Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory

TLDR

Fog computing enhances equipment computational power and offers new solutions for industrial applications, yet no quantitative energy‑aware model exists for smart‑meter‑based load balancing and scheduling in smart factories. The study proposes an energy‑aware load balancing and scheduling (ELBS) method for manufacturing clusters using fog computing. An energy‑consumption model linked to workload is built on fog nodes, and an optimization function for load balancing is solved by an improved particle swarm optimization algorithm, with a multi‑agent system coordinating distributed scheduling. Experiments on a candy packing line demonstrate that ELBS achieves optimal scheduling and load balancing for mixing work robots.

Abstract

Due to the development of modern information technology, the emergence of the fog computing enhances equipment computational power and provides new solutions for traditional industrial applications. Generally, it is impossible to establish a quantitative energy-aware model with a smart meter for load balancing and scheduling optimization in smart factory. With the focus on complex energy consumption problems of manufacturing clusters, this paper proposes an energy-aware load balancing and scheduling (ELBS) method based on fog computing. First, an energy consumption model related to the workload is established on the fog node, and an optimization function aiming at the load balancing of manufacturing cluster is formulated. Then, the improved particle swarm optimization algorithm is used to obtain an optimal solution, and the priority for achieving tasks is built toward the manufacturing cluster. Finally, a multiagent system is introduced to achieve the distributed scheduling of manufacturing cluster. The proposed ELBS method is verified by experiments with candy packing line, and experimental results showed that proposed method provides optimal scheduling and load balancing for the mixing work robots.