Latency Aware Adaptive Ant Colony Algorithm for Service Placement for Healthcare Fog

Fog computing offers a compelling paradigm for real-time healthcare data processing by minimizing latency and bringing computation closer to its source. However, efficient service placement remains a critical challenge for maximizing fog computing’s benefits in this domain. Existing service placement algorithms often struggle to adapt to dynamic fog environments and prioritize low latency for real-time healthcare applications. This research addresses this gap by proposing LA-AACO (Latency Aware Adaptive Ant Colony Optimization), a novel service placement algorithm specifically designed for healthcare applications in fog computing environments. LA-AACO incorporates an adaptive Latency Weight (β) parameter to balance exploration and exploitation during the search process. Additionally, it utilizes a latency-aware fitness function that directly prioritizes solutions with minimal overall latency for healthcare data processing. The LA-AACO is evaluated against established algorithms GWO and CSA with an ECG event monitoring application as the representative healthcare workload. The results demonstrate LA-AACO's superiority across all evaluated metrics, achieving significantly higher fog resource utilization ((93%), lower latency (0.19s), faster response (3.7s), lower energy consumption (1.5J) and faster runtime (39.4s) compared to existing algorithms.