consistency among all nodes is a major consideration when designing high-availability across-domain datacenters. Even lots of mechanisms are proposed to reach this consistency target, we believe knowing the degree of consistency is helpful to an application developer as the dimension of uncertainty is reduced: The quality of service (QoS) becomes, to some degree, predictable. For this purpose, this paper proposes a novel algorithm called RPECA which can be applied to monitor consistency behaviors in a cost-efficient way. RPECA is based on theory of rumor propagation in complex networks. In this paper, we focus on the probability of each node's specific status in the network (Ignorant, Spreader or Stifler). Based on the discrete-time markov chain model technique, we apply a set of topology-independent equations to describe the microscope dynamic property of each node at any given time. Besides, we construct the whole phase diagram of the rumor spreading process in SF and small-world networks to simulate consistency behavior. In the experimental part, on one hand, the numerical results of our RPECA method could almost coincide with the empirical results of Monte Carlo (MC) simulations, which proves that our algorithm could simulated the whole phase diagram correctly. On the other hand, since the numerical results could be solved with less iterations, our RPECA algorithm could significantly outperform MC method with respect to time complexity.
机译:在不同数据中心跨服务器或存储复制数据允许使用更靠近客户端的数据并减少应用程序的延迟,此外,它还增加了一个或某个数据中心失败的活动中的可用性。因此,所有节点之间的副本一致性是在跨域数据中心设计高可用性时的主要考虑因素。提出了许多机制来达到这种一致性目标,我们相信了解一致性对应用程序开发人员有所帮助,因为不确定的维度降低:服务质量(QoS)变为某种程度,可预测。为此目的,本文提出了一种名为RPECA的新型算法,其可以应用于以成本效益的方式监控一致性行为。 RPECA基于复杂网络中的谣言传播理论。在本文中,我们专注于每个节点在网络中的特定状态的概率(无知,吊具或太空人)。基于离散时间马尔可夫链模型技术,我们应用一组独立的等式,以描述任何给定时间的每个节点的显微镜动态特性。此外,我们构建了SF和小世界网络中谣言扩展过程的整个相图,以模拟一致性行为。在实验部分,一方面,我们的RPECA方法的数值结果几乎可以与Monte Carlo(MC)模拟的经验结果一致,这证明了我们的算法可以正确模拟整个相图。另一方面,由于可以用更少的迭代解决数值结果,因此我们的RPECA算法可以显着优于MC方法的时间复杂性。

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