## 4 Analytical Framework for Heterogeneous Service Provisioning
### Algorithms for Rate-Adaptive Provisioning
We developed a convex model supported by adaptive pricing, dynamic weight adjustment, and RL-based rate control by formulating service provisioning as a joint optimization prob

Figure 9: Load balancing efficiency across healthcare services. Higher percentages reflect better resource distribution across mist, edge, and cloud layers. lem that considers both latency and service rate objectives. The integration of LSTM-based traffic prediction and RL agents enables service-delivery rate adaptation under varying load conditions and service requirements. Comprehensive evaluation using an extended EdgeCloudSim environment demonstrates that ML-RASPF consistently outperforms state-of-the-art baselines, including heuristic, queue-based, and energy-aware techniques across five critical performance metrics: latency, service delivery rate, energy consumption, bandwidth utilization, and load balancing efficiency. These findings confirm the potential of ML-RASPF to enable adaptive and intelligent service orchestration in real-world hospital environments, supporting use cases such as ECG monitoring, diagnostic kiosks, and emergency response systems. In future work, we plan to extend ML-RASPF with proactive service migration, privacy-preserving mechanisms (e.g., federated learning), and multi-agent reinforcement learning for cooperative and fault-tolerant edge orchestration. We also aim to support emergency-mode prioritization for critical scenarios, incorporate workload-aware soft preemptive scheduling for neural processing unit-based ML execution, and validate the framework through real-world hospital deployments under live traffic conditions. This research received no external funding. The data supporting the findings of this study were synthetically generated during the simulation experiments. These data are not publicly available but may be obtained from the author upon reasonable request. The author declares no conflicts of interest. ## References

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