Network Map Reduce
Haoyu Song, Jun Gong, Hongfei Chen

TL;DR
This paper proposes a network data analytics architecture inspired by MapReduce, embedding data processing directly into network devices for real-time, efficient, and versatile network monitoring and management.
Contribution
It introduces a novel in-network MapReduce-like architecture and programming model, enabling real-time analytics directly within network infrastructure.
Findings
Enables real-time network analytics with in-network processing.
Improves cost performance by embedding analytics in network devices.
Supports a wide range of interactive network queries.
Abstract
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…
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Taxonomy
TopicsSoftware-Defined Networks and 5G · Advanced Computing and Algorithms · IoT and Edge/Fog Computing
