SDN Path Selection Using Performance Scores

The system performs preliminary actions by collecting and storing historical network performance data before it is needed for path selection. Network devices continuously gather performance metrics (latency, loss, jitter) and store them in databases, so when path selection is required, the information is already prepared and available, eliminating the need to wait for recent snapshots while maintaining high accuracy.

The system implements feedback mechanisms where path selection decisions are continuously refined based on historical performance data. The controller receives feedback about actual path performance, compares it with predicted performance, and uses machine learning algorithms to improve future path selection decisions. This creates a closed-loop system that learns from past performance to make more accurate predictions.

The system introduces an intermediary controller that acts as a mediator between network devices and the path selection process. The controller collects performance data from multiple devices, performs the computationally intensive machine learning analysis and historical data processing, and then provides path selection recommendations to the network devices. This distributes the computational burden and prevents any single device from becoming overloaded.

The system creates simplified copies or representations of complex network performance data through performance scores and metrics. Instead of processing raw, detailed performance data from multiple sources, the system generates aggregated performance scores that capture the essential characteristics of network paths. These simplified representations can be processed more efficiently while retaining the necessary information for accurate path selection.

The system performs preliminary machine learning training and model generation in advance, so that when path selection is needed, pre-computed models and performance predictions are already available. The machine learning algorithms are trained on historical data beforehand, creating ready-to-use prediction models that can quickly evaluate new path options without requiring time-consuming real-time computation.

The system applies partial machine learning analysis by focusing on the most critical performance factors and using simplified models for initial path selection. Rather than running complete, computationally intensive machine learning algorithms for every path evaluation, the system uses partial analysis of key metrics and reserves full machine learning processing for cases where simpler methods are insufficient, thus reducing average processing time while maintaining SLA compliance.