## 4 Analytical Framework for Heterogeneous Service Provisioning
### Algorithms for Rate-Adaptive Provisioning
We evaluate transmission latency and end-to-end delay. Transmission latency is the time taken for a service request to reach the service provider from the service consumer via the mist and edge nodes. End-to-end latency is the total time taken for a complete service transaction, including request transmission, service execution, and result delivery back to the consumer. Low latency is crucial for time-sensitive healthcare applications, such as remote monitoring, emergency alert systems, and clinical decision support systems. #### 5.3.2 Service Delivery Rate

This metric captures the rate at which services are successfully delivered to end-users, expressed in Mb/s. A higher service delivery rate indicates better bandwidth utilization and overall system throughput. In healthcare contexts, this directly impacts the QoS for applications such as telehealth, medical video streaming, and real-time diagnostics. #### 5.3.3 Energy Consumption

We measure total energy consumption, including both static and dynamic components across mist, edge, and cloud layers. Static energy accounts for idle node consumption, while dynamic energy is proportional to service-specific computation and communication loads. This metric reflects the framework's ability to deliver healthcare services in an energy-aware and sustainable manner. #### 5.3.4 Bandwidth Utilization

Bandwidth utilization is defined as the ratio of consumed bandwidth to the total available capacity across all network links. High utilization implies effective communication, scheduling, and link optimization. This metric helps quantify how well the system avoids network congestion and supports concurrent services. #### 5.3.5 Load Balancing Efficiency

Load balancing efficiency measures the uniformity of workload distribution across mist and edge nodes. It is computed using the standard deviation of task loads across nodes, with lower variance indicating better balance. This metric assesses the framework's ability to prevent node overload and idle states, ensuring high availability and resource fairness under dynamic IoT traffic conditions. #### 5.3.6 Baselines for Comparison

We evaluate ML-RASPF against four diverse baseline techniques, representing a spectrum of heuristic, scheduling-based, and energy-aware optimization strategies. These baselines enable a comprehensive comparative analysis of latency handling, throughput, energy efficiency, and adaptability under dynamic workloads:

* Energy-aware offloading [8]: an energy-aware task offloading framework that leverages dynamic load balancing and resource compatibility evaluation among fog nodes. The method uses lightweight metaheuristics to optimize offloading decisions based on task priority, fog availability, and energy profile, but it does not consider ML-based traffic prediction or RL-based rate control. * JANUS [7]: a latency-aware traffic scheduling system for IoT data streaming in edge environments. JANUS employs multi-level queue management and global coordination using heuristic stream selection policies. Although effective in managing latency-sensitive streams, it does not perform joint rate-latency optimization or predictive traffic adaptation. * FCFS: a baseline queuing strategy where incoming service requests are served in the order they arrive, without considering bandwidth, latency sensitivity, or system state. FCFS represents non-prioritized resource allocation and serves as a lower-bound reference. * LRFS: a heuristic baseline that prioritizes service requests with the smallest bandwidth requirements. While this may help reduce short-term congestion, it often leads to unfair treatment of larger or high-priority flows, particularly in healthcare workloads. All comparative results are averaged over 10 simulation runs and include 95% confidence intervals. These baselines collectively offer a rigorous evaluation landscape to highlight ML-RASPF's adaptability and QoS-aware performance in real-time healthcare IoT scenarios.