[1]王春凯,庄福振,史忠植.易变数据流的系统资源配置方法[J].智能系统学报,2019,14(06):1278-1285.[doi:10.11992/tis.201908011]
 WANG Chunkai,ZHUANG Fuzhen,SHI Zhongzhi.System resource allocation for variable data streams[J].CAAI Transactions on Intelligent Systems,2019,14(06):1278-1285.[doi:10.11992/tis.201908011]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年06期
页码:
1278-1285
栏目:
出版日期:
2019-11-05

文章信息/Info

Title:
System resource allocation for variable data streams
作者:
王春凯12 庄福振2 史忠植2
1. 中国再保险(集团)股份有限公司 博士后科研工作站, 北京 100033;
2. 中国科学院 计算技术研究所, 北京 100190
Author(s):
WANG Chunkai12 ZHUANG Fuzhen2 SHI Zhongzhi2
1. Post-doctoral Research Center, China Reinsurance (Group) Corporation, Beijing 100033, China;
2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
关键词:
大规模数据流管理系统易变数据流增量学习模型预测参数配置微批处理系统性能异常检测
Keywords:
large-scale data stream management systemvariable data streamincremental learningmodel predictionparameter configurationmini-batch processingsystem performanceoutlier detection
分类号:
TP311
DOI:
10.11992/tis.201908011
摘要:
大规模数据流管理系统往往由上层的关系查询系统和下层的流处理系统组成。当用户提交查询请求时,往往需要根据数据流的流速和分布情况动态配置系统参数。然而,由于数据流的易变性,频繁改变参数配置会降低系统性能。针对该问题,提出了OrientStream+框架。设定以用户自定义查询延迟阈值为间隔片段的微批量数据流传输机制;并利用多级别管道缓存,对相同配置的数据流进行批量处理;然后按照数据流的时间戳计算出精准查询结果;引入基于异常检测的增量学习模型,用于提高OrientStream+的预测精度。最后,在Storm上实现了该资源配置框架,并进行了大量的实验。实验结果表明,OrientStream+框架可进一步降低系统的处理延迟并提高系统的吞吐率。
Abstract:
A large-scale data stream management system (LSDSMS) usually contains a relational query system (RQS) and a stream processing system (SPS). When users submit queries to the RQS, it is often necessary to set system parameters according to the rate and distribution of the data streams. However, because of the variability of data streams, changing the resource allocation often reduces the performance of the LSDSMS. In view this problem, we propose a framework for automating the characterization deployment in the LSDSMS OrientStream+. First, based on a user-defined query latency threshold, we designed a data stream transmission mechanism for a mini-batch scheme. Then, we introduced a multi-level pipeline cache for processing batch data streams in the same configuration and obtained accurate query results using the timestamp of the data streams. We also propose an incremental leaning technique with outlier detection to improve the prediction accuracy of OrientStream+. Finally, we validated the proposed approach on the open-source SPS–Storm. Our experimental results show that OrientStream+ can reduce processing latency and improve the LSDSMS throughput.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-08-15。
基金项目:国家自然科学基金项目(U1836206,61773361);中国博士后科学基金项目(2019M650044)
作者简介:王春凯,男,1981年生,博士后,主要研究方向为数据流管理、知识融合。曾主持和参与中国博士后科学基金项目、国家重点研发计划项目、国家自然科学基金项目以及其他横向课题的研究。发表学术论文10余篇;庄福振,男,1983年生,副研究员。主要研究方向为迁移学习、数据挖掘、机器学习。曾主持和参与国家重点研发计划项目、国家"863 "计划项目、" 973"子课题、国家自然科学基金项目以及其他横向课题的研究。发表学术论文40余篇;史忠植,男,1941年生,研究员。主要研究方向为智能科学、人工智能、机器学习、知识工程等。1979年、1998年、2001年均获中国科学院科技进步二等奖,1994年获中国科学院科技进步特等奖,2002年获国家科技进步二等奖。发表学术论文400余篇,出版专著5部
通讯作者:王春凯.E-mail:chunkai_wang@163.com
更新日期/Last Update: 2019-12-25