Centralized Network Overcapacity Prediction and Resource Reallocation
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Solution Overview
Problem
Conventional methods for predicting and managing overcapacity events in network systems are system-specific and isolated, failing to consider the impact on other systems in the network, leading to potential cascading overcapacity events.
Innovation Solution
A centralized system that performs holistic network optimization, using machine-learning models for system-specific overcapacity prediction, simulation techniques for cascading overcapacity prediction, and user-related data to generate optimal resource reallocation recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional system-specific approaches are used to predict and mitigate overcapacity events, then the prediction and mitigation for a particular system can be performed, but the impact on other systems in the network is not considered, leading to potential cascading overcapacity events
Solution Approach 1:
The system segments the network into individual system units while maintaining a hierarchical structure. Each system can be independently analyzed for overcapacity conditions using system-specific models, while the hierarchical framework enables aggregation of these individual assessments to evaluate network-wide impacts and propagate mitigation strategies across multiple levels of the network hierarchy.
Solution Approach 2:
The system merges system-specific overcapacity prediction capabilities with network-wide simulation and optimization functions. By integrating individual system models into a unified network-level framework, the system simultaneously maintains detailed system-level accuracy while achieving comprehensive network-level adaptability and coordination for resource allocation decisions.
2Productivity
If resources are reallocated from a system experiencing overcapacity to other systems without considering network-wide impact, then the immediate overcapacity issue can be addressed, but cascading overcapacity events may occur at other systems
Solution Approach 1:
The system performs preliminary simulation of resource reallocation scenarios before actual implementation. By using simulation models to predict the outcomes of proposed resource transfers and identifying potential cascading effects in advance, the system can adjust reallocation strategies to prevent downstream overcapacity events while maintaining efficient resource utilization.
Solution Approach 2:
The system implements feedback mechanisms where simulation results and actual network performance data continuously inform resource reallocation decisions. The simulation component provides feedback on potential cascading effects, and this information feeds back into the optimization engine to adjust reallocation strategies, creating a closed-loop system that maintains network stability while improving productivity.
3Device complexity
If isolated system-specific approaches are used for resource reallocation, then the reallocation process can be simplified, but the overall network optimization is compromised
Solution Approach 1:
The system divides the complex network optimization problem into manageable segments by implementing a hierarchical structure. Individual system-level models handle local overcapacity detection and prediction, while higher-level simulation and optimization components coordinate resource allocation across the network. This segmentation reduces the computational complexity of the overall system while maintaining network-wide optimization capabilities.
Solution Approach 2:
The system employs dynamic resource allocation where the level of complexity and coordination applied to each reallocation decision adapts based on network conditions. For isolated, minor overcapacity events, simpler system-specific responses suffice, while for larger or interconnected issues, the system dynamically engages more comprehensive simulation and optimization processes, optimizing the balance between complexity and optimization effectiveness.
Data Source
AI summary
Systems and methods are disclosed for determining optimal resource allocation options for network systems. The method includes receiving real-time data associated with a plurality of systems; generating one or more features associated with a first system of the plurality of systems based on at least a portion of the real-time data; generating, via input of one or more features into a machine learning model, a prediction that the first system is approaching a capacity threshold; determining one or more probabilities associated with respective one or more second systems of the plurality of systems, each of the one or more probabilities indicating a preferability of the respective second system for reallocating one or more resources from the first system; and simulating the reallocation across the plurality of systems based on the one or more probabilities associated with the respective one or more second systems.