As real-time and immediate feedback becomes increasingly important in tasks related to mobile information, big data stream processing systems are increasingly applied to process massive amounts of mobile data. However, when processing a drastically fluctuating mobile data stream, the lack of an elastic resource-scheduling strategy limits the elasticity and scalability of data stream processing systems. To address this problem, this paper builds a flow-network model, a resource allocation model, and a data redistribution model as the foundation for proposing Flink with an elastic resource-scheduling strategy (Flink-ER), which consists of a capacity detection algorithm, an elastic resource reallocation algorithm, and a data redistribution algorithm. The strategy improves the performance of the platform by dynamically rescaling the cluster and increasing the parallelism of operators based on the processing load. The experimental results show that the throughput of a cluster was promoted under the premise of meeting latency constraints, which verifies the efficiency of the strategy.

中文翻译：

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Flink-ER：一种用于处理Flink上波动的移动流数据的弹性资源调度策略

随着实时和即时反馈在与移动信息相关的任务中变得越来越重要，大数据流处理系统越来越多地用于处理大量的移动数据。但是，当处理波动剧烈的移动数据流时，缺乏弹性的资源调度策略限制了数据流处理系统的弹性和可伸缩性。为了解决这个问题，本文建立了一个流网络模型，一个资源分配模型和一个数据重新分配模型，以此作为提出具有弹性资源调度策略（Flink-ER）的Flink的基础，该策略由容量检测算法组成，弹性资源重新分配算法和数据重新分配算法。该策略通过动态调整集群规模并根据处理负载提高操作员的并行性来提高平台性能。实验结果表明，在满足等待时间约束的前提下，可以提高集群的吞吐量，从而验证了该策略的有效性。