GWTBFO, Revolutionizing Cloud Efficiency: A Novel Approach Using Grey Wolf-Based TOPSIS and Bacterial Foraging Firefly Optimization

Background: The growing demand for cloud computing services necessitates innovative strategies to enhance cloud deployment efficiency. Existing cloud load balancing models often grapple with suboptimal resource allocation, leading to increased task makespan, lowered virtual machine (VM) efficiency, and failure to meet task deadlines effectively. Objective: Addressing these limitations, this study aims to introduce a novel model that significantly improves cloud deployment efficiency through a strategic blend of pre-emptive load analysis and resource pre-allocation operations. Methods: The proposed model hinges on the innovative use of Grey Wolf-based TOPSIS (GWTOPSIS) operations for the initial segregation of VMs. This approach considers VM capacity, current load, and availability levels, dynamically modifying internal weights to adapt VM categories based on fluctuating demand. The novelty of GWTOPSIS lies in its extension of traditional TOPSIS methods, allowing for more responsive and demand-aligned VM categorization. Furthermore, the clustering of new tasks based on makespan, deadline, and resource utilization levels—with a higher preference for makespan—addresses critical task scheduling challenges. A pivotal aspect of our model is the integration of the Bacterial Foraging Firefly Optimizer (BFFO) for task assignment to VMs. This optimizer synergizes the exploratory prowess of bacterial foraging with the efficient search capabilities of fireflies, leading to a more effective task-to-VM assignment process. Results: Empirical evaluations of our model reveal substantial improvements over existing models: a reduction in task makespan by 8.5%, a 3.9% boost in VM computation efficiency, a 1.9% enhancement in deadline hit ratio, a 3.5% increase in task diversity, a 2.4% improvement in execution efficiency, and a 3.5% reduction in decision delay Conclusion: These improvements not only demonstrate the efficacy of our model in real-time cloud deployment scenarios but also underscore its potential to revolutionize cloud resource management.