Workload Balancing in Distributed Cloud-Based Production Systems Using Swarm Optimization

Abstract

The emergence of Industry 4.0 unlocked new possibilities for using production cloud systems which enable businesses
to optimize the use of computational resources. However, other problems arise related to effectively distributing
workloads because of constantly changing job workloads, diverse resources, and problems with expansion. This paper
offers a new methodology for balancing the workload with swarm optimization, particularly focusing on cross-domain
recalibration PSO to dynamically adjust the workload in distributed cloud environments. It is suggested that the main
goals are minimizing task fulfillment time, maximizing resource use, and lowering latency in the system. The proposed
approach is tested with traditional load balancing approaches such as round-robin and least connection algorithms.
The results testify that the approach based on response PSO is more effective than conventional techniques concerning
the response time and efficiency of resource consumption. Performance evaluation using simulated cloud production
scenarios illustrates the effectiveness and flexibility of swarm optimization in changing environments. The work is
focused on the development of sophisticated algorithms for balancing the load in remote manufacturing systems and
represents a step toward intelligent and more agile smart factories.