一种基于阈值模糊的云数据中心虚拟机整合算法

IF 0.6 Q4 COMPUTER SCIENCE, THEORY & METHODS International Journal of Grid and High Performance Computing Pub Date : 2021-01-01 DOI:10.4018/ijghpc.2021010102
N. Baskaran, R. Eswari
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引用次数: 1

摘要

云计算近年来呈指数级增长。随着数据的日益增长,对云存储的需求也越来越大,因此需要建立云数据中心。但是它们消耗了大量的电力,低效地使用资源,而且还违反了服务水平协议。本文提出了一种基于自适应模糊的虚拟机选择算法(AFT_FS)来解决这些问题。该算法采用四个阈值检测过载主机,并采用基于模糊的方法选择虚拟机进行迁移。该算法在实际数据中进行了实验测试,并在各种指标上与现有算法进行了性能比较。仿真结果表明,与其他算法相比,所提出的AFT_FS方法具有最大的能效和最小的SLA率。
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An Efficient Threshold-Fuzzy-Based Algorithm for VM Consolidation in Cloud Datacenter
Cloud computing has grown exponentially in the recent years. Data growth is increasing day by day, which increases the demand for cloud storage, which leads to setting up cloud data centers. But they consume enormous amounts of power, use the resources inefficiently, and also violate service-level agreements. In this paper, an adaptive fuzzy-based VM selection algorithm (AFT_FS) is proposed to address these problems. The proposed algorithm uses four thresholds to detect overloaded host and fuzzy-based approach to select VM for migration. The algorithm is experimentally tested for real-world data, and the performance is compared with existing algorithms for various metrics. The simulation results testify to the proposed AFT_FS method is the utmost energy efficient and minimizes the SLA rate compared to other algorithms.
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来源期刊
CiteScore
1.70
自引率
10.00%
发文量
24
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