Flow-network based auto rescale strategy for Flink

Ziyang LI; Jiong YU; Chen BIAN; Yitian ZHANG; Yonglin PU; Yuefei WANG; Liang LU

doi:10.11959/j.issn.1000-436x.2019173

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Flow-network based auto rescale strategy for Flink

Papers | 更新时间：2024-06-05

- Flow-network based auto rescale strategy for Flink
- Journal on Communications Vol. 40, Issue 8, Pages: 85-101(2019)
- 作者机构：
  
  1. 新疆大学信息科学与工程学院，新疆乌鲁木齐 830046
  2. 新疆大学软件学院，新疆乌鲁木齐 830008
  3. 广东金融学院互联网金融与信息工程学院，广东广州 510521
  4. 中国民航大学计算机科学与技术学院，天津 300300
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61862060);The National Natural Science Foundation of China(61462079);The National Natural Science Foundation of China(61562086);The National Natural Science Foundation of China(61562078);The Science and Technology Support Projects of Ministry of National Science and Technology(2015BAH02F01);The Natural Science Foundation of Xinjiang Uygur Autonomous Region(2017D01A20);Educational Research Program of Xinjiang Uygur Autonomous Region of China(XJEDU2016S106)
- DOI：10.11959/j.issn.1000-436x.2019173
  CLC： TP311
- Online First：2019-08，
  
  Published：25 August 2019
- 稿件说明：
移动端阅览
Ziyang LI, Jiong YU, Chen BIAN, et al. Flow-network based auto rescale strategy for Flink[J]. Journal on Communications, 2019, 40(8): 85-101.
DOI：

Ziyang LI, Jiong YU, Chen BIAN, et al. Flow-network based auto rescale strategy for Flink[J]. Journal on Communications, 2019, 40(8): 85-101. DOI： 10.11959/j.issn.1000-436x.2019173.

摘要

为了解决大数据流式计算平台中存在计算负载波动上升，但集群无法有效应对负载变化的问题，提出了基于流网络的 Flink 平台弹性资源调度策略（FAR-Flink）。该策略首先建立流网络模型并通过构建算法计算每条边的容量值，其次通过弹性资源调度算法确定集群性能瓶颈并制定动态资源调度计划，最后通过基于数据分簇和分桶管理的状态数据迁移算法，实施调度计划并完成节点间的高效数据迁移。实验结果表明，该策略在状态数据复杂的应用场景中有较好的优化效果，在满足计算时延约束的前提下提高了集群的吞吐量，缩短了状态数据迁移的时间。由此可见，FAR-Flink策略有效提升了集群对负载波动的响应能力。

Abstract

In order to solve the problem that the load of big data stream computing platform is increasing with fluctuation while the cluster was not able to rescale efficiently

the Flow-network based auto rescale strategy for Flink was proposed.Firstly

the flow-network model was set up and the capacity of each edge that was calculated by self-learning algorithm.Secondly

the bottleneck of the cluster was acquired by maximum-flow algorithm and the resource rescheduling plan was drawn up.Finally

the resource rescheduling plan was executed and the stateful data was migrated efficiently by the data migration algorithm based on the strategy of data partitioning by bulk and bucket.The experimental results show that the strategy can effectively provide performance promotion in the application with complex stateful data.It improved the throughput of the cluster and reduced the time overhead of the data migration on the premise of satisfying the latency constrain of the application

which means that the strategy promotes the scalability of the cluster efficiently.

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references

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