AWStream: adaptive wide-area streaming analytics
AWStream: adaptive wide-area streaming analytics
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DOI：
10.1145/3230543.3230554
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发表时间：
2018-08
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通讯作者：
Ben Zhang;Xin Jin;Sylvia Ratnasamy;J. Wawrzynek;Edward A. Lee
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作者：
Ben Zhang;Xin Jin;Sylvia Ratnasamy;J. Wawrzynek;Edward A. Lee
新兴的广域流分析类别面临着稀缺且可变的WAN带宽的挑战。使用TCP或UDP构建的非自适应应用程序会遭受延迟增加或准确性降低的问题。适应网络变化的最新方法需要开发人员编写次优的手动策略，或者仅限于特定于应用程序的优化。我们提出了AWStream，流处理系统，同时实现低延迟和高精度在广域网，需要最少的开发人员的努力。为了实现这一点，AWStream使用了三个想法：（i）它将应用程序自适应作为流处理模型中的第一级编程抽象;（ii）通过离线和在线分析的组合，它自动学习准确的配置文件，该配置文件对准确性和带宽权衡进行建模;以及（iii）在运行时，它仔细地调整应用数据速率以匹配可用带宽，同时最大化可实现的精度。我们评估AWStream与三个现实世界的应用程序：增强现实，行人检测和监控日志分析。我们的实验表明，AWStream实现了亚秒级的延迟，只有标称精度下降（2-6%）。
The emerging class of wide-area streaming analytics faces the challenge of scarce and variable WAN bandwidth. Non-adaptive applications built with TCP or UDP suffer from increased latency or degraded accuracy. State-of-the-art approaches that adapt to network changes require developer writing sub-optimal manual policies or are limited to application-specific optimizations. We present AWStream, a stream processing system that simultaneously achieves low latency and high accuracy in the wide area, requiring minimal developer efforts. To realize this, AWStream uses three ideas: (i) it integrates application adaptation as a first-class programming abstraction in the stream processing model; (ii) with a combination of offline and online profiling, it automatically learns an accurate profile that models accuracy and bandwidth trade-off; and (iii) at runtime, it carefully adjusts the application data rate to match the available bandwidth while maximizing the achievable accuracy. We evaluate AWStream with three real-world applications: augmented reality, pedestrian detection, and monitoring log analysis. Our experiments show that AWStream achieves sub-second latency with only nominal accuracy drop (2-6%).