• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Organizational and End User Computing >Balance Resource Utilization (BRU) Approach for the Dynamic Load Balancing in Cloud Environment by Using AR Prediction Model
【24h】

Balance Resource Utilization (BRU) Approach for the Dynamic Load Balancing in Cloud Environment by Using AR Prediction Model

机译：利用AR预测模型的云环境中动态负载平衡的平衡资源利用（BRU）方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

One of the major challenges for the cloud provider is the efficient utilization of the physical resources. To achieve this, this paper proposed a Balance Resource Utilization (BRU) approach that not only minimizes the resource leakage but also increases the resource utilization and optimize the system performance. The proposed approach consider two resources i.e., CPU and memory, as decision metrics for load balancing and use three thresholds named lower threshold, upper threshold and warning threshold to define underloaded, overloaded and warning situations, respectively. The main concept of this approach is to place VM to the PM, where resource requirement of the VM and resource utilization of the PM are complements to each other. To evade unnecessary migrations due to the temporary peak load AR time series prediction model is used. The authors' approach treats load balancing problem from the practical perspective and implemented in OpenStack cloud with KVM hypervisor. Moreover, proposed approach resolve the issue of VM migration in the heterogeneous environment.
机译：云提供商面临的主要挑战之一是有效利用物理资源。为此，本文提出了一种平衡资源利用（BRU）方法，该方法不仅可以最大程度地减少资源泄漏，还可以提高资源利用率并优化系统性能。提出的方法考虑了两个资源，即CPU和内存，作为负载均衡的决策指标，并分别使用三个阈值分别称为下阈值，上阈值和警告阈值来定义欠载，过载和警告情况。这种方法的主要概念是将VM放置到PM，其中VM的资源需求和PM的资源利用率是相互补充的。为了避免由于临时峰值负载而造成的不必要迁移，使用了AR时间序列预测模型。作者的方法从实际角度处理了负载平衡问题，并在带有KVM虚拟机管理程序的OpenStack云中实现。此外，提出的方法解决了异构环境中VM迁移的问题。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号