WANalytics: Analytics for a Geo-Distributed Data-Intensive World.

Large organizations today operate data centers around the globe where massive amounts of data are produced and consumed by local users. Despite their geographically diverse origin, such data must be analyzed/mined as a whole. We call the problem of supporting rich DAGs of computation across geographically distributed data Wide-Area Big-Data (WABD). To the best of our knowledge, WABD is not supported by currently deployed systems nor suciently studied in literature; it is addressed today by continuously copying raw data to a central location for analysis. We observe from production workloads that WABD is important for large organizations, and that centralized solutions incur substantial cross-data center network costs. We argue that these trends will only worsen as the gap between data volumes and transoceanic bandwidth widens. Further, emerging concerns over data sovereignty and privacy may trigger government regulations that can threaten the very viability of centralized solutions. To address WABD we propose WANalytics, a system that pushes computation to edge data centers, automatically optimizing workow execution plans and replicating data when needed. Our Hadoop-based prototype delivers 257 reduction in WAN bandwidth on a production workload from Microsoft. We round out our evaluation by also demonstrating substantial gains for three standard benchmarks: TPC-CH, Berkeley Big Data, and BigBench.