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首页> 外文会议>International Conference on High Performance Computing and Communications(HPCC 2005); 20050921-23; Sorrento(IT) >Towards a Bayesian Statistical Model for the Classification of the Causes of Data Loss
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Towards a Bayesian Statistical Model for the Classification of the Causes of Data Loss

机译：建立贝叶斯统计模型对数据丢失原因进行分类

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摘要

Given the critical nature of communications in computational Grids it is important to develop efficient, intelligent, and adaptive communication mechanisms. An important milestone on this path is the development of classification mechanisms that can distinguish between the various causes of data loss in cluster and Grid environments. The idea is to use the classification mechanism to determine if data loss is caused by contention within the network or if the cause lies outside of the network domain. If it is outside of the network domain, then it is not necessary to trigger aggressive congestion-control mechanisms. Thus the goal is to operate the data transfer at the highest possible rate by only backing off aggressively when the data loss is classified as being network related. In this paper, we investigate one promising approach to developing such classification mechanisms based on the analysis of the patterns of packet loss and the application of Bayesian statistics.
机译：考虑到通信在计算网格中的关键性质，开发高效，智能和自适应的通信机制非常重要。在这条道路上的一个重要里程碑是分类机制的发展，可以区分集群和网格环境中数据丢失的各种原因。想法是使用分类机制来确定数据丢失是由网络内的争用引起的，还是原因在于网络域之外。如果它在网络域之外，则无需触发积极的拥塞控制机制。因此，目标是通过仅在数据丢失被归类为与网络有关的情况下主动回退来以最高可能的速率操作数据传输。在本文中，我们将基于数据包丢失模式的分析和贝叶斯统计的应用，研究一种开发这种分类机制的有前途的方法。

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