Algorithm for load balancing in distributed control systems for large-scale data streams in real time

Abstract

The article analyses algorithmic methods of load balancing in distributed control systems that process large volumes of data streams under real-time constraints. As flow rates and architectural complexity increase, the operational goal is to achieve throughput and limited latency while ensuring even utilization of heterogeneous computing resources. Previous studies show that in real-time environments, misallocation of work leads to queues, long wait times, and delays in message processing. The main causes of imbalance are peak loads, rapid changes in flow intensity, concentration of events around “hot” buttons, and hardware heterogeneity in nodes. State-of-the-art load balancing strategies are systematized, including static, dynamic, and adaptive algorithms, as well as feedback-based mechanisms. The limitations of static policies in streaming settings are emphasized, and the foundations of algorithms capable of quickly responding to system state changes are established. A conceptual adaptive approach to load balancing based on online node performance indicators, including task queue occupancy, CPU load, and task execution time, is proposed. The performance of the balancing strategies is evaluated using throughput, p95 and p99 delay quantiles, node-level load uniformity, and the proportion of lost messages. The results show that adaptive load balancing improves the reliability of distributed systems by reducing the risk of congestion and stabilizing queuing delay during heavy flow processing. Further research should focus on workload prediction, context-sensitive threshold adaptation, and environment-sensitive reallocation policy design for streaming manufacturing platforms.