A Green-aware Strategy for Virtual Machine Placement in Cloud Datacenters
محورهای موضوعی : Computer EngineeringHedayat Nasrolahi Matak 1 , Homayun Motameni 2 , Behnam Barzegar 3 , Ebrahim Akbari 4 , Hossein Shirgahi 5
1 - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
2 - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
3 - Department of Computer Engineering, Babol Branch, Islamic Azad University, Babol, Iran
4 - Department of Computer engineering, Sari Branch, Islamic Azad University, Sari, IRAN.
5 - Department of computer engineering, Jouybar branch, Islamic Azad University, Jouybar, Iran
کلید واژه: Cloud computing, Virtual machine placement, Multi objective optimization, Meta-heuristic algorithms.,
چکیده مقاله :
This paper presents a method for optimizing the dual-target virtual machine provisioning problem, which is a challenge in cloud data centers. In the cloud environment, it is important to balance the interests of service providers and customers. From the producers’ viewpoint, optimizing energy consumption and reducing costs are essential. From the users’ point of view, it is desirable to achieve an adequate level of quality of service, and network latency is one of the factors that contribute to its reduction. Therefore, optimizing bandwidth usage to reduce network delay is the second important objective considered in this study. To solve this problem, a two-objective method based on a genetic algorithm is presented, which provides near-optimal results in an acceptable time. The evaluations show the superiority of the proposed algorithm in terms of total energy consumption and total traffic in the network compared with methods based on a genetic algorithm, ant colony, greedy FFD algorithm, and randomized deployment method.
This paper presents a method for optimizing the dual-target virtual machine provisioning problem, which is a challenge in cloud data centers. In the cloud environment, it is important to balance the interests of service providers and customers. From the producers’ viewpoint, optimizing energy consumption and reducing costs are essential. From the users’ point of view, it is desirable to achieve an adequate level of quality of service, and network latency is one of the factors that contribute to its reduction. Therefore, optimizing bandwidth usage to reduce network delay is the second important objective considered in this study. To solve this problem, a two-objective method based on a genetic algorithm is presented, which provides near-optimal results in an acceptable time. The evaluations show the superiority of the proposed algorithm in terms of total energy consumption and total traffic in the network compared with methods based on a genetic algorithm, ant colony, greedy FFD algorithm, and randomized deployment method.
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