Critical-Flow QoS Activation for Downscaled Network Topologies
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network technologies face inefficiencies due to overprovisioning, leading to excessive energy consumption and inability to adapt to dynamic network topologies, with static QoS mechanisms failing to manage congestion effectively.
Innovation Solution
Implementing AI-driven elastic networks that dynamically adjust network architectures and configurations to reduce energy consumption while maintaining service level agreements (SLAs) through mechanisms like digital twins, energy forecasting, and adaptive path management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If overprovisioning is used to meet increasing user demands, then network capacity is expanded and service level agreements are met, but energy consumption increases and resource efficiency decreases
Solution Approach 1:
The patent implements dynamic QoS configuration that adapts to real-time network conditions and traffic patterns. The system dynamically adjusts QoS parameters such as priority levels, bandwidth allocation, and packet handling based on current network state, replacing static overprovisioning with flexible, demand-driven resource allocation that maintains SLA compliance while reducing energy consumption during low-demand periods
Solution Approach 2:
The system changes QoS parameters dynamically based on traffic conditions and network state. By adjusting parameters like priority queues, shaping rates, and policing thresholds in response to real-time measurements, the network optimizes resource usage without requiring permanent overprovisioning, thus reducing energy consumption while maintaining service quality
2Reliability
If static QoS mechanisms are used to manage network congestion, then traffic classification and prioritization are implemented, but adaptability to dynamic network topologies is lost
Solution Approach 1:
The patent transforms static QoS mechanisms into dynamic systems that automatically adapt to changing network topologies and traffic patterns. The system continuously monitors network conditions and adjusts QoS configurations in real-time, enabling effective congestion management regardless of topology changes while maintaining the ability to prioritize critical traffic flows
Solution Approach 2:
The system implements feedback mechanisms that monitor network performance metrics and use this information to automatically adjust QoS parameters. This closed-loop control enables the network to adapt to dynamic conditions and topology changes while maintaining effective congestion management and SLA compliance without manual reconfiguration
Data Source
AI summary
In one implementation, a device obtains an indication of an energy-saving action to be performed by an entity in a computer network to reduce energy consumption by the computer network. The device identifies one or more peer entities in the computer network that would be affected by the entity performing the energy-saving action. The device determines a quality of service configuration for traffic associated with the entity and the one or more peer entities. The device implements the quality of service configuration in the computer network, in advance of the entity performing the energy-saving action.