Adaptive load balancing algorithms for cloud computing distributed systems

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.