Trading Timeliness and Accuracy in Geo-Distributed Streaming Analytics
Benjamin Heintz, Abhishek Chandra, Ramesh K. Sitaraman · 2016
Many applications must ingest rapid data streams and produce analytics results in near-real-time. It is increasingly common for inputs to such applications to originate from geographically distributed sources. The typical infrastructure for processing such geo-distributed streams follows a hub-and-spoke model, where several edge servers perform partial computation before forwarding results over a wide-area network (WAN) to a central location for final processing. Due to limited WAN bandwidth, it is not always possible to produce exact results. In such cases, applications must either sacrifice timeliness by allowing delayed---i.e., stale---results, or sacrifice accuracy by allowing some error in final results.