Cloud computing manages many resources and alterations to meet the demands made by consumers at multiple locations and in numerous applications. Cloud computing presents a significant obstacle to efficient resource usage and balance of loads due to the dynamic nature of consumer requirements and tasks. The inflexibility of conventional methods guarantees inadequate outcomes and waste of resources. Motivated by improved cloud infrastructure management, the present research introduces a novel approach to load optimization and migrating Virtual Machines (VMs) based on agents modelled and Internet of Things (IoT) devices. This research aims to boost cloud performance primarily by optimizing the utilization of resources and distribution of workloads. Hence, a novel approach, the Optimized Virtual Machine Migration Scheme (OVMMS), is introduced that uses the Squirrel Search Algorithm (SSA) for migrating VMs. By emulating squirrel behaviour during migration and search, these agents maximize load balance and the distribution of resources. During the analysis, IoT devices were enabled to monitor and control cloud resources to minimize wastage. Results from experimental analysis demonstrate that the proposed strategy outperforms the state-of-the-art in numerous key areas, including service dissemination, load mitigation, managing failures, mitigating time, and endurance of VM. The results show that the number of failures and the time it takes to mitigate them have dropped dramatically, while services' efficiency and distribution rates have improved substantially. The results illustrate that the squirrel-driven approach holds significant potential for addressing vital issues in cloud computing scenarios. This method asserts that optimizing the distribution of resources and the allocation of workloads may improve systems adaptability, service dependability, and cloud infrastructure operations. The proposed scheme maximizes load mitigation by 11.59%, service dissemination by 8.1%, and VM availability by 8.56%, reducing failures by 12.12% for the maximum service providers.
