【作者】 景京；

【导师】 石磊； 卫琳；

【作者基本信息】 郑州大学 ， 计算机应用技术， 2013， 硕士

【摘要】 随着全球范围内分布式系统发展进程的加快及其构造的日趋复杂,越来越多的失效现象层出不穷,严重威胁着系统的高可用性。失效检测作为保障分布式系统高可用性的核心技术之一,已成为研究热点方向。目前,失效检测已被广泛应用于通信协议、Web服务器、集群管理和失效恢复等方面。同时,失效检测在无线网络、云计算平台、大数据等领域也得到了重要应用。通过研究现有自适应失效检测算法及其实现过程可以发现,失效检测的本质是研究非线性时间序列的预测问题。而最小二乘支持向量回归算法是非线性时间序列预测领域的有效方法之一,在小样本空间和预测精度等方面有一定优势。传统失效检测算法基于概率统计模型计算下一心跳的到达时间,受限于某种概率分布且需要较大数据量,存在一定局限性。而采用最小二乘支持向量回归算法进行失效检测预测恰能弥补其不足。在实际应用中常面临小样本空间,为此本文提出FD-LSSVR模型并进行了讨论。针对现有自适应失效检测算法需考虑离群值这一问题,模型引入聚类分析法综合考虑心跳延迟和权值两个指标来过滤对预测结果影响较大的离群值,其中权值的分配满足幂律分布。实验结果表明,本文提出的自适应失效检测模型FD-LSSVR不仅满足◇P检测级别,而且在检测时间和准确度方面均有较好表现,可用以缓解子网络延迟对失效检测的影响。另外,FD-LSSVR算法可根据实际情况,通过调整参数α值的大小来满足多种不同应用的需求,具有一定的灵活性。更多还原

【Abstract】 With the acceleration of the developmental process of the worldwide distributed systems and its more complex structure than before, more and more failure phenomena happen that seriously threats the high availability of systems. As one of the core technology of protecting the high availability of distributed systems, failure detection has gradually become a hot research direction. Recently failure detection has been widely used in communication protocols, Web server, cluster management and failure recovery etc. At the same time, failure detection has also been an important application in the fields of wireless network, cloud computing platform, and big data etc.Throughout the existing adaptive failure detection algorithm and in-depth research of the process of failure detection, we can understand that the nature of failure detection belongs to nonlinear time series prediction problem. The least squares support vector algorithm is one of the effective methods of nonlinear time series prediction, which has certain advantages in small sample space and forecast accuracy. The traditional adaptive failure detection algorithm is generally based on probability statistical model which is limited to some probability distribution and requires a large amount of data to calculate the arrival time of next heartbeat message, for what there are some limitations. While the use of least squares support vector regression algorithm for failure detection forecast just to make up for its shortcomings. Practical applications are often faced by small sample space, so this paper proposed and discussed FD-LSSVR (Failure Detection-Least Squares Sport Vector Regression) model, which also introduces clustering analysis method that considering the two indicators of heartbeat delays and weights to remove outliers that have a greater impact on predicted results for that the existing adaptive failure detection algorithms need to consider outliers. The weights assigned to meet the power-law distribution.The experimental results show that the model not only satisfies the OP detection level, but also performs great at both detection time and accuracy, for what it can be used to alleviate the sub-network delay for failure detection. In addition, according to actual situation, FD-LSSVR can meet the needs of a variety of different applications by adjusting the size of the parameter a values, with a certain degree of flexibility.更多还原