一种基于语义吸引的节点规模估计方法

Network Size Estimation Method Based Semantic Attraction

节点规模是各种分布式应用的基础信息,节点波动的大规模网络环境要求节点规模估计方法具有较高的估计精度和较强的鲁棒性,已有的节点规模估计方法多侧重于某个方面的优化而未能充分权衡计算精度和鲁棒性.提出一种基于语义吸引的节点规模估计方法——SEBSA(a network size estimation method based semanticattraction).SEBSA将每个节点标识所对应的实数区间上的哈希值作为语义信息,节点通过与哈希值临近的节点周期性地交换哈希空间上的邻居信息,以快速吸引与自己哈希值最近的一组节点,测量该组节点哈希值的平均间距以估计节点规模.理论分析和实验结果表明,相对于已有方法,SEBSA在节点频繁波动的网络环境中仍然能够快速地提供准确的节点规模信息.

Network size is the fundamental information of the distributed applications. Network size estimation methods must feature both high accuracy and adequate robustness in order to adapt to a large environment with a high node chum. Considering the fact that the existing network size estimation methods mainly focus on single optimization objective and fail to ensure accuracy and robustness simultaneously, a network size estimation method based semantic attraction--SEBSA is proposed in this paper, As the semantic information in SEBSA, hash values are hashed in real intervals by the peers＇ identifies. The peers with adjacent hash values in SEBSA periodically exchange hash neighbors to attract the most adjacent peers in a hash space quickly. Meanwhile, every peer computes the average spacing among hash values of the hash neighbors to estimate network size. Theoretic analysis and experimental results reveal that compared with existing size estimation methods, SEBSA can provide accurate size estimation information quickly even in continually fluctuating network environment.