Load Balancing in Cloud Computing: A Proposed Novel Approach Based on Walrus Behavior

This research provides a comprehensive evaluation of load-balancing algorithms in cloud computing, classifying them into static, dynamic, and nature-inspired categories. Static algorithms, such as Round Robin and Min-Min, offer simplicity and efficiency in environments with stable workloads but struggle with adaptability to varying demands. Dynamic algorithms like Throttled Load Balancing and Least Connection are more flexible, adjusting to real-time server load changes and improving resource utilization, though they introduce higher overhead and computational costs. Nature-inspired algorithms, including Ant Colony Optimization and Particle Swarm Optimization, draw from biological processes to achieve high scalability, fault tolerance, and adaptability. A novel Walrus Optimization Algorithm (WaOA) is proposed, inspired by the social and migratory behaviors of walruses, to address challenges such as task bottlenecks and resource underutilization. MATLAB simulations reveal that WaOA outperforms traditional and nature-inspired methods in terms of scalability, response time, and resource optimization. The study concludes with suggestions for integrating machine learning, hybrid techniques, and real-world testing to further enhance WaOA’s effectiveness.