EDGEWISE: A Better Stream Processing Engine for the Edge

被引:0
|
作者
Fu, Xinwei [1 ]
Ghaffar, Talha [1 ]
Davis, James C. [1 ]
Lee, Dongyoon [1 ]
机构
[1] Virginia Tech, Blacksburg, VA 24061 USA
来源
PROCEEDINGS OF THE 2019 USENIX ANNUAL TECHNICAL CONFERENCE | 2019年
基金
美国国家科学基金会;
关键词
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Many Internet of Things (IoT) applications would benefit if streams of data could be analyzed rapidly at the Edge, near the data source. However, existing Stream Processing Engines (SPEs) are unsuited for the Edge because their designs assume Cloud-class resources and relatively generous throughput and latency constraints. This paper presents EDGEWISE, a new Edge-friendly SPE, and shows analytically and empirically that EDGEWISE improves both throughput and latency. The key idea of EDGEWISE is to incorporate a congestion-aware scheduler and a fixed-size worker pool into an SPE. Though this idea has been explored in the past, we are the first to apply it to modern SPEs and we provide a new queue-theoretic analysis to support it. In our single-node and distributed experiments we compare EDGEWISE to the state-of-the-art Storm system. We report up to a 3x improvement in throughput while keeping latency low.
引用
收藏
页码:929 / 945
页数:17
相关论文
empty
未找到相关数据