东篱科研大数据发现系统（DRDS）

位置：成果数据库 > 期刊 > 期刊详情页

基于自适应随机行走的可扩展无偏抽样方法

期刊名称：软件学报
时间：0
页码：630-643
语言：中文
分类：TP393[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
作者机构：[1]国防科学技术大学计算机学院并行与分布处理国家重点实验室,湖南长沙410073, [2]工程兵指挥学院计算机教研室,江苏徐州221000
相关基金：Supported by the National Natural Science Foundation of China under Grant Nos.60873215, 60621003 （国家自然科学基金）; the National Basic Research Program of China under Grant No.2005CB321801 （国家重点基础研究发展计划（973））; the Specialized Research Fund for the Doctoral Program of Higher Education of China No.200899980003 （高等学校博士学科点专项科研基金）; the Foundation for the Author of National Excellent Doctoral Dissertation of China under Grant No.200141 （高等学校全国优秀博士学位论文作者专项）; the Graduate Research Innovation Fund of NUDT of China under Grant No.B080602 （国防科学技术大学优秀研究生创新资助）.
相关项目：基于覆盖网的快速自适应数据分发机理研究

关键词：无偏抽样, 非结构化P2P系统, 随机算法, 可扩展性, unbiased sampling, unstructured P2P system, randomized algorithm, scalability

中文摘要：

针对非结构化P2P系统中可扩展的快速无偏抽样问题，提出了一种基于多个peer自适应随机行走的抽样方法SMARW．在该方法中，基于代理随机行走选择一组临时的peer执行抽样过程，一次产生一组可调数目的抽样节点，提高了抽样速度，选择每次产生的抽样节点作为临时peer进行新的抽样过程，这种简单的方法可以保证系统具有近似最优的系统负载均衡程度同时，SMARW利用自适应的分布式随机行走修正过程提高抽样过程的收敛速度．理论分析和模拟测试表明，SMARW方法具有较高的无偏抽样能力以及近似最优的系统负载均衡程度．

英文摘要：

To deal with the scalable and fast unbiased sampling problems in unstructured P2P systems, a sampling method based on multi-peer adaptive random walk （SMARW） is proposed. In the method, based on the multi-peer random walk process, a set of provisional peers are selected as agents which start the sampling processes, by which the sampling process is speeded up with receiving a set of tunable number samples each time; Meanwhile, after receiving new samples earlier agents are replaced with these new samples which repeat the sampling process. With this simple replacement, it can be guaranteed with high probability that the system can reach the optimal load balance; furthermore, SMARW adopts an adaptive distributed random walk adjustment process to increase the convergence rate of the sampling process. A detailed theorical analysis and performance evaluation confirm that SMARW has a high level of unbiased sampling and near-optimal load balancing capability.

同期刊论文项目