NS-OWACC: nature-inspired strategies for optimizing workload allocation in cloud computing

In modern cloud-based computing through pooled resources, service providers must ensure resource accessibility. The migration of workloads to the cloud necessitates careful planning, including the provision of a sufficient number of easily available virtual machines (VMs). This paper addresses the NP-hard problem of load distribution by proposing an advanced scheduling technique designed to tackle this issue directly. The main goal of the investigation is to maximize the assignment of tasks among virtual machines (VMs), ensuring an evenly distributed workload throughout the entire system. We proposed a new method to enhance the distribution of work in cloud-based structures, leveraging insights from spider monkey foraging habits. The proposed optimization technique tries to increase efficiency by strategically distributing jobs to the VMs with the least workload. The algorithm demonstrates robust performance in simulations evaluating load distribution, response time, and efficiency across several task types. The suggested load distribution technique demonstrates substantial enhancements compared to current methods, with an amazing 85% effectiveness in distributing the workload across 20 concurrent tasks. The proposed method outperforms existing algorithms, such as Improved Ant Colony Optimization and Particle Swarm Optimization—Artificial Bee Colony, which achieve load-balancing rates of 70% and 75%, respectively. This paper elucidates the intricacies of workload distribution in cloud-based computing systems while proposing a comprehensive method to improve resource consumption and overall system efficiency, hence advancing distributed computing settings.