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首页> 外文期刊>Simulation modelling practice and theory: International journal of the Federation of European Simulation Societies >VMSAGE: A virtual machine scheduling algorithm based on the gravitational effect for green Cloud computing
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VMSAGE: A virtual machine scheduling algorithm based on the gravitational effect for green Cloud computing

机译:VMSAGE:基于绿云计算重力效应的虚拟机调度算法

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The area of sustainable green smart computing highlights key challenges towards reducing cost and carbon dioxide emissions due to the high-energy consumption of Cloud data centres. Here, we focus on the Cloud virtual machine (VM) scheduling that is usually based on simple algorithms, e.g. VM placement on nodes with low memory usage. This approach fails to consider the actual configuration of nodes inside the server rack resulting in local overheating of Cloud data centres. To solve this, we propose a VM scheduling algorithm based on the gravitational effect, called VMSAGE, to optimize energy efficiency of Cloud computing systems. Inspired by the physical gravitation model, we define the thermal repulsion and logical gravitation factors between physical nodes and VMs. To achieve optimized VM scheduling, we propose a gravitation function that refers to the calculation of the logical quality of each VM, host and rack through the algorithm, so as to draw the attractiveness between them. Based on the concept of dimension reduction, VMSAGE conducts the two-dimensional plane target selection twice to reduce the computational cost. Additionally, VMSAGE evaluates attributes of the computer room to carry out the VM deployment. To demonstrate the effectiveness of our solution, we compare it with the Best Fit Heuristic (BFH) and the dynamic voltage and frequency scaling (DVFS) algorithms. The results indicate that our algorithm achieves 10% and 20% optimized energy consumption respectively. The experimental results highlight our contribution, in where VMSAGE can significantly reduce energy consumption rates and VM migration times.
机译:可持续绿色智能计算领域突出了降低成本和二氧化碳排放量的关键挑战,由于云数据中心的高能消耗。在这里,我们专注于通常基于简单算法的云虚拟机(VM)调度,例如, VM放置在具有低内存使用率的节点上。此方法未能考虑服务器机架内的节点的实际配置,从而导致云数据中心的局部过热。为了解决这个问题,我们提出了一种基于重力效应的VM调度算法,称为VMMAGE,以优化云计算系统的能效。灵感来自物理着重模型,我们在物理节点和VM之间定义了热排斥和逻辑重力因子。为了实现优化的VM调度,我们提出了一种引力功能,指的是通过算法计算每个VM,主机和机架的逻辑质量,从而汲取它们之间的吸引力。基于尺寸减小的概念,VMSAGE两次进行二维平面目标选择以降低计算成本。此外,VMSAGE评估计算机房的属性以执行VM部署。为了展示我们解决方案的有效性,我们将其与最佳合适的启发式(BFH)和动态电压和频率缩放(DVFS)算法进行比较。结果表明,我们的算法分别达到了10%和20%的优化能耗。实验结果突出了我们的贡献,在VMSAGE可以显着降低能耗率和VM迁移时间。

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