Provisioning Engine Resource Allocation Using Traffic Forecasting
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Solution Overview
Problem
Wireless communication networks face challenges in efficiently allocating computing resources to network provisioning engines due to varying traffic patterns and volumes, leading to degraded user experiences when engines experience traffic upticks without sufficient resources.
Innovation Solution
Implementing a resource allocation system that hosts traffic forecasting, resource forecasting, and resource allocation machine learning models to predict future traffic and hardware requirements, enabling dynamic allocation of computing resources based on these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple provisioning engines are operated with different subscriber types and traffic patterns, then service diversity and network coverage are improved, but resource allocation complexity and difficulty in meeting computing needs increase
Solution Approach 1:
The system performs preliminary actions by collecting historical traffic data and training machine learning models in advance to predict future resource requirements. The traffic forecasting model and resource forecasting model are trained using historical data before actual resource allocation is needed, enabling the system to anticipate future needs and allocate resources proactively rather than reactively.
Solution Approach 2:
The system implements feedback mechanisms where actual traffic data from provisioning engines is continuously collected and fed back into the machine learning models. This feedback loop allows the models to refine their predictions based on actual performance, and the resource allocation system adjusts allocations based on predicted versus actual traffic patterns, creating a closed-loop optimization system.
2Adaptability or versatility
If dynamic traffic patterns are accommodated with multiple provisioning engines, then network coverage and service coverage are improved, but computing resource allocation accuracy deteriorates
Solution Approach 1:
The system performs preliminary forecasting by training machine learning models on historical traffic data before actual resource allocation is needed. The traffic forecasting model predicts future traffic patterns, and the resource forecasting model translates these predictions into hardware requirements in advance, enabling accurate resource allocation before traffic spikes occur.
Solution Approach 2:
The system replaces traditional mechanical resource allocation methods with machine learning-based predictive models. Instead of using simple threshold-based or static allocation rules, the system employs neural networks and machine learning algorithms that can process complex, dynamic traffic patterns and make intelligent predictions about future resource needs, significantly improving allocation accuracy.
3Ease of operation
If reactive resource allocation is used to meet traffic demands, then implementation simplicity is maintained, but user experience degrades when traffic upticks occur without sufficient resources
Solution Approach 1:
The system performs preliminary resource allocation decisions by predicting future traffic requirements in advance. Instead of waiting for traffic spikes to manifest and then reacting to them, the machine learning models forecast future needs and the resource allocation system proactively adjusts hardware resources before actual traffic increases occur, preventing user experience degradation before it happens.
Solution Approach 2:
The system continuously monitors actual traffic patterns and feeds this information back into the machine learning models. This feedback mechanism allows the system to learn from actual user behavior and traffic patterns, continuously improving the accuracy of its predictions and enabling more reliable resource allocation that proactively prevents user experience degradation.
Data Source
AI summary
Various embodiments include a wireless communication network that comprises resource allocation circuitry. The resource allocation circuitry hosts a traffic forecasting machine learning model, a resource forecasting machine learning model, and a resource allocation machine learning model. The resource allocation circuitry obtains traffic data for a provisioning engine cluster and provides the traffic data to the traffic forecasting model. The resource allocation circuitry obtains an output that comprises a traffic prediction for the provisioning engine cluster and provides the prediction to the resource forecasting model. The resource allocation circuitry obtains an output that comprises a hardware requirement prediction for the provisioning engine cluster and provides the hardware requirement prediction to the resource allocation model. The resource allocation circuitry obtains an output that comprises a hardware allocation recommendation for the network provisioning engine cluster. The resource allocation circuitry allocates hardware resources to the cluster based on the hardware allocation recommendation.