Flink环境下基于负载预测的弹性资源调度策略

李梓杨; 于炯; 王跃飞; 卞琛; 蒲勇霖; 张译天; 刘宇

doi:10.11959/j.issn.1000-436x.2020195

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Flink环境下基于负载预测的弹性资源调度策略

学术论文 | 更新时间：2024-06-05

- Flink环境下基于负载预测的弹性资源调度策略
- Load prediction based elastic resource scheduling strategy in Flink
- 通信学报 2020年41卷第10期页码：92-108
- 作者机构：
  
  1. 新疆大学软件学院，新疆乌鲁木齐 830008
  2. 新疆大学信息科学与工程学院，新疆乌鲁木齐 830046
  3. 成都大学计算机学院，四川成都 610106
  4. 广东金融学院互联网金融与信息工程学院，广东广州 510521
- 作者简介：
  
  [ "李梓杨（1993- ），男，新疆乌鲁木齐人，新疆大学博士生，主要研究方向为分布式系统、内存计算、流式计算" ]
  [ "于炯（1964- ），男，北京人，博士，新疆大学教授、博士生导师，主要研究方向为网格计算、并行计算、分布式系统" ]
  [ "王跃飞（1991- ），男，新疆乌鲁木齐人，博士，成都大学讲师，主要研究方向为数据挖掘、机器学习" ]
  [ "卞琛（1981- ），男，江苏南京人，博士，广东金融学院副教授，主要研究方向为分布式系统、内存计算、绿色计算" ]
  [ "蒲勇霖（1991- ），男，山东淄博人，新疆大学博士生，主要研究方向为内存计算、流式计算、绿色计算" ]
  [ "张译天（1995- ），男，河南商丘人，新疆大学硕士生，主要研究方向为云计算、实时计算、分布式计算" ]
  [ "刘宇（1996- ），男，新疆克拉玛依人，新疆大学硕士生，主要研究方向为云计算、分布式计算" ]
- 基金信息：
  
  国家自然科学基金资助项目(61862060);国家自然科学基金资助项目(61462079);国家自然科学基金资助项目(61562086);国家自然科学基金资助项目(61562078);新疆维吾尔自治区自然科学基金资助项目(2017D01A20);新疆大学博士生科技创新基金资助项目(XJUBSCX-201902)
- DOI：10.11959/j.issn.1000-436x.2020195
  中图分类号： TP311
- 网络出版日期：2020-10，
  
  纸质出版日期：2020-10-25
- 稿件说明：
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李梓杨, 于炯, 王跃飞, 等. Flink环境下基于负载预测的弹性资源调度策略[J]. 通信学报, 2020,41(10):92-108.

Ziyang LI, Jiong YU, Yuefei WANG, et al. Load prediction based elastic resource scheduling strategy in Flink[J]. Journal on communications, 2020, 41(10): 92-108.
李梓杨, 于炯, 王跃飞, 等. Flink环境下基于负载预测的弹性资源调度策略[J]. 通信学报, 2020,41(10):92-108. DOI： 10.11959/j.issn.1000-436x.2020195.

Ziyang LI, Jiong YU, Yuefei WANG, et al. Load prediction based elastic resource scheduling strategy in Flink[J]. Journal on communications, 2020, 41(10): 92-108. DOI： 10.11959/j.issn.1000-436x.2020195.

摘要

为了解决大数据流式计算平台中存在计算负载剧烈波动，但集群因资源不足而遇到性能瓶颈的问题，提出了Flink环境下基于负载预测的弹性资源调度（LPERS-Flink）策略。首先，建立负载预测模型并在此基础上提出负载预测算法，预测集群负载的变化趋势；其次，建立资源判定模型，以判定集群出现资源瓶颈与资源过剩的问题，由此提出弹性资源调度算法，制定弹性资源调度计划；最后，通过在线负载迁移算法执行调度计划，实现高效的节点间负载迁移。实验结果表明，该策略在负载剧烈波动的应用场景中有较好的优化效果，实现了集群规模和资源配置对负载变化的及时响应，降低了负载迁移的通信开销。

Abstract

In order to solve the problem that the load of big data stream computing platform fluctuates drastically while the cluster was suffering from the performance bottleneck due to the shortage of computing resources

the load prediction based elastic resource scheduling strategy in Flink (LPERS-Flink) was proposed.Firstly

the load prediction model was set up as the foundation to propose the load prediction algorithm and predict the variation tendency of the processing load.Secondly

the resource judgment model was set up to identify the performance bottleneck and resource redundancy of the cluster while the resource scheduling algorithm was proposed to draw up the resource rescheduling plan.Finally

the online load migration algorithm was proposed to execute the resource rescheduling plan and migrate processing load among nodes efficiently.The experimental results show that the strategy provides better performance promotion in the application with drastically fluctuating processing load.The scale and resource configuration of the cluster responded to the variation of processing load in time and the communication overhead of the load migration was reduced effectively.

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references

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