Green computing focuses on the energy consumption to minimize costs and adverse environmental impacts in data centers.\nImproving the utilization of host computers is one of the main green cloud computing strategies to reduce energy consumption,\nbut the high utilization of the host CPU can affect user experience, reduce the quality of service, and even lead to service-level\nagreement (SLA) violations. In addition, the ant colony algorithm performs well in finding suitable computing resources in\nunknown networks. In this paper, an energy-saving virtual machine placement method (UE-ACO) is proposed based on the\nimproved ant colony algorithm to reduce the energy consumption and satisfy usersâ?? experience, which achieves the balance\nbetween energy consumption and user experience in data centers. We improve the pheromone and heuristic factors of the\ntraditional ant colony algorithm, which can guarantee that the improved algorithm can jump out of the local optimum and enter\nthe global optimal, avoiding the premature maturity of the algorithm. Experimental results show that compared to the traditional\nant colony algorithm, min-min algorithm, and round-robin algorithm, the proposed algorithm UE-ACO can save up to 20%, 24%,\nand 30% of energy consumption while satisfying user experience.
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