Adaptive Task Scheduling in Edge-Fog-Cloud with Network Failure Resilience

Task scheduling across the edge–fog–cloud continuum faces significant challenges from dynamic network conditions,
including bandwidth fluctuations, intermittent connectivity, and variable latency. This paper presents an enhanced comparative
study of machine learning schedulers with quality-of-service thresholds versus traditional heuristic approaches under both
stable and failure-prone network scenarios. Using the EdgeSimPy simulation framework integrated with the real-world AI4Mobile iV2I+ dataset, we evaluate three ML models in two configurations, compared against four heuristic schedulers across 1000 tasks per experiment. The enhanced implementation integrates federated learning with encryption-based privacy preservation, multi-tenant scheduling with differentiated service policies, realtime constraint monitoring, and mobility-aware node management. Results demonstrate that threshold-constrained ML schedulers achieve optimal balance across latency, energy consumption, and cost metrics, reducing energy consumption by up to 44% versus ML-only approaches and 50% versus heuristics. Under failure-prone conditions with 10% probability of datarate degradation, threshold-constrained ML maintains robust performance with 95.7–97.2% success rates, demonstrating superior adaptability compared to pure ML approaches.