Adaptive load balancing algorithms for cloud computing distributed systems
Ziyu Lin, B. Wang · IET conference proceedings. · 2026
With the rapid advancement of cloud computing technology, load balancing issues in distributed systems have become increasingly prominent, posing significant challenges particularly in optimizing resources for real-time data exchange and integrated services. To address the current issues in integrated cloud computing distributed systems—such as uneven load distribution, low resource utilization, and high demands for real-time data exchange—this paper designs an adaptive load balancing algorithm. By constructing a multidimensional load state evaluation model that integrates service characteristics, combined with an improved minimum connection method and a dynamic threshold adjustment mechanism, the algorithm achieves intelligent task scheduling and optimal resource allocation. The algorithm specifically focuses on key metrics withi n integration system, such as message processing capacity, service response time, and data throughput, while incorporating adaptive mechanisms to address dynamic load fluctuations. Experimental results demonstrate that under high-concurrency conditions, the algorithm maintains response times below 85ms, achieves 89.7% resource utilization, and sustains a load balancing ratio of 0.12. These metrics represent improvements of 42.3% and 27.8%, respectively, compared to traditional methods. For data exchange tasks within the integrated system, average processing latency decreased by 35% and message throughput increased by 40%, significantly enhancing system performance and stability. The proposed algorithm not only a novel solution for improving processing efficiency in integrated services within cloud computing distributed systems but al so provides important reference for the development of next-generation cloud computing architectures and resource management strategies.