Towards Optimizing Wide-Area Streaming Analytics

Modern analytics services require the analysis of large quantities of data derived from disparate geo-distributed sources. Further, the analytics requirements can be complex, with many applications requiring a combination of both real-time and historical analysis, resulting in complex tradeoffs between cost, performance, and information quality. While the traditional approach to analytics processing is to send all the data to a dedicated centralized location, an alternative approach would be to push all computing to the edge for in-situ processing. We argue that neither approach is optimal for modern analytics requirements. Instead, we examine complex tradeoffs driven by a large number of factors such as application, data, and resource characteristics. We present an empirical study using Planet Lab experiments with beacon data from Akamai´s download analytics service. We explore key tradeoffs and their implications for the design of next-generation scalable wide-area analytics.