Green Elastic Network What-If Evaluation for SLA Reliability
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Solution Overview
Problem
Traditional network designs face challenges in efficiently managing energy consumption while maintaining service level agreements (SLAs), as overprovisioning leads to inefficiencies and cascading effects from configuration changes without adequate what-if scenario evaluation.
Innovation Solution
The implementation of an AI-driven elastic network that utilizes a digital twin of the network to simulate and optimize energy consumption by dynamically adapting network architectures and configurations based on traffic demand and SLA requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If overprovisioning is used to meet user demands and SLAs, then service reliability is improved, but energy consumption increases
Solution Approach 1:
The patent implements dynamic network provisioning that adjusts network capacity and resource allocation in real-time based on actual traffic demand and SLA requirements. The system continuously monitors network conditions and automatically scales resources up or down, replacing static overprovisioning with adaptive dynamic provisioning that maintains reliability while reducing energy waste during low-demand periods.
Solution Approach 2:
The system changes key operational parameters such as network capacity allocation, resource provisioning levels, and configuration settings based on real-time conditions. By dynamically adjusting these parameters rather than maintaining fixed overprovisioned states, the system optimizes the balance between service reliability and energy consumption across different operational scenarios.
2Use of energy by moving object
If configuration changes are made to conserve energy, then energy consumption is reduced, but network performance may be degraded
Solution Approach 1:
The system performs preliminary what-if scenario evaluations and simulations before implementing configuration changes. By pre-assessing the potential impact of energy-saving modifications on network performance, the system identifies safe changes that conserve energy without degrading service quality, thus avoiding harmful unintended consequences.
Solution Approach 2:
The system implements continuous monitoring and feedback mechanisms that track network performance metrics after configuration changes. This feedback loop allows the system to detect performance degradation early and automatically revert or adjust changes, ensuring that energy conservation measures do not compromise network reliability or service quality.
3Reliability
If what-if scenario evaluation is performed before configuration changes, then network reliability is maintained, but system complexity increases
Solution Approach 1:
The system uses digital twins or virtual copies of the network to perform what-if scenario evaluations. By simulating configuration changes in a virtual replica rather than the live network, the system can assess potential impacts on reliability without adding complex evaluation infrastructure to the actual network, thus maintaining reliability while managing complexity.
Data Source
AI summary
In one implementation, a device queries an ontology that represents entities in a computer network and their relationships for a particular topology in the computer network. The device computes a mathematical system that represents traffic in the particular topology, based on traffic for only a portion of the particular topology. The device uses the mathematical system to compute traffic in the particular topology for a potential change to the particular topology expected to reduce energy consumption by the computer network. The device causes, based in part on the traffic computed using the mathematical system, the potential change to be made to the computer network.