## 4 Analytical Framework for Heterogeneous Service Provisioning
### Algorithms for Rate-Adaptive Provisioning
We also performed a sensitivity analysis by varying key system parameters such as RL learning rate, link bandwidth capacity, and LSTM prediction window. ML-RASPF showed robust behavior across these settings, consistently maintaining its performance advantage in latency and throughput across all configurations. ### Results and Analysis

In this section, we present a comprehensive analysis of the simulation results evaluated against four recent and diverse baseline techniques: JANUS [7], energy-aware offloading [8], FCFS, and LRFS. We explain evaluation results in the following. #### 5.4.1 Latency

Figure 5 illustrates the transmission latency across the three representative healthcare services: diagnostic kiosks, video displays, and patient Wi-Fi. ML-RASPF consistently achieves the lowest latency across all services due to its predictive load routing and adaptive congestion handling. For instance, in diagnostic kiosk services, ML-RASPF records a latency of 117 ms, compared to 143 ms for JANUS, 155 ms for energy-aware offloading, and over 170 ms for FCFS and LRFS approaches. Video services and Wi-Fi applications also reflect similar trends, albeit with slightly relaxed constraints. Similar trends were observed for end-to-end delay, where the compounded effect of transmission, queueing, and processing time follows the same ranking among approaches, with ML-RASPF retaining the lead in latency-sensitive service delivery. Thus, results for end-to-end delay are omitted for brevity but follow similar trends as transmission latency. ML-RASPF maintained up to 20-30% lower end-to-end delay than the baselines, reinforcing its advantage for real-time responsiveness in smart healthcare environments. #### 5.4.2 Service Delivery Rate

Figure 6 compares the service delivery rate (in Mbps) for three healthcare service types--diagnostic kiosks, video displays, and patient Wi-Fi across five approaches. ML-RASPF consistently achieves the highest delivery rates: 8.5 Mbps for diagnostic kiosks, 11.8 Mbps for video displays, and 6.8 Mbps for patient Wi-Fi. In comparison, the next best method, JANUS, achieves 7.1 Mbps, 8.9 Mbps, and 6.1 Mbps, respectively, for the same services. This reflects an average improvement of 18-22% over JANUS and up to 35% compared to FCFS and LRFS, especially in bandwidth-heavy services. These gains are attributed to ML-RASPF's proactive traffic forecasting (via LSTM) and its RL rate control mechanism, which intelligently adjusts service rates based on predicted traffic and real-time latency feedback. Such capabilities enable it to prioritize high-impact services and ensure consistently high throughput even under fluctuating load conditions. #### 5.4.3 Energy Consumption

Figure 7 presents the total energy consumption (static + dynamic) for the three healthcare service types under different provisioning approaches. ML-RASPF demonstrates the most energy-efficient performance across all service categories. For diagnostic kiosks, ML-RASPF reduces energy usage by approximately 18.2% compared to JANUS and by 26.5% compared to the energy-aware offloading method. In the case of video displays, ML-RASPF consumes around 21.7% and 16.8% less energy than FCFS and LRFS, respectively. For patient Wi-Fi services, ML-RASPF achieves a 19.3% reduction over JANUS and a 14.6% saving relative to LRFS. This improvement stems from ML-RASPF's RL-based

Figure 5: Transmission latency for diagnostic kiosks, video displays, and patient Wi-Fi services. Figure 6: Service delivery rate vs. buffer size for diagnostic kiosks, video displays, and patient Wi-Fi services. rate control, which reduces unnecessary data transmission and optimizes local processing at the mist layer. The ability to intelligently route latency-tolerant services away from overloaded nodes also contributes to reduced overall energy usage. These findings confirm ML-RASPF's suitability for resource-constrained environments, where sustainable and low-power operation is essential.