Enhancement of ACOTS Algorithm for Virtual Machine Placements in Cloud Data Centers

Since many cloud data centers provide worldwide services on demand, it has been a popular research topic in recent years. Users and virtual machines grow in the number directly to data centers’ expanding size. Virtual Machine Placement methods are mainly used in data centers to consolidate servers. Therefore, the placement of virtual machines is the most active research area today. The performance of VM Placements in cloud computing depends on various factors, including resource management, power consumption, and others. This paper provides the optimal solution for VM placements in cloud environments using ACOTS (Ant Colony Optimization with Tabu Search) hybrid metaheuristic algorithm enhancing with the feature of modified Eagle strategy. The hybridization leverages ACO’s global search capabilities, TS’s memory-driven diversification, and the adaptive exploration-exploitation balance of EagleMOD (modified EAGLE strategy). By integrating these techniques, the proposed algorithm enhances convergence speed and resolves the issue of local optima, ensuring robust performance across various scenarios.