Optimization of Load Balancing and Real-Time Scheduling in Distributed Systems for Cross-Language Text Processing Tasks

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INTRODUCTION: With the rapid expansion of cross-language text processing applications, distributed systems must support large-scale multilingual workloads with high efficiency and responsiveness. Effective load balancing and real-time scheduling are therefore essential for maintaining system performance. However, many existing approaches rely on static or general-purpose scheduling strategies, which fail to capture language-dependent workload variations, resulting in limited scalability and inefficient resource utilization. OBJECTIVES: This study aims to improve load balancing efficiency and real-time scheduling performance in distributed systems for cross-language text processing. The focus is on addressing dynamic task variations, heterogeneous resource demands, and robustness challenges in multilingual, noisy, and fluctuating computing environments. METHODS: A cross-language-aware dynamic load balancing and scheduling optimization framework based on reinforcement learning is proposed. The framework incorporates language pair, task type, priority, input length, estimated resource demand, and real-time node status into an adaptive scheduling strategy. Denoising mechanisms and feature fusion modules are further introduced to enhance scheduling stability and decision robustness. RESULTS: Experimental results show that the proposed method outperforms existing approaches in response time, resource utilization, and task completion rate. The framework achieves a task completion rate of 96.5%. Under high-noise conditions, the reduction in task completion rate is approximately 50% lower than that of baseline methods, demonstrating improved robustness. CONCLUSION: The proposed framework provides an effective solution for dynamic scheduling and load balancing in cross-language text processing systems. By coupling multilingual task characteristics with distributed resource states, it offers scalable support for multilingual distributed computing and new insights into reinforcement learning-based scheduling optimization.