<ul><li>A study proposes an online learning-decomposition framework to dynamically decompose Service Level Agreements (SLAs) in network slice management.</li><li>The framework continuously updates risk models based on the most recent feedback using components like online gradient descent and FIFO memory buffers.</li><li>Empirical study shows that the proposed framework outperforms static approaches, providing more accurate and resilient SLA decomposition.</li><li>Comprehensive complexity analysis of the solution is also provided.</li></ul>

Online SLA Decomposition: Enabling Real-Time Adaptation to Evolving Network Systems

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