Subscription-Based Control Plane for Traffic-Aware Power Management
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Solution Overview
Problem
Traditional computer networks consume excessive power due to inefficient power management, especially during low network traffic periods, leading to increased energy costs and carbon footprint without adapting to changing network conditions.
Innovation Solution
A centralized control plane dynamically manages power consumption across network paths by providing network usage data to networking devices, allowing them to make informed power-saving decisions based on real-time conditions using protocols like LISP, BGP-EVPN, and OMP, and adjusting power settings through a subscription-based energy efficiency service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network devices are designed for continuous operation and high availability, then network reliability is improved, but power consumption increases
Solution Approach 1:
The patent implements dynamic power management by enabling network devices to transition between active and low-power states based on real-time network conditions. The control plane monitors traffic patterns and dynamically adjusts device power states, allowing the system to maintain reliability when needed while reducing power consumption during low-traffic periods.
Solution Approach 2:
The system changes operational parameters of network devices based on network conditions. By monitoring metrics such as traffic volume, device utilization, and network demand, the control plane adjusts power consumption parameters dynamically, transitioning devices between different power states to optimize the balance between reliability and energy efficiency.
2Productivity
If network devices operate at full power continuously, then network performance is maintained, but energy costs increase
Solution Approach 1:
The patent implements a feedback mechanism where the control plane continuously monitors network performance metrics and device utilization levels. Based on this feedback, the system determines when network devices can safely transition to lower power states without degrading performance, and when they need to return to full power mode to maintain service quality.
Solution Approach 2:
The system performs preliminary assessments of network conditions before transitioning devices to power-saving modes. By predicting future network demand based on current patterns and thresholds, the control plane can proactively adjust power states to avoid both performance degradation and unnecessary energy consumption.
3Loss of energy
If individual device power management is implemented, then local energy efficiency is improved, but overall network optimization is limited
Solution Approach 1:
The patent creates a universal control plane that manages power optimization across the entire network infrastructure. This centralized controller coordinates power management decisions across multiple devices, ensuring that local optimizations at individual device levels are harmonized with overall network requirements, enabling both device-level efficiency and network-wide adaptability.
Data Source
AI summary
Described herein are systems and methods for optimizing energy efficiency in a network utilizing a control plane or other network administration device or software suite. The control plane continuously monitors end-to-end network paths and collects real-time data about network topology, traffic patterns, and connected devices. By analyzing the collected network data, the control plane identifies power needs for network nodes and generates energy saving recommendations or instructions tailored to each node's specific capabilities. Network nodes can subscribe to the energy efficiency service provided by the control plane, receive network usage data, and execute energy saving operations based on the recommendations. The control plane dynamically updates the energy saving recommendations in response to changes in network conditions, enabling network nodes to optimize their energy efficiency without compromising network performance and availability. These updates can be based on current network conditions but can be generated from historical data and/or machine learning processes.