A TTL-based Approach for Data Aggregation in Geo-distributed Streaming Analytics

Streaming analytics require real-time aggregation and processing of geographically distributed data streams continuously over time. The typical analytics infrastructure for processing such streams follow a hub-and-spoke model, comprising multiple edges connected to a center by a wide-area network (WAN). The aggregation of such streams often require that the results be available at the center within a certain acceptable delay bound. Further, the WAN bandwidth available between the edges and the center is often scarce or expensive, requiring that the traffic between the edges and the center be minimized. We propose a novel Time-to-Live (TTL-)based mechanism for real-time aggregation that provably optimizes both delay and traffic, providing a theoretical basis for understanding the delay-traffic tradeoff that is fundamental to streaming analytics. Our TTL-based optimization model provides analytical answers to how much aggregation should be performed at the edge versus the center, how much delay can be incurred at the edges, and how the edge-to-center bandwidth must be apportioned across applications with different delay requirements. To evaluate our approach, we implement our TTL-based aggregation mechanism in Apache Flink, a popular stream analytics framework. We deploy our Flink implementation in a hub-and-spoke architecture on geo-distributed Amazon EC2 data centers and a WAN-emulated local testbed, and run aggregation tasks for realistic workloads derived from extensive Akamai and Twitter traces. The delay-traffic tradeoff achieved by our Flink implementation agrees closely with theoretical predictions of our model. We show that by deriving the optimal TTLs using our model, our system can achieve a "sweet spot" where both delay and traffic are minimized, in comparison to traditional aggregation schemes such as batching and streaming.