Predictive Network Resource Right-Sizing for Seasonal Capacity Spikes
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Solution Overview
Problem
Network devices often operate at maximum power capacity to accommodate peak demand, leading to significant resource wastage and environmental impact, despite not requiring this capacity at all times.
Innovation Solution
Implement an auto-energizing infrastructure that predicts capacity spikes using time series forecasting AI tools, providing a component resource manager with expected utilization and maximum potential delta, allowing it to self-tune resource capacity dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network devices operate at maximum power capacity to accommodate peak demands, then reliability of network service is improved, but energy consumption increases significantly
Solution Approach 1:
The patent applies dynamics by transitioning from static maximum capacity provisioning to dynamic capacity adjustment. The system continuously monitors network utilization metrics and automatically scales resource capacity up or down based on real-time demand conditions, allowing the network device to adapt its power consumption to actual workload requirements while maintaining service reliability.
Solution Approach 2:
The patent implements preliminary action through predictive analytics that analyze historical network utilization patterns to forecast future capacity requirements. By predicting demand spikes before they occur, the system can proactively allocate necessary resources in advance, ensuring reliability is maintained during peak periods without requiring continuous maximum capacity operation.
2Reliability
If sufficient power capacity is allocated for highest conceivable requirement, then network performance reliability is improved, but resource wastage increases
Solution Approach 1:
The patent applies self-service by enabling the network device to autonomously manage its own capacity allocation without external intervention. The embedded analytics system automatically monitors performance metrics, predicts future requirements, and adjusts resource provisioning dynamically, allowing the system to serve its own capacity management needs while eliminating waste from over-provisioning.
Solution Approach 2:
The patent implements feedback mechanisms that continuously collect network utilization data and feed it back to the capacity management system. This closed-loop feedback enables real-time adjustment of resource allocation based on actual demand, ensuring that capacity is optimized to match current conditions while maintaining the reliability needed for peak performance when required.
3Use of energy by moving object
If capacity right-sizing is implemented to optimize resource allocation, then energy efficiency is improved, but complexity of capacity management increases
Solution Approach 1:
The patent replaces manual capacity management processes with automated software-based analytics and control systems. By substituting human intervention and manual configuration with algorithm-driven predictive analytics and automated provisioning, the system achieves sophisticated capacity right-sizing optimization without requiring complex human management processes, thereby improving energy efficiency while managing complexity through automation.
Data Source
AI summary
Techniques for tuning resource capacity for a network device are described. A component resource manager receives an expected utilization of the resource during a utilization volatility period from an auto energizing infrastructure. The utilization volatility period is a period of time during which a chance of a utilization increase of the resource is within a predetermined threshold. The component resource manager receives a calculation of a maximum potential capacity delta for the expected utilization of the resource during the utilization volatility period from the auto energizing infrastructure. Based on the expected utilization and the maximum potential capacity delta, the component resource manager determines the right-size capacity of the resource for the utilization volatility period. The right-size capacity is at least an amount of the resource needed to ensure proper network functioning. The component resource manager provides the right-size capacity of the resource during the utilization volatility period.