Resource Management in Cloud Computing Using Machine Learning: A Survey
Resource Management in Cloud Computing Using Machine Learning: A Survey
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DOI：
10.1109/icmla51294.2020.00132
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Sepideh Goodarzy;Maziyar Nazari;Richard Han;Eric Keller;Eric Rozner
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文献类型：
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作者：
Sepideh Goodarzy;Maziyar Nazari;Richard Han;Eric Keller;Eric Rozner
在云计算研究中，有效的资源管理是一个至关重要的问题，因为资源过度配置增加了云提供商和云客户的成本；资源配置不足增加了应用延迟，并可能违反服务水平协议，最终导致云提供商失去客户和收入。因此，研究人员一直致力于通过不同的方式来开发云计算环境下的最优资源管理，如容器放置、作业调度和多资源调度。机器学习技术在这一领域得到了广泛的应用。在本文中，我们对云计算环境中利用机器学习技术进行资源管理解决方案的项目进行了全面调查。最后，我们对这两种方案进行了比较。此外，我们还提出了一些未来的方向，以指导研究人员推动这一领域的发展。
Efficient resource management in cloud computing research is a crucial problem because resource over-provisioning increases costs for cloud providers and cloud customers; resource under-provisioning increases the application latency, and it may violate service level agreements, which eventually makes cloud providers lose their customers and income. As a result, researchers have been striving to develop optimal resource management in cloud computing environments in different ways, such as container placement, job scheduling and multi-resource scheduling. Machine learning techniques are extensively used in this area. In this paper, we present a comprehensive survey on the projects that leveraged machine learning techniques for resource management solutions in the cloud computing environment. At the end, we provide a comparison between these projects. Furthermore, we propose some future directions that will guide researchers to advance this field.