Dynamic Container Configuration for Real-Time Batch Optimization

The patent implements dynamic batch configuration by training a machine learning model (e.g., reinforcement learning agent) that continuously learns from historical workload data and dynamically adjusts batch configuration parameters such as batch size, parallelism degree, and resource allocation. This allows the system to adapt configurations in real-time based on current workload characteristics, resolving the contradiction between configuration stability and processing speed optimization.

The system establishes a feedback loop where execution results and performance metrics from batch jobs are continuously collected and fed back to the ML model. The model uses this feedback to learn optimal configuration strategies and make dynamic adjustments. This feedback mechanism enables the system to maintain reliability through learned patterns while achieving productivity improvements through adaptive optimization.

The patent applies preliminary action by pre-training the ML model on extensive historical workload data and error patterns before actual batch processing. The model learns to predict potential errors and identify optimal configurations that prevent errors from occurring in the first place. This preliminary learning phase enables the system to make accurate configuration decisions without requiring repeated trial-and-error reconfigurations during actual processing.

The system implements self-service through autonomous ML-driven configuration selection and automatic error handling. When errors are detected, the system automatically adjusts configurations based on learned patterns from the ML model without requiring manual intervention or complete reconfiguration cycles. This self-service capability maintains high processing accuracy while minimizing time loss through automated, intelligent adaptation.