Application of a hybrid load balancing algorithm for managing the distribution of computational tasks in high-load systems

The growing volume of processed data and the widespread adoption of cloud technologies have made efficient task distribution in high-load computing systems a critical challenge in modern computer science. However, existing solutions often fail to account for resource heterogeneity, dynamic workload variations, and multi-objective optimization, leaving gaps in achieving optimal resource utilization. This study aims to address these limitations by proposing a hybrid load-balancing algorithm that combines the strengths of Artificial Bee Colony (ABC) and Max-Min scheduling strategies. The research employs simulation in the CloudSim environment to evaluate the algorithm’s performance under varying workload conditions (100 to 5000 tasks). Tasks are classified into "light" and "heavy" based on their MIPS requirements, with ABC handling lightweight tasks for rapid distribution and Max-Min managing resource-intensive tasks to minimize makespan. Comparative analysis against baseline algorithms (FCFS, SJF, Min-Min, Max-Min, PSO, and ABC) demonstrates the hybrid approach’s superior efficiency, particularly in large-scale and heterogeneous environments. Results show a 15–30% reduction in average task completion time at high loads (5000 tasks), confirming its adaptability and scalability. The study concludes that hybrid algorithms, integrating heuristic and metaheuristic techniques, offer a robust solution for dynamic cloud environments. The proposed method bridges the gap between responsiveness and strategic resource allocation, making it viable for real-world deployment in data centers and distributed systems. The practical significance of the work lies in increasing energy efficiency, reducing costs and ensuring quality of service (QoS) in cloud computing.

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