Machine-learning predictive autoscaling for Flink

Introduced a predictive autoscaler for Flink applications that uses machine learning to forecast workload and proactively adjust CPU resources. The system consists of a workload forecast model (time-series forecasting on Kafka throughput) and a resource prediction model (regression mapping Kafka throughput to Flink CPU usage). It aims to prevent reactive scaling spirals, restart spikes, and address limited parallelism constraints by enabling vertical scaling and deterministic resource allocation.