OptiGrid: Optimizing Many-Objective Grid-Based Evolutionary Algorithm for Efficient VM Placement in Cloud
محورهای موضوعی : Artificial Intelligence Tools in Software and Data Engineering
1 - Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
کلید واژه: Cloud computing, Virtual machine placement, Many-objective optimization, GrEA evolutionary algorithm.,
چکیده مقاله :
Virtual machine (VM) placement is a process of dynamically mapping VMs to physical machines (PMs) in Cloud datacentre. To optimal virtual machine placement, there is a need for a many-objective optimization approach in a timely manner that can achieve a trade-off between meeting the cloud service provider’s requirements and the user defined QoS parameters. Most of the existing researches on virtual machine placement focus on a single objective or multi objectives into account while there is a lack of effective approaches on problems with more than three objectives, which are often known as many-objective optimization problems. Therefore, in this paper, we present a self-organization framework for virtual machine placement in cloud environment, where key is to orient whether service placement is required or not in each period. In addition, an improved many-objective grid based evolutionary algorithm (iGrEA) is proposed to virtual machine placement, considering the following three objective functions: (1) minimizing energy consumption, (2) minimizing migration time, and (3) minimizing response time. The proposed approach is evaluated via simulation and experiments on real traces. The results show that the proposed approach is more efficient and effective than the other tested approaches.
Virtual machine (VM) placement is a process of dynamically mapping VMs to physical machines (PMs) in Cloud datacentre. To optimal virtual machine placement, there is a need for a many-objective optimization approach in a timely manner that can achieve a trade-off between meeting the cloud service provider’s requirements and the user defined QoS parameters. Most of the existing researches on virtual machine placement focus on a single objective or multi objectives into account while there is a lack of effective approaches on problems with more than three objectives, which are often known as many-objective optimization problems. Therefore, in this paper, we present a self-organization framework for virtual machine placement in cloud environment, where key is to orient whether service placement is required or not in each period. In addition, an improved many-objective grid based evolutionary algorithm (iGrEA) is proposed to virtual machine placement, considering the following three objective functions: (1) minimizing energy consumption, (2) minimizing migration time, and (3) minimizing response time. The proposed approach is evaluated via simulation and experiments on real traces. The results show that the proposed approach is more efficient and effective than the other tested approaches.
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