Green Elastic Network Auditing for Energy-Saving Upgrades
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Solution Overview
Problem
Existing networks face inefficiencies due to overprovisioning, leading to excessive energy consumption and resource wastage, while the ability to elastically scale to meet user demands is limited by infrastructure capabilities, with challenges in identifying which devices need upgrading.
Innovation Solution
An AI-driven elastic network system that forecasts usage and simulates equipment replacement to reduce energy consumption while maintaining performance, utilizing machine learning to optimize network configurations and dynamically adapt network architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network capacity is significantly expanded through overprovisioning to meet increasing user demands and service level agreements, then network performance and reliability are improved, but energy consumption increases excessively due to idle resources
Solution Approach 1:
The patent implements dynamic network provisioning that allows network resources to be flexibly adjusted based on actual demand. The system continuously monitors network usage patterns and dynamically provisions resources, enabling the network to scale up during peak periods and scale down during low-utilization periods, thus maintaining SLA compliance while reducing energy consumption from idle resources
Solution Approach 2:
The system changes network operational parameters by transitioning from static overprovisioning to dynamic provisioning levels. It adjusts network resource allocation parameters based on monitored usage data, allowing the network to operate at optimal provisioning levels that match actual demand rather than maintaining fixed high-capacity configurations
2Use of energy by moving object
If the network elastically scales up or down to conserve energy, then energy efficiency is improved, but the ability to do so is limited by existing infrastructure capabilities
Solution Approach 1:
The system performs preliminary analysis by monitoring and analyzing network usage patterns over time to identify devices that would benefit from upgrades. This proactive approach allows the system to plan and execute infrastructure improvements before they are critically needed, enabling smoother transitions to more capable equipment that supports better elastic scaling
Solution Approach 2:
The patent implements a feedback mechanism that continuously monitors network performance, usage patterns, and energy consumption. This feedback loop identifies when existing infrastructure limits elastic scaling and triggers recommendations for targeted device upgrades, enabling the network to progressively improve its scaling capabilities based on actual operational needs
3Use of energy by moving object
If networking equipment is replaced with new equipment to reduce energy consumption, then energy efficiency is improved, but it is challenging to identify which specific devices should be upgraded
Solution Approach 1:
The system employs feedback through continuous monitoring of network device performance metrics, energy consumption data, and usage patterns. This feedback mechanism automatically identifies which specific devices are underperforming or consuming excessive energy relative to their utilization, providing data-driven recommendations for targeted upgrades rather than blanket replacements
Solution Approach 2:
The system performs self-service by automatically analyzing network data to identify upgrade candidates without requiring manual assessment. It autonomously monitors device metrics, compares performance against benchmarks, and generates prioritized recommendations for device upgrades, eliminating the need for manual device evaluation
Data Source
AI summary
In one implementation, a device determines a target level of performance required by a computer network. The device forecasts usage of the computer network. The device runs simulation to determine whether replacing networking equipment in the computer network with new equipment will reduce energy consumption by the computer network and still satisfy the target level of performance, based on the usage of the computer network that was forecast by the device. The device sends, to a user interface, a recommendation to replace the networking equipment with the new equipment based on a result of the simulation.