Efficient fitting of long-tailed data sets into hyperexponential distributions

被引:0
|
作者
Riska, A [1 ]
Diev, V [1 ]
Smirni, E [1 ]
机构
[1] Coll William & Mary, Dept Comp Sci, Williamsburg, VA 23187 USA
来源
GLOBECOM'02: IEEE GLOBAL TELECOMMUNICATIONS CONFERENCE, VOLS 1-3, CONFERENCE RECORDS: THE WORLD CONVERGES | 2002年
关键词
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We propose a new technique for fitting long-tailed data sets into hyperexponential distributions. The approach partitions the data set in a divide and conquer fashion and uses the Expectation-Maximization (EM) algorithm to fit the data of each partition into a hyperexponential distribution. The fitting results of all partitions are combined to generate the fitting for the entire data set. The new method is accurate and efficienta nd allows one to apply existing analytic tools to analyze the behavior of queueing systems that operate under workloads that exhibit long-tail behavior, such as queues in Internet-related systems.
引用
收藏
页码:2513 / 2517
页数:5
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