The emerging class of wide-area streaming analytics faces the challenge of scarce and variable WAN bandwidth. Nonadaptive 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 applicationspecific 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 subsecond latency with only nominal accuracy drop (2-6%).
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