Migration of containers on the basis of load prediction with dynamic inertia weight based PSO algorithm

Due to the necessity of virtualization in a fog environment with limited resources, service providers are challenged to reduce the energy consumption of hosts. The consolidation of virtual machines (VMs) has led to a significant amount of research into the effective management of energy usage. Due to their high computational overhead, the existing virtualization techniques may not be suited to minimize the energy consumption of fog devices. As containers have recently gained popularity for encapsulating fog services, they are an ideal candidate for addressing this issue, particularly for fog devices. In the proposed work, an ensemble model is used for load prediction on hosts to classify them as overloaded, underloaded, or balanced. A container selection algorithm identifies containers for migration when a host becomes overloaded. Additionally, an energy-efficient container migration strategy facilitated by a dynamic inertia weight-based particle swarm optimization (DIWPSO) algorithm is introduced to meet resource demands. This approach entails migrating containers from overloaded hosts to others in order to balance the load and reduce the energy consumption of hosts located on fog nodes. Experimental results demonstrate that load balancing can be achieved at a lower migration cost. The proposed DIWPSO algorithm significantly reduces energy consumption by 10.89% through container migration. Moreover, compared to meta-heuristic solutions such as PSO, ABC (Artificial Bee Colony), and E-ABC (Enhanced Artificial Bee Colony), the proposed DIWPSO algorithm shows superior performance across various evaluation parameters.