Sustainability Logic for Energy-Efficient Network Traffic Distribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current network traffic management strategies, such as ECMP and UCMP, do not consider energy efficiency or sustainability, leading to challenges in optimizing network traffic distribution across multiple paths while minimizing energy expenditure.
Innovation Solution
The implementation of a network node with a sustainability logic that predicts traffic volume, identifies network topology, and determines which links to reserve based on capacity and energy costs, aiming to reduce the number of active links and minimize energy consumption while maintaining network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If links are selectively depowered to minimize energy expenditure, then energy efficiency is improved, but network reliability deteriorates due to packet drops on cycled paths
Solution Approach 1:
The patent applies preliminary action by predicting future traffic volume before making power management decisions. The sustainability logic component forecasts traffic demands and proactively determines which links should be powered or depowered in advance, preventing packet drops by ensuring capacity is available before traffic arrives. This resolves the contradiction by maintaining reliability through advance planning while achieving energy efficiency through selective depowering of unneeded links.
Solution Approach 2:
The patent implements feedback mechanisms where the sustainability logic continuously monitors actual traffic volume and compares it against predicted values. Based on this feedback, the system dynamically adjusts link power states and routing decisions. This closed-loop control ensures that energy efficiency is optimized while reliability is maintained through real-time adaptation to actual network conditions.
2Reliability
If multiple paths are maintained for redundancy and load balancing, then network reliability is improved, but energy consumption increases
Solution Approach 1:
The patent applies partial action by maintaining only the necessary number of active paths rather than all available paths. The sustainability logic determines the optimal subset of links to keep powered based on predicted traffic volume and capacity requirements. This resolves the contradiction by providing sufficient redundancy for reliability while minimizing energy consumption by depowering excess paths that are not currently needed.
Solution Approach 2:
The patent implements dynamic path selection where the set of active links changes over time based on traffic conditions. Rather than statically maintaining all paths for redundancy, the system dynamically powers links on and off according to real-time and predicted traffic demands. This dynamic approach maintains reliability when needed while reducing energy consumption during low-traffic periods.
3Use of energy by moving object
If links are cycled on/off to reduce energy expenditure, then energy efficiency is improved, but traffic forwarding reliability deteriorates
Solution Approach 1:
The patent resolves this contradiction by performing preliminary traffic volume prediction before cycling links. The sustainability logic forecasts whether traffic will be present on a given path and only depowers links when prediction indicates no traffic. This preliminary action ensures that links are not cycled during actual traffic flow, maintaining packet forwarding reliability while achieving energy efficiency through selective depowering of truly idle paths.
Data Source
AI summary
Described herein are devices, systems, methods, and processes for optimizing network traffic distribution across multiple paths in a manner that is energy-efficient and environmental sustainability-aware. This may be achieved by leveraging time-series analytics and capacity planning based on seasonalities. Data associated with the Layer 3 topology of the network can be collected. Bandwidth can be pre-reserved on an energy-aware traffic engineering tunnel. The time-series data can be used to build a capacity plan based on the seasonalities. Nodes may be clustered based on usage patterns and network utilization seasonality. The data can be used to make decisions about when and where to combine or shut down paths for energy efficiency, while maintaining optimal network performance. A hysteresis mechanism may be incorporated to avoid oscillation when changing active links. Power savings can be achieved by fully turning off or depowering certain network components when they are not needed.