Genetic-based Framework for Joint Latency and Cost Reduction in Edge-Fog Environments

Reducing latency in the Internet of Things (IoT) remains a critical concern. While cloud computing facilitates communication, it falls short of meeting real-time requirements reliably. Edge and fog computing have emerged as viable solutions by positioning computing nodes closer to end users, offering lower latency and increased processing power. An edge-fog framework comprises various components, including edge and fog nodes, whose strategic placement is crucial as it directly impacts latency and system cost. This paper presents an effective and tunable node placement strategy based on a genetic algorithm to address the optimization problem of deploying edge and fog nodes. The task assignment for this algorithm involves two key components: first, it identifies the nearest node to a given user; second, it assesses the node’s capacity to ensure it has sufficient frequency resources to handle the assigned tasks. The ultimate objective is to minimize latency and cost through optimal node placement. Simulation results demonstrate the proposed framework achieves up to 8.4% latency and 65.2% cost reduction.