A Comparative Analysis Report of Nature-Inspired Algorithms for Load Balancing in Cloud Environment

Nature-inspired algorithms are often better than traditional algorithms for load balancing because they can handle complex and dynamic environments more effectively. Load balancing is a critical aspect of distributed systems where tasks need to be allocated to different nodes to optimize resource utilization and improve overall performance. Nature-inspired algorithms are well-suited for complex and dynamic load-balancing scenarios that can be challenging for traditional algorithms. These algorithms mimic natural processes and can adapt to changes in workload, network conditions, and system resources, thus improving scalability and optimizing performance. Traditional load-balancing algorithms like round-robin and least connection work well in static and straightforward environments but are often unable to adapt to changing conditions. On the other hand, natural processes-based algorithms like genetic algorithms, particle swarm optimization, and ant colony optimization are capable of adapting to changing conditions more effectively due to their design. Overall, nature-inspired algorithms offer a promising approach to load balancing in distributed systems and can help organizations achieve better resource utilization and performance in their computing environments. This paper provides a survey and comparison of various nature-inspired algorithms for load balancing, along with their respective advantages and disadvantages based on different parameters. Researchers who are interested in working with nature-inspired algorithms for load balancing can benefit from this study as it offers detailed information on each algorithm’s key parameters, including basic operators, common control parameters, algorithm-specific control parameters, features, advantages, disadvantages, and applications. This paper serves as a valuable resource by providing all this information in one place.