Network Device Data Reduction via Segmentation
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Solution Overview
Problem
In distributed computing environments, large datasets processed through networks cause bottlenecks due to high network traffic, leading to increased latency, congestion, and infrastructure costs, with existing solutions focusing on static optimization rather than dynamic adjustment of network settings.
Innovation Solution
Implementing network devices with processing resources and reduction modules that perform reduction functions on data as it transmits from mapper to reducer engines, utilizing programmable elements to actively reduce network traffic and leverage computing capabilities within switches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If large datasets are transmitted through the network in distributed computing environments, then data processing capability is improved, but network traffic increases causing bottlenecks, latency, and congestion
Solution Approach 1:
The network device divides incoming data into multiple segments and performs reduction operations on each segment independently before transmission. This segmentation allows the network device to process and reduce data in parallel, decreasing overall network traffic while maintaining data processing capability.
Solution Approach 2:
The network device acts as an intermediary between data sources and destinations, performing reduction functions on data as it passes through. This intermediary role allows the network device to actively reduce network traffic without requiring changes to end systems, effectively mediating the data flow to minimize bottlenecks.
2Quantity of substance
If network devices perform reduction functions on data, then network traffic is reduced, but device complexity increases
Solution Approach 1:
The network device is designed with multi-functionality, combining traditional network switching with embedded reduction capabilities. By integrating multiple functions into a single device, the patent avoids adding separate complex reduction systems, thereby reducing overall system complexity while maintaining traffic reduction benefits.
Solution Approach 2:
The network device performs reduction functions on data as it naturally passes through its forwarding path, without requiring separate dedicated processing stages. This self-service approach allows the device to reduce traffic using its existing infrastructure, minimizing the need for additional complex components.
3Ease of operation
If static optimization is used for network settings, then configuration simplicity is maintained, but adaptability to dynamic traffic patterns is reduced
Solution Approach 1:
The network device dynamically adjusts its reduction operations based on real-time traffic patterns and conditions. By continuously monitoring data flow characteristics and adapting reduction strategies accordingly, the device maintains optimal performance across varying workloads without requiring manual reconfiguration, thus preserving ease of operation while enhancing adaptability.
Data Source
AI summary
Examples of reducing data in a network are disclosed. In one example implementation according to aspects of the present disclosure, method may include receiving, by a network device, data from a mapper system. The method may then include performing, by the network device, a reduction function on the data received from the mapper system to reduce the data. The method may also include transmitting, by the network device, the reduced data to a reducer system.