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Network Map Reduce

Networking data analytics is increasingly used for enhanced network visibility and controllability. We draw the similarities between the Software Defined Networking (SDN) architecture and the MapReduce programming model. Inspired by the similarity, we suggest the necessary data plane innovations to make network data plane devices function as distributed mappers and optionally, reducers. A streaming network data MapReduce architecture can therefore conveniently solve a series of network monitoring and management problems. Unlike the traditional networking data analytical system, our proposed system embeds the data analytics engine directly in the network infrastructure. The affinity leads to a concise system architecture and better cost performance ratio. On top of this architecture, we propose a general MapReduce-like programming model for real-time and one-pass networking data analytics, which involves joint in-network and out-of-network computing. We show this model can address a wide range of interactive queries from various network applications. This position paper strives to make a point that the white-box trend does not necessarily lead to simple and dumb networking devices.

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Related contextCo-authorshipCo-authorshipCo-authorshipAuthorshipAuthorshipAuthorshipTopic signalTopic signalWNetwork Map Reducepreprint / 2016AHaoyu SongResearcherAJun GongResearcherAHongfei ChenResearcherTDistributed, Parallel, ...4102 worksTNetworking and Internet...3614 works
PaperSignal 105 links

Network Map Reduce

preprint / 2016

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