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

When a network slice spans multiple domains, each domain must uphold the End-to-End (E2E) Service Level Agreement (SLA) associated with the slice. This requires decomposing the E2E SLA into partial SLAs for each domain. In a two-level network slicing management system with an E2E orchestrator and local controllers, we propose an online learning-decomposition framework that dynamically updates risk models using recent feedback. This approach utilizes online gradient descent and FIFO memory buffers to enhance stability and robustness. Our empirical study shows the proposed framework outperforms state-of-the-art static methods, offering more accurate and resilient SLA decomposition under varying conditions and sparse data.

A Deep RL Approach on Task Placement and Scaling of Edge Resources for Cellular Vehicle-to-Network Service Provisioning

Cellular-Vehicle-to-Everything (C-V2X) is currently at the forefront of the digital transformation of our society. By enabling vehicles to communicate with each other and with the traffic environment using cellular networks, we redefine transportation, improving road safety and transportation services, increasing efficiency of traffic flows, and reducing environmental impact. This paper proposes a decentralized approach for provisioning Cellular Vehicular-to-Network (C-V2N) services, addressing the coupled problems of service task placement and scaling of edge resources. We formalize the joint problem and prove its complexity. We propose an approach to tackle it, linking the two problems, employing decentralized decision-making using (i) a greedy approach for task placement and (ii) a Deep Deterministic Policy Gradient (DDPG) based approach for scaling. We benchmark the performance of our approach, focusing on the scaling agent, against several State-of-the-Art (SoA) scaling approaches via simulations using a real C-V2N traffic data set. The results show that DDPG-based solutions outperform SoA solutions, keeping the latency experienced by the C-V2N service below the target delay while optimizing the use of computing resources. By conducting a complexity analysis, we prove that DDPG-based solutions achieve runtimes in the range of sub-milliseconds, meeting the strict latency requirements of C-V2N services.