Intelligent and Scalable Backend Architecture for High-Concurrency Distributed Data Processing
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Abstract
With the rapid evolution of cloud computing and large-scale distributed applications, backend systems are required to simultaneously satisfy high concurrency, low latency, scalability, and strong reliability. Traditional monolithic architectures are increasingly incapable of supporting modern workloads due to limited flexibility and poor fault isolation. In this paper, we propose a scalable and intelligent backend architecture integrating microservices, asynchronous message-driven communication, and adaptive resource scheduling. The architecture incorporates data-driven intelligence, deep learning-based monitoring, and reinforcement learning-based scheduling to enhance system robustness and efficiency. Experimental results demonstrate that the proposed system significantly improves throughput, reduces latency, and enhances fault tolerance under high-concurrency conditions.
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