An Adaptive Artificial Bee Colony Approach for Dynamic Load Balancing in Cloud Computing
Ch. V. S. Satyamurty, Bandi Rambabu · 2025
Load balancing plays a pivotal role in cloud computing by ensuring optimal resource utilization, minimizing response time, and preventing server overload. However, conventional load balancing algorithms often lack the adaptability required to handle dynamic and unpredictable workloads efficiently. This paper proposes an Adaptive Artificial Bee Colony (AABC) algorithm, an enhancement of the classical ABC approach, incorporating adaptive control parameters that enable dynamic adjustment of search strategies based on real-time workload conditions. The algorithm is designed to improve convergence speed and ensure balanced task allocation across virtual machines. The performance of AABC is rigorously evaluated in a simulated cloud environment and benchmarked against Artificial Bee Colony (ABC), Honeybee Foraging Algorithm (HFA), and Modified Particle Swarm Optimization (MPSO). Experimental results demonstrate that AABC achieves significant improvements, including up to 18.5 % reduction in response time, enhanced load distribution, and increased energy efficiency. These findings establish AABC as a scalable and effective solution for managing dynamic workloads in modern cloud infrastructures.