Maximum support priority queuing approach using AI-powered swarm intelligence technique for resource allocation and load balancing for webserver in cloud environment

Cloud computing is frequently scheduling user requests for resources and services in distant environments. Using resource scheduling, user resource requests are handled according to priority within a specified time limit. The cloud design is resource-centric and built on the Energy Proficient Computing Framework (EPCF). Computers are provided with renewable energy based on the number of processes they can execute and the state in which they operate. Load balancing is the main challenge with cloud computing, energy efficiency, and resource utilization are some of the issues that cloud computing environments. To address these problems, task scheduling is employed as a consolidation strategy. We tackle the issue of the energy consumption of virtualized cloud data centers and their effective resource use. Task classification and criteria serve as the foundation of the suggested method, which improves resource use and scheduling effectiveness. Preliminary Service Arrival Impact Rate (PSAIR) workflow activities are used in the first phase to prevent the dropping of jobs with more dependencies and lengthy execution times in different queues. According to the On-Demand Job Request Priority Intensive Rate (OJRPIR), which is crucial for cloud computing queue job scheduling, tasks are then categorized according to the intensities of the necessary resources. Utilizing the Particle Swarm Intelligence Allocation Model (PSIM), the optimal schedules are chosen. Lastly, the Energy Efficient-Maximal Support Priority Queuing Approach (EE-MSPQA) is a priority-based scheduling method for distributed systems or cloud computing that aims to increase energy efficiency while efficiently handling work requests. Experiments validated the suggested method. Results from comparisons on benchmark datasets are shown. In terms of energy usage and load balancing, the results demonstrate the superiority of the proposed algorithm over the other algorithms that were analyzed. The simulation results for the proposed EE-MSPQA method indicate a performance metric of -0.1948.