Abstract
Green computing focuses on the energy consumption to minimize costs and adverse environmental impacts in data centers. Improving the utilization of host computers is one of the main green cloud computing strategies to reduce energy consumption, but the high utilization of the host CPU can affect user experience, reduce the quality of service, and even lead to service-level agreement (SLA) violations. In addition, the ant colony algorithm performs well in finding suitable computing resources in unknown networks. In this paper, an energy-saving virtual machine placement method (UE-ACO) is proposed based on the improved ant colony algorithm to reduce the energy consumption and satisfy users’ experience, which achieves the balance between energy consumption and user experience in data centers. We improve the pheromone and heuristic factors of the traditional ant colony algorithm, which can guarantee that the improved algorithm can jump out of the local optimum and enter the global optimal, avoiding the premature maturity of the algorithm. Experimental results show that compared to the traditional ant colony algorithm, min-min algorithm, and round-robin algorithm, the proposed algorithm UE-ACO can save up to 20%, 24%, and 30% of energy consumption while satisfying user experience.
Highlights
Cloud computing [1] is a further extension of distributed computing, parallel computing, and grid computing
(2) In order to adapt to the dynamic characteristics of cloud computing resources and meet user experience, solve cloud computing resource scheduling problems, and reduce data center power consumption, we propose a user experience-oriented energysaving virtual machine placement method (UEACO)
Task waiting time is an important indicator of user experience, which directly affects the score of user experience and affects the execution process of the algorithm. e results show that the energy-saving virtual machine placement method oriented to user experience can better meet user needs
Summary
Cloud computing [1] is a further extension of distributed computing, parallel computing, and grid computing. By modifying the pheromone and heuristic factor update methods and defining the parameter regulatory factor (RF), the improved roulette probability selection mechanism guides the ant to search, avoiding the algorithm to enter the local optimal due to premature convergence, and finds the optimal solution for virtual machine placement, effectively reduces energy consumption, and achieves a balance between energy consumption and user experience. (1) Considering the constraints of user experience while optimizing energy consumption, the energy-saving virtual machine placement system model of the cloud data center is established. (2) In order to adapt to the dynamic characteristics of cloud computing resources and meet user experience, solve cloud computing resource scheduling problems, and reduce data center power consumption, we propose a user experience-oriented energysaving virtual machine placement method (UEACO). (3) e advantage of this algorithm is that it has a positive feedback mechanism, fast convergence speed, and can effectively solve the problem that the traditional ant colony algorithm falls into a local optimum. e algorithm does a good job of finding suitable computing resources in unknown networks
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