Swarm Intelligence-Based Algorithm for Workload Placement in Edge-Fog-Cloud Continuum

This paper addresses the workload placement problem in the edge-fog-cloud continuum. We model the edge fog-cloud computing continuum as a multi-agent framework consisting of networked resource supply and demand agents. Inspired by the swarm intelligence behavior of the ant colony optimization, we propose a workload scheduler for the arriving demand agents to increase local resource utilization and reduce communication costs without relying on a centralized scheduler. Like the ants, the demand agents will release pheromones on the resource agent to indicate the available resources. The next arriving demand agent will most probably choose a neighbor, following the pheromone value and communication cost. The framework’s performance is evaluated in terms of local resource utilization, dependency on fog and cloud, and communication cost. We compare these metrics for the ant-inspired algorithm with random and greedy algorithms.The simulation results reveal that the proposed algorithm inspired by swarm intelligence can increase resource utilization at the edge and reduce the dependency on higher layers, while also decreasing the communication cost for the task of resource allocation