Soft-Sleep Network Resource Activation With Advance Rerouting
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Solution Overview
Problem
Existing networks face inefficiencies due to overprovisioning, leading to idle resources and increased energy consumption, while attempts to conserve energy can disrupt traffic and violate service level agreements (SLAs).
Innovation Solution
Implementing an AI-driven elastic network that dynamically adapts its architecture to reduce energy consumption by identifying actions that minimize resource usage without causing performance degradation, using a digital twin to simulate and optimize network configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network resources are overprovisioned to meet increasing user demands and service level agreements, then network reliability is improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamic resource provisioning where network resources are adjusted in real-time based on actual traffic conditions. The system transitions from static overprovisioning to dynamic scaling, activating or deactivating resources as needed to match demand, thereby maintaining reliability while reducing energy consumption during low-utilization periods
Solution Approach 2:
The system changes operational parameters of network resources based on traffic conditions. By monitoring utilization metrics and adjusting resource states (active/standby/deactivated), the system optimizes the balance between reliability and energy consumption, switching parameters dynamically rather than maintaining fixed overprovisioned states
2Use of energy by moving object
If network resources are deactivated to conserve energy, then energy consumption is reduced, but network performance degrades
Solution Approach 1:
The system performs preliminary actions by predicting future traffic patterns and proactively adjusting resource states before demand changes occur. This allows smooth transitions without performance degradation, as resources are activated or deactivated in advance based on forecasts rather than reactive changes that cause disruptions
Solution Approach 2:
The system implements continuous feedback loops that monitor network performance metrics, traffic patterns, and resource utilization. This feedback enables the system to detect when deactivation would cause performance degradation and adjust accordingly, maintaining the balance between energy savings and service quality through closed-loop control
3Use of energy by moving object
If routing protocol convergence is triggered by shutting down network resources, then energy consumption is reduced, but traffic disruption occurs
Solution Approach 1:
The system performs preliminary routing adjustments and traffic redistribution before actually deactivating resources. By pre-computing alternative paths and gradually shifting traffic away from resources scheduled for deactivation, the system avoids sudden routing protocol convergence and associated traffic disruptions
Solution Approach 2:
The system implements periodic, gradual resource deactivation rather than sudden shutdowns. By deactivating resources in staged intervals and monitoring traffic flow continuously, the system minimizes disruption to productivity while achieving energy savings, avoiding the need for immediate routing protocol convergence
Data Source
AI summary
In one implementation, a device identifies an action to be performed in a computer network to reduce energy consumption by the computer network. The device determines whether performance of the action in the computer network will result in a performance degradation in the computer network. The device devises a rerouting strategy for the computer network, when performance of the action in the computer network will result in a performance degradation. The device implements the rerouting strategy in advance of performance of the action in the computer network, to mitigate against the performance degradation.