Paper Info
Title
WANalytics: Geo-Distributed Analytics for a Data Intensive World
Abstract
Many large organizations collect massive volumes of data each day in a geographically distributed fashion, at data centers around the globe. Despite their geographically diverse origin the data must be processed and analyzed as a whole to extract insight. We call the problem of supporting large-scale geo-distributed analytics Wide-Area Big Data (WABD). To the best of our knowledge, WABD is currently addressed by copying all the data to a central data center where the analytics are run. This approach consumes expensive cross-data center bandwidth and is incompatible with data sovereignty restrictions that are starting to take shape. We instead propose WANalytics, a system that solves the WABD problem by orchestrating distributed query execution and adjusting data replication across data centers in order to minimize bandwidth usage, while respecting sovereignty requirements. WANalytics achieves an up to 360x reduction in data transfer cost when compared to the centralized approach on both real Microsoft production workloads and standard synthetic benchmarks, including TPC-CH and Berkeley Big-Data. In this demonstration, attendees will interact with a live geo-scale multi-data center deployment of WANalytics, allowing them to experience the data transfer reduction our system achieves, and to explore how it dynamically adapts execution strategy in response to changes in the workload and environment.
Year
DOI
Venue
2015
10.1145/2723372.2735365
ACM SIGMOD Conference
Field
DocType
Citations 
Data mining,Software deployment,Replication (computing),Data transmission,Computer science,Copying,Online analytical processing,Analytics,Big data,Data center,Database
Conference
22
PageRank 
References 
Authors
0.78
13
7
Name
Order
Citations
PageRank
Ashish Vulimiri11878.44
Carlo Curino2201290.35
P. Brighten Godfrey32519145.37
Thomas Jungblut4220.78
Konstantinos Karanasos519714.54
Jitendra Padhye66770514.84
George Varghese78149727.66