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引用次数: 0
摘要
虚拟机放置（VMP）在提高云数据中心（CDC）效率方面发挥着重要作用。随着云计算的使用急剧增加，似乎有必要采用有效的算法来降低云数据中心的功耗。众所周知，VMP 是一个确定性算法无法在多项式时间内解决的 NP-Hard 问题。本文提出了一种名为 "组合随机最佳首次拟合"（Combinated Random Best First Fit，CRBFF）的算法，目的是提高服务质量（QoS），将虚拟机（VM）最佳地放置在异构物理机（PM）上。在谷歌计算引擎（GCE）、亚马逊网络服务弹性计算云（AWS EC2）和微软 Azure 场景下，通过不同指标对 CRBFF 的有效性进行了评估，结果表明 CRBFF 的性能优于其他常见算法。
A hybrid energy-aware algorithm for virtual machine placement in cloud computing
Virtual Machine Placement (VMP) plays a significant role in improving efficiency of Cloud Data Center (CDC). With the dramatic increase in the use of cloud computing, it seems necessary to apply effective algorithms to reduce the power consumption of CDC. VMP is known as a NP-Hard problem that cannot be solved by deterministic algorithms in polynomial time. In this paper, an algorithm named Combinated Random Best First Fit (CRBFF) is proposed with the aim of increasing the Quality of Service (QoS), in which Virtual Machines (VMs) are optimally placed on heterogeneous Physical Machines (PMs). The effectiveness of CRBFF is evaluated by different metrics on Google Compute Engine (GCE), Amazon Web Service Elastic Compute Cloud (AWS EC2) and Microsoft Azure scenarios and the results show that CRBFF performs better than other common algorithms.
期刊介绍：
Computing publishes original papers, short communications and surveys on all fields of computing. The contributions should be written in English and may be of theoretical or applied nature, the essential criteria are computational relevance and systematic foundation of results.
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