Publication | Closed Access
NetAgg
72
Citations
55
References
2014
Year
Unknown Venue
Cluster ComputingEngineeringData Centre ApplicationsMap-reduceDistributed Data AnalyticsData ScienceData-intensive PlatformPerformance BottleneckData IntegrationBatch ProcessingParallel ComputingData ManagementComputer ScienceData-intensive ComputingEdge ComputingCloud ComputingParallel ProgrammingMassive Data ProcessingBig Data
Data centre applications for batch processing (e.g. map/reduce frameworks) and online services (e.g. search engines) scale by distributing data and computation across many servers. They typically follow a partition/aggregation pattern: tasks are first partitioned across servers that process data locally, and then those partial results are aggregated. This data aggregation step, however, shifts the performance bottleneck to the network, which typically struggles to support many-to-few, high-bandwidth traffic between servers.
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