The rapid growth of interconnected IoT devices has introduced complexities in their monitoring and management. Autonomous and intelligent management systems are essential for addressing these challenges and achieving self-healing, self-configuring, and self-managing networks. Intelligent agents have emerged as a powerful solution for autonomous network design, but their dynamic and intelligent management requires processing large volumes of data for training network function agents. This poses significant challenges for resource-constrained environments like IoT devices, which have limited computational power, network bandwidth, and power consumption capabilities.
In this paper, we propose a scalable and comprehensive approach called Scalable and Efficient DevOps (SE-DO) to optimize the performance of intelligent agents in resource-constrained environments using a multi-agent system architecture. Our approach leverages a multi-agent-based service design that enables both reactive responses and proactive anticipation and reconfiguration of the network system to meet dynamic requirements. This approach is particularly suitable for next-generation networks like 6G, which demand highly efficient and reliable solutions to support emerging services and applications.
To demonstrate the effectiveness of our approach, we implement a multi-agent system comprising a data collector agent, a data analytics/preprocessing agent, a data training agent, and a data predictor agent. We analyze the impact of different machine learning models, including ANN, CNN, and RNN, on each agent's performance while considering resource constraints in both micro-service and agent-based approaches. Through experiments on real-world data, our proposed architecture achieves high accuracy and efficiency within the limitations of resource-constrained environments
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