Abstract:
The complexity and dynamism of modern networks pose significant challenges to network management services. Existing technologies often exhibit latency in migrating servic...Show MoreMetadata
Abstract:
The complexity and dynamism of modern networks pose significant challenges to network management services. Existing technologies often exhibit latency in migrating services after encountering problems. However, adopting a proactive approach through anomaly detection before migration enables the early reservation of resources, thereby ensuring overall performance and stability. This paper proposes a Network Management Service Composition (NMSC) migration method leveraging anomaly detection. The method includes an anomaly detection approach using a Transformer model with a time decay mechanism and a multi-agent reinforcement learning algorithm enhanced by a graph attention autoencoder. First, a time decay mechanism is designed to enhance the self-attention mechanism, allowing the model to capture long-term dependencies while maintaining high sensitivity to recent events. Second, a critic network, enhanced by a graph attention autoencoder, enables the multi-agent algorithm to comprehend the interaction dynamics among agents during computation. Experimental results demonstrate that the proposed anomaly detection algorithm significantly outperforms existing algorithms. Furthermore, the service composition migration algorithm exhibits superior performance in terms of reward value and migration delay, thus proving its effectiveness and feasibility.
Date of Conference: 26-29 June 2024
Date Added to IEEE Xplore: 31 October 2024
ISBN Information: