Improving Resource Utilisation in the Cloud Environment Using Multivariate Probabilistic Models
Improving Resource Utilisation in the Cloud Environment Using Multivariate Probabilistic Models
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DOI：
10.1109/cloud.2012.66
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发表时间：
2012-06
期刊：
影响因子：
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通讯作者：
Sijin He;Li Guo;M. Ghanem;Yike Guo
中科院分区：
文献类型：
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作者：
Sijin He;Li Guo;M. Ghanem;Yike Guo
在云计算环境中，基于虚拟机（VM）基于虚拟机（VM）的资源提供已被广泛接受和采用。使用静态调度方法在不同的物理机器上分配VM（PMS）导致的关键问题是，资源往往没有被充分利用。尽管已经开发了一些现有的云重新配置算法来解决该问题，但由于忽略了VM和PMS的多维特性，它们通常会导致较高的迁移成本和较低的资源利用。在本文中，我们介绍并评估一种用于改善云提供商资源利用率的新算法。通过使用多元概率模型，我们的算法选择合适的PMS进行VM重新分配，然后将其用于生成重新配置计划。我们还描述了两个可以在算法中使用的启发式指标来捕获VM和PMS的多维特性。通过在实验中结合这两个启发式指标，我们观察到我们的方法将资源利用率提高了约8％的云提供商，例如IC Cloud，它们接受用户定义的VM配置，而对于提供商，例如Amazon EC2（例如Amazon EC2） ，仅提供有限类型的VM配置。
Resource provisioning based on virtual machine (VM) has been widely accepted and adopted in cloud computing environments. A key problem resulting from using static scheduling approaches for allocating VMs on different physical machines (PMs) is that resources tend to be not fully utilised. Although some existing cloud reconfiguration algorithms have been developed to address the problem, they normally result in high migration costs and low resource utilisation due to ignoring the multi-dimensional characteristics of VMs and PMs. In this paper we present and evaluate a new algorithm for improving resource utilisation for cloud providers. By using a multivariate probabilistic model, our algorithm selects suitable PMs for VM re-allocation which are then used to generate a reconfiguration plan. We also describe two heuristics metrics which can be used in the algorithm to capture the multi-dimensional characteristics of VMs and PMs. By combining these two heuristics metrics in our experiments, we observed that our approach improves the resource utilisation level by around 8% for cloud providers, such as IC Cloud, which accept user-defined VM configurations and 14% for providers, such as Amazon EC2, which only provide limited types of VM configurations.