Latency-Aware Placement of Data Stream Analytics on Edge Computing
Latency-Aware Placement of Data Stream Analytics on Edge Computing
复制标题
边缘计算上数据流分析的延迟感知放置
DOI：
10.1007/978-3-030-03596-9_14
复制
发表时间：
2018
期刊：
影响因子：
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通讯作者：
L. Lefèvre
中科院分区：
文献类型：
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作者：
A. Veith;M. Assunção;L. Lefèvre
在严格的时间约束下处理到达的数据事件的兴趣导致了用于数据流处理的体系结构和引擎的出现。边缘计算最初旨在最大限度地减少向移动设备传递内容的延迟，现在可以用于执行某些流处理操作。然而，将运营商从云迁移到边缘是具有挑战性的，因为运营商的安置决策必须考虑应用要求和网络能力。在这项工作中，我们介绍了为其算子拓扑遵循串联平行图的数据流处理应用程序创建布局配置的策略。我们考虑了运营商的特点和要求，以改善此类应用的响应时间。结果表明，对于包含多个分叉和连接的应用图，我们的策略可以在传输更少数据和更好地利用资源的同时，将响应时间提高高达50%。
The interest in processing data events under stringent time constraints as they arrive has led to the emergence of architecture and engines for data stream processing. Edge computing, initially designed to minimize the latency of content delivered to mobile devices, can be used for executing certain stream processing operations. Moving operators from cloud to edge, however, is challenging as operator-placement decisions must consider the application requirements and the network capabilities. In this work, we introduce strategies to create placement configurations for data stream processing applications whose operator topologies follow series parallel graphs. We consider the operator characteristics and requirements to improve the response time of such applications. Results show that our strategies can improve the response time in up to 50% for application graphs comprising multiple forks and joins while transferring less data and better using the resources.