《计算机应用研究》|Application Research of Computers

基于改进Eclat算法的资源池节点异常模式挖掘

Mining anomaly pattern of nodes in resource pool based on improved Eclat algorithm

免费全文下载 (已被下载 次)  
获取PDF全文
作者 高强,张凤荔,陈学勤,王馨云,耿贞伟,周帆
机构 1.电子科技大学 信息与软件工程学院,成都 610054;2.云南电网有限责任公司 信息中心,昆明 650217
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2018)02-0333-06
DOI 10.3969/j.issn.1001-3695.2018.02.003
摘要 云计算环境中,资源池节点异常模式挖掘对于快速诊断节点状态具有重要作用。针对云环境下计算资源池、存储资源池、网络资源池节点数据特征,对资源池节点状态信息进行预处理,利用关联规则算法挖掘资源池节点参数状态信息之间的关联关系,如高位—高位和低位—高位模式等。提出了一种基于限制属性连接并具有垂直数据格式的关联规则算法i-Eclat算法。i-Eclat算法通过转换资源池节点状态数据格式、建立非频繁2-项集以减少连接次数,并构建信息存储结构体来限制冗余属性连接。实验表明,所提出的方法可以有效发现云计算资源池节点之间的隐藏关系;同时,i-Eclat比经典算法计算性能更优,特别是针对较大数据集的处理。
关键词 模式异常挖掘;关联规则;资源池;i-Eclat算法;云计算
基金项目 国家自然科学基金资助项目(61602097,61272527)
四川省科技支撑计划资助项目(2016GZ0065,2016GZ0063)
中央高校基本科研业务费资助项目(ZYGX2015J072)
本文URL http://www.arocmag.com/article/01-2018-02-003.html
英文标题 Mining anomaly pattern of nodes in resource pool based on improved Eclat algorithm
作者英文名 Gao Qiang, Zhang Fengli, Chen Xueqin, Wang Xinyun, Geng Zhenwei, Zhou Fan
机构英文名 1.SchoolofInformation&SoftwareEngineering,UniversityofElectronicScience&TechnologyofChina,Chengdu610054,China;2.InformationCenter,YunnanPowerGridCo.,LTD.,Kunming650217,China
英文摘要 In cloud computing, mining anomaly pattern of nodes plays an important role in promptly diagnosing node states of resource pool.According to the data characters of computing resource, storage resource and network resource, it preprocessed the information of node status.And it used association rule algorithm to mine the rules among the nodes’ parameter status of resource pool, e.g., “high level-high level” and “low level-high level”.This paper proposed an improved Eclat algorithm, called i-Eclat, which based on vertical data format of restrictive attribute connection.i-Eclat reduced the number of connections by transforming the format of state data and constructing a non-frequent 2-itemsets, as well as it constructed the storage structure to limit the redundant attribute connections.Extensive experiments have been conducted to demonstrate that this method can find the hiding rules among resource pool’s nodes.And it also shows that i-Eclat outperforms traditional methods on computing efficiency, especially on processing big data sets.
英文关键词 anomaly pattern mining; association rule; resource pool; i-Eclat algorithm; cloud computing
参考文献 查看稿件参考文献
  [1] 李乔, 郑啸. 云计算研究现状综述[J] . 计算机科学, 2011, 38(4):32-37.
[2] 戴元顺. 云计算技术简述[J] . 信息通信技术, 2010, 4(2):29-35.
[3] Bahrami M, Singhal M. The role of cloud computing architecture in big data[M] //Information Granularity, Big Data, and Computational Intelligence. [S. l. ] :Springer International Publishing, 2015:275-295.
[4] Agrawal R, Imielinski T, Swami A. Mining associations between sets of items in massive databases[C] //Proc of ACM-SIGMOD International Conference on Management of Data. New York:ACM Press, 1993:207-216.
[5] Agrawal R, Srikant R. Fast algorithms for mining association rules in large databases[C] //Proc of International Conference on Very Large Data Bases. San Francisco:Morgan Kaufmann Publishers Inc, 1994:487-499.
[6] Han Jiawei, Pei Jian, Yin Yiwen. Mining frequent patterns without candidate generation[J] . ACM Sigmod Record, 2000, 29(2):1-12.
[7] Zaki M J. Scalable algorithms for association mining[J] . IEEE Trans on Knowledge & Data Engineering, 2000, 12(3):372-390.
[8] 熊忠阳, 陈培恩, 张玉芳. 基于散列布尔矩阵的关联规则Eclat改进算法[J] . 计算机应用研究, 2010, 27(4):1323-1325.
[9] 冯培恩, 刘屿, 邱清盈, 等. 提高Eclat算法效率的策略[J] . 浙江大学学报:工学版, 2013, 47(2):223-230.
[10] Yu Xiaomei, Wang Hong. Improvement of Eclat algorithm based on support in frequent itemset mining[J] . Journal of Computers, 2014, 9(9):2116-2123.
[11] 丁三军, 薛宇, 王朝霞, 等. 基于模糊数据挖掘的虚拟环境主机故障预测[J] . 计算机工程, 2015, 41(11):202-206.
[12] Mabu S, Chen Ci, Lu Nannan, et al. An intrusion-detection model based on fuzzy class-association-rule mining using genetic network programming[J] . IEEE Trans on Systems Man & Cybernetics Part C, 2011, 41(1):130-139.
[13] Premalatha K, Natarajan A M. Chi-square test for anomaly detection in XML documents using negative association rules[J] . Computer & Information Science, 2009, 2(1):35-42.
[14] 任涛. 面向IaaS的虚拟机异常检测系统研究[D] . 重庆:重庆大学, 2014.
[15] Dean D J, Nguyen H, Gu Xiaohui. UBL:unsupervised behavior learning for predicting performance anomalies in virtualized cloud systems[C] //Proc of International Conference on Autonomic Computing. 2012:191-200.
[16] Wang Chengwei, Talwar V, Schwan K, et al. Online detection of utility cloud anomalies using metric distributions[C] //Proc of IEEE Network Operations and Management Symposium. 2010:96-103.
[17] Wang Chengwei. EbAT:online methods for detecting utility cloud anomalies[C] //Proc of Middleware Doctoral Symposium. 2009:1-6.
收稿日期 2016/10/18
修回日期 2016/12/1
页码 333-338
中图分类号 TP391
文献标志码 A