MONITORING OF SERVICE LEVEL AGREEMENTS (SLA) IN B5G/6G NETWORK ENVIRONMENTS: A PROPOSAL
Abstract
The dynamism and heterogeneity of traffic in 5G and 6G networks make it difficult to guarantee service levels (SLA). A system is proposed for predictive SLA management and monitoring by integrating real-time monitoring, dynamic flow redirection, and predictive analysis with Machine Learning in SDN environments. By detecting metric violations or anticipating congestion, it redirects traffic, prioritizes low latency, and proactively adapts network resources to optimize SLAs. Although further implementation and evaluation is required, the proposal represents a
a promising solution to the current challenges of ensuring the quality of service with heterogeneous traffic in new-generation networks.
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