Network Plane De-Energization for Traffic-Aware Power Saving

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Solution Overview

Problem

Existing networking structures with pods and planes often maintain all components in a fully powered state, leading to inefficiencies in energy conservation and sustainability.

Innovation Solution

Implement a system with a plane monitoring logic that selectively de-energizes and re-energizes networking planes based on network traffic levels and thresholds, using load balancing protocols and machine learning to manage traffic migration.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If all networking planes are kept in a fully powered state, then network reliability and performance are maintained, but energy consumption increases

Engineering Contradiction:
Improvenetwork reliabilityVSAvoidenergy consumption
Core Design Contradiction:
ReliabilityVSUse of energy by stationary object

Solution Approach 1:

The patent applies dynamics by transitioning networking planes between active and de-energized states based on real-time network conditions. The control plane dynamically adjusts the power state of data planes and management planes, enabling the system to adapt its energy consumption to actual traffic demands while maintaining reliability when needed.

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

The patent changes the operational parameter of networking planes from a static fully-powered state to a dynamic state that can be de-energized or re-energized. By monitoring network traffic thresholds and control plane instructions, the system adjusts the power state parameter of individual planes, achieving energy savings without permanently compromising network functionality.

Inventive Principle:
Principle #35Parameter changes

2Use of energy by stationary object

If networking planes are de-energized to save energy, then energy consumption decreases, but network performance and reliability may be affected

Engineering Contradiction:
Improveenergy consumptionVSAvoidnetwork performance
Core Design Contradiction:
Use of energy by stationary objectVSProductivity

Solution Approach 1:

The patent implements feedback mechanisms where the control plane monitors network traffic conditions and receives status information from de-energized planes. When traffic thresholds are exceeded or network performance requirements change, the control plane receives feedback and re-energizes the appropriate planes, ensuring performance is maintained when needed.

Inventive Principle:
Principle #23Feedback

Solution Approach 2:

The patent applies preliminary action by having the control plane proactively manage the energy state of networking planes before performance issues arise. The control plane monitors traffic patterns and de-energizes planes during low-utilization periods, preventing energy waste while ensuring planes are re-energized in advance of potential performance requirements.

Inventive Principle:
Principle #10Preliminary action

Data Source

PatentUS12578776B2Sustainable networking plane de-energization
Publication Date: 2026.03.17 CISCO TECHNOLOGY INC
  • US12578776B2 patent drawing
  • US12578776B2 patent drawing
  • US12578776B2 patent drawing

AI summary

Devices, systems, methods, and processes for sustainably operating a plurality of network planes via de-energization and re-energization is described herein. In many network configurations, a plurality of planes exist that allow for more modular connections in a network fabric. Often, these planes are configured such that each plane is not directly connected to another plane. Because of this, various embodiments described herein can evaluate network conditions and determine if there are conditions suitable to de-energize a plane by either directing the plane to enter a lower-power mode, by shutting off the plane, or disconnecting the available power. This de-energization period can be for a period of time or can occur until a triggering event is detected that indicates that the plane should be re-energized. These determinations can be done based on current traffic trends or historical conditions. They may also be heuristic-based or generated via one or more machine-learning processes.