Adaptive Control for Massive Data Stream Processing

Abstract

This paper presents a novel approach to massive data stream processing based on adaptive control. The core idea is to dynamically adjust processing strategies based on the inherent characteristics of the data stream, thereby optimizing efficiency and resource utilization. We leverage reinforcement learning (RL) to learn stream features and utilize these learned features to dynamically adjust processing parameters. This adaptive mechanism allows the system to respond effectively to varying data stream patterns, a critical challenge in modern data processing environments. The proposed method aims to significantly improve throughput and reduce latency compared to traditional static processing methods. The theoretical framework and the core algorithm are detailed, showcasing the potential for enhanced performance in high-volume, high-velocity data streams. The presented approach offers a scalable solution for handling diverse data streams, making it suitable for applications such as network monitoring, financial analytics, and sensor data processing.