In this paper we propose a novel reservation plan adaptation system based on machine learning. In the context of cloud auto-scaling, an important issue is the ability to define and use a resource reservation plan, which enables efficient resource scheduling. If necessary, the plan may allocate new resources upon reservation where a sufficient amount of resources is available. Our solution allows the updating of a reservation plan initially prepared by an administrator. It makes it possible to adapt reservation plans one or more weeks ahead. Hence, it allows time for the administrator to analyze the plan and discover potential problems with resource under-provisioning or over-provisioning, which may prevent server overload in the former case and unnecessary expenses in the latter. It also makes it possible to extract and analyze the knowledge learned, which may provide useful information about resource usage characteristics. The proposed solution is tested on OpenStack using real Wikipedia server traffic data. Experimental results demonstrate that machine learning enables an improvement in resource usage.

中文翻译：

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在云计算中使用机器学习进行VM预留计划调整

在本文中，我们提出了一种基于机器学习的新颖的预订计划自适应系统。在云自动扩展的情况下，一个重要的问题是定义和使用资源预留计划的能力，该计划可以实现有效的资源调度。如果有必要，该计划可以在预留足够资源的情况下根据需要分配新资源。我们的解决方案允许更新管理员最初准备的预订计划。这样就可以提前一个或多个星期调整预订计划。因此，它为管理员留出了时间来分析计划并发现资源不足或资源过剩的潜在问题，这可以防止在前一种情况下服务器过载，而在后一种情况下避免不必要的支出。它还使提取和分析所学知识成为可能，这可以提供有关资源使用特征的有用信息。所提出的解决方案已使用真实的Wikipedia服务器流量数据在OpenStack上进行了测试。实验结果表明，机器学习可以改善资源使用率。