Fine-Grained Scaling in Stream Processing Systems: Hybrid CPU-Memory Autoscaling with Graph Neural Networks

Abstract

Stream-processing engines in distributed environments require dynamic resource allocation, though traditional autoscaling treats CPU and memory as coupled units. Recent technological developments showcase disaggregated resource management that allows operator-specific allocation based on runtime computational demands. The Justin framework for Apache Flink demonstrates hybrid CPU/memory scaling through precise resource distribution matching individual operator requirements during execution. StreamTune represents another advancement, employing Graph Neural Networks trained on execution histories to optimize operator parallelism and identify bottlenecks dynamically. These innovations achieve resource consumption reductions while preserving throughput, crucial for cloud-native deployments requiring cost optimization. Such developments prove essential when building large-scale platforms handling millions of events per second with minimal latency and maximum resource efficiency. Graph Neural Network integration enables predictive scaling through pattern recognition from execution data, shifting from reactive to proactive resource management paradigms. Machine learning convergence with distributed stream processing opens pathways for intelligent infrastructure adapting to workload variations while maintaining performance guarantees. Production environments reveal practical importance as operational expenses maintain direct relationships with resource utilization performance. These innovations create the groundwork for autonomous systems to achieve self-optimization via real-time performance indicators and predictive computational models.