With the development of future networks, the popularization of technologies such as 5G and the Internet of Things (IoT) has led to new characteristics of network environments with high dynamics, low latency, and complexity. Traditional service grid architectures have limitations in coping with the dynamics and complex traffic management of future networks, such as increased latency and inflexible traffic management. This study aims to design an intelligent and adaptive service grid architecture to optimize the performance to cope with the new challenges in future networks.
To address the network routing optimization problem, this study uses deep Q-learning algorithms to achieve intelligent routing, which effectively reduces the network delay and packet loss rate. To address the complexity of traffic management in large-scale distributed systems, a dynamic traffic management module combining convolutional neural networks and long and short-term memory networks is designed to improve the accuracy of traffic prediction. To enhance network adaptability and fault recovery, the study introduces a network resilience enhancement module, which ensures service continuity under high load and fault conditions. In addition, efficient transmission and low resource consumption under multiple network protocols are realized by the design of protocol adaptive module.
Experimental evaluation of the entire optimized architecture shows that this intelligent service grid architecture exhibits excellent performance in future network environments. The intelligent routing module effectively reduces network delay and packet loss, the dynamic traffic management module improves the accuracy of traffic prediction, the network resilience enhancement module ensures service continuity under high load and fault conditions, and the protocol adaptation module demonstrates efficient transmission and low resource consumption under multiple network protocols. Through the optimal design of the service grid architecture and the introduction of intelligent technologies, the performance and adaptability of service grids in future network environments are successfully enhanced, and the deficiencies faced by traditional service grids in dynamic network environments are addressed. This research provides important technical support and development direction for intelligent service grids in future networks.