一种业务关联的数据采集方法

doi:10.3969/j.issn.1001-2400.2013.02.010

J4 ›› 2013, Vol. 40 ›› Issue (2): 54-59+158.doi: 10.3969/j.issn.1001-2400.2013.02.010

一种业务关联的数据采集方法

盖炳帅^1,2;王劲林²;刘学²

(1. 中国科学院研究生院，北京 100049;
2. 中国科学院声学研究所国家网络新媒体工程技术研究中心，北京 100190)

收稿日期:2011-12-30 出版日期:2013-04-20 发布日期:2013-05-22
通讯作者: 盖炳帅
作者简介:盖炳帅(1984-)，男，中国科学院博士研究生，E-mail: gaibs@dsp.ac.cn．
基金资助:
国家高技术研究发展计划(863计划)资助项目(2011AA01A102);中国科学院战略性先导科技专项基金资助项目(XDA06010302);国家科技支撑计划资助项目(2011BAH08B02)

Service-related data sampling methodology

GAI Bingshuai^1,2;WANG Jinlin²;LIU Xue²

(1. Graduate Univ. of Chinese Academy of Sciences, Beijing 100049， China;
2. National Network New Media Engineering Research Center, Inst. of Acoustic, Chinese Academy of Sciences, Beijing 100190， China)

Received:2011-12-30 Online:2013-04-20 Published:2013-05-22
Contact: GAI Bingshuai

摘要/Abstract

摘要：

在分析当前主要数据采集方法的基础上，针对业务数据采集问题，提出了一种业务关联的数据采集方法．该方法将采集周期相同的业务所对应的采集数据项放置到一个数据项队列中，并采用启发式算法对数据项队列排序，然后顺序调度数据项队列中放置的各项，完成数据采集．仿真实验结果表明，该方法在业务分析允许的时间段内完成采集属于同一业务的数据项，同时将不同业务间共有的数据项的冗余采集率降低了60%～90％．

关键词: 网络管理, 数据采集, 数据项, 数据项队列, 排序

Abstract:

Based on the analysis of current popular data sampling methods, this paper proposes a service-related data sampling methodology in the field of service data sampling. Firstly, this method puts service data items with the same sampling cycle in a queue, and sorts the queue by a heuristic algorithm. Secondly, this method completes data sampling process for service data items in the queue in order. Simulation results show that this method can complete service data sampling in less than the period required by service analysis, and can reduce the redundant sampling rate by 60%～90％.

Key words: network management, data sampling, data item, data item queue, sorting

中图分类号:

TP393

盖炳帅;王劲林;刘学. 一种业务关联的数据采集方法[J]. J4, 2013, 40(2): 54-59+158.

GAI Bingshuai;WANG Jinlin;LIU Xue. Service-related data sampling methodology[J]. J4, 2013, 40(2): 54-59+158.

参考文献

［1］ ISO/IEC. 7498-4-1989 Open Systems Interconnection-Basic Reference Model-Management framework［S］. Geneva: ISO/IEEE, 1989.
［2］ Bai Jia, Eyisi E P, Xue Yuan, et al. Distributed Sampling Rate Adaptation for Networked Control Systems［C］//The First International Workshop on Cyber-Physical Networking Systems. Shanghai: ACM/IEEE, 2011: 768-773.
［3］ Alippi C, Anastasi G, Francesco M D, et al. An Adaptive Sampling Algorithm for Effective Energy Management in Wireless Sensor Networks With Energy-Hungry Sensors［J］. IEEE Trans on Instrumentation and Measurement, 2010, 59(2): 335-344.
［4］ Liu Chong, Wu Kui, Pei Jian. An Energy-Efficient Data Collection Framework for Wireless Sensor Networks by Exploiting Spatiotemporal Correlation［J］. IEEE Trans on Parallel and Distributed Systems, 2007, 18(7): 1010-1023.
［5］ Szalapski T, Madria S. Energy Efficient, Adaptive and Collaborative Data Sampling in Wireless Sensor Networks［C］//The 12th IEEE International Conference on Mobile Data Management. Lule: IEEE, 2011: 38-39.
［6］ Lassoued I, Barakat C. A Multi-task Adaptive Monitoring System Combining Different Sampling Primitives［C］//Proc of the 23rd International Teletraffic Congress. San Francisco: ACM, 2011: 79-86.
［7］ Halepovic E, Williamson C, Ghaderi M. DYNABYTE: a Dynamic Sampling Algorithm for Redundant Content Detection［C］//Proc of 20th International Conference on Computer Communications and Networks (ICCCN). Maui: IEEE, 2011: 1-8.
［8］ Aggarwal B, Akella A, Anand A, et al. EndRE: An End-System Redundancy Elimination Service for Enterprises［C］//Proc of the 7th USENIX Conference on Networked Systems Design and Implementation. Berkeley: ACM, 2010: 419-432.
［9］ Choi B Y, Park J, Zhang Zhili. Adaptive Random Sampling for Traffic Load Measurement［C］//IEEE International Conference on Communications. Anchorage: IEEE, 2003: 1552-1556.
［10］ Choi B Y, Park J, Zhang Zhili. Adaptive Packet Sampling for Accurate and Scalable Flow Measurement［C］//Global Telecommunications Conference. Dallas: IEEE, 2004: 1448-1452.
［11］ Durai B, Gonsalves T A, Sivalingam K M. Adaptive Push Based Data Collection Method for Online Performance Monitoring［C］//National Conference on Communications. Bangalore: the Indian Institute of Science, 2011: 1-5.
［12］ Ji Zhihui, Kuang Zhenguo, Ni Hong. A Novel Two-Dimension Adaptive Data Collection Method for Network Management［C］//International Conference on Communications and Mobile Computing. Kunming: IEEE, 2009: 237-241.
［13］ Gu Jun, Huang Xiaofei. Efficient Local Search With Search Space Smoothing: A Case Study of the Traveling Salesman Problem (TSP)［J］. IEEE Trans on Systems, Man, and Cybernetics, 1994, 24(5): 728-735.
［14］张军英, 敖磊, 贾江涛, 等. 求解TSP问题的改进蚁群算法［J］. 西安电子科技大学学报, 2005, 32(5): 681-685.
Zhang Junying, Ao Lei, Jia Jiangtao, et al. An Improvement of the Ant Colony Algorithm for Solving Tsp Problems［J］. Journal of Xidian University, 2005, 32(5): 681-685.

[1]	谭雯,甘新标,白皓,肖调杰,陈旭光,雷书梦,刘杰. 面向超级计算机系统的大规模图遍历优化[J]. 西安电子科技大学学报, 2021, 48(6): 84-95.
[2]	杨五四,陈莉,王毅,张茂省. 一种适应度排序的高维多目标粒子群优化算法[J]. 西安电子科技大学学报, 2021, 48(3): 78-84.
[3]	刘明骞,孟燕,张卫东. 雷达辐射源信号识别的效能综合评估方法[J]. 西安电子科技大学学报, 2019, 46(6): 1-8.
[4]	田瑞;刘马良. JESD204B协议的高速串行转换器接口[J]. 西安电子科技大学学报, 2017, 44(4): 69-74.
[5]	刘娟妮;周诠. SMVQ索引分类编码无损信息隐藏算法[J]. 西安电子科技大学学报, 2016, 43(5): 128-132+146.
[6]	代才;王宇平;何晓光. 高维多目标问题的排序新方法[J]. J4, 2014, 41(6): 89-94.
[7]	盖炳帅王劲林刘学. 一种业务关联的数据采集方法[J]. J4, 2013, 40(2): 54-59.
[8]	肖嵩;刘婷;杜建超;卢冀. 分级视频码流的三维精细粒度排序方法[J]. J4, 2012, 39(2): 73-79.
[9]	郭明喜;贾冲;沈越泓;高媛媛. 一种高性能低复杂度的V-BLAST检测方案[J]. J4, 2010, 37(3): 570-575.
[10]	高山;毕笃彦. 交叉视觉皮质模型的格论分析[J]. J4, 2009, 36(6): 1120-1125.
[11]	韩卫占;张思东;孙玉. 通信网网络管理控制系统可靠性及其评价研究 [J]. J4, 2008, 35(1): 133-139.
[12]	祁昶;王高峰. 基于MOGA／SOS的互连缓冲及线型优化算法 [J]. J4, 2007, 34(4): 622-628.
[13]	盛敏;田野;李建东;江帆. 基于妥善排序的无线局域网功率控制策略 [J]. J4, 2007, 34(1): 1-5.
[14]	孙德华(1);史加荣(2);刘三阳(1). 一类不完全信息多属性决策的随机模拟方法[J]. J4, 2006, 33(2): 287-291.
[15]	周宏安(1;2);刘三阳(1). 区间数互补判断矩阵排序的一种新方法[J]. J4, 2006, 33(2): 292-294.

一种业务关联的数据采集方法

Service-related data sampling methodology

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 10