云计算中基于机器学习的QoS迁移方案:调查和研究挑战

A.-Young Son, E. Huh, Sang-Ho Na, Pillwoo Lee
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引用次数: 0

摘要

虚拟机迁移已经成为云数据中心的一个热门话题。针对QoS提出了许多VM迁移方案,旨在改进影响cdc的各种度量。此外,它将机器学习方法与CDC中的建模和预测相结合。基于预测度量的迁移方案可以大大提高物理机的资源利用率。有效的虚拟机迁移方案可以降低数据中心的功耗和时间。因此,它需要考虑可能影响迁移性能和能源效率的指标。在本文中,我们总结和分类疾病预防控制中心以前的迁移方法。最后,对该领域的研究问题进行了讨论。在未来的工作中,我们将研究热迁移机制,以提高各种疾病预防控制中心的热迁移性能和能效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Migration scheme based machine learning for QoS in cloud computing: Survey and research challenges
VM migration has become a hot topic in the Cloud Dater Centers (CDCs). Numerous VM migration schemes are proposed for QoS aimed to improve various metrics affecting the CDCs. Also, it combines a Machine learning approach with modeling and predicting in CDC. Migration scheme through prediction based metric can greatly improve the physical machines resource utilization. Also, effective VM migration scheme can reduce the power consumption and time of the data centers. Thus, it needs to consider metrics which may impact the migration performance and energy efficiency. In this paper, we summarize and classify previous approaches of migration in CDCs. Furthermore, we conclude with a discussion of research problems in this area. In the future work, we will study on live migration mechanism to improve the live migration performance and energy efficiency in the variety of CDCs.
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