当前位置: X-MOL 学术J. Supercomput. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Prediction-based underutilized and destination host selection approaches for energy-efficient dynamic VM consolidation in data centers
The Journal of Supercomputing ( IF 3.3 ) Pub Date : 2020-03-16 , DOI: 10.1007/s11227-020-03248-4
Kawsar Haghshenas , Siamak Mohammadi

Improving the energy efficiency while guaranteeing quality of services (QoS) is one of the main challenges of efficient resource management of large-scale data centers. Dynamic virtual machine (VM) consolidation is a promising approach that aims to reduce the energy consumption by reallocating VMs to hosts dynamically. Previous works mostly have considered only the current utilization of resources in the dynamic VM consolidation procedure, which imposes unnecessary migrations and host power mode transitions. Moreover, they select the destinations of VM migrations with conservative approaches to keep the service-level agreements , which is not in line with packing VMs on fewer physical hosts. In this paper, we propose a regression-based approach that predicts the resource utilization of the VMs and hosts based on their historical data and uses the predictions in different problems of the whole process. Predicting future utilization provides the opportunity of selecting the host with higher utilization for the destination of a VM migration, which leads to a better VMs placement from the viewpoint of VM consolidation. Results show that our proposed approach reduces the energy consumption of the modeled data center by up to 38% compared to other works in the area, guaranteeing the same QoS. Moreover, the results show a better scalability than all other approaches. Our proposed approach improves the energy efficiency even for the largest simulated benchmarks and takes less than 5% time overhead to execute for a data center with 7600 physical hosts.

中文翻译:

用于数据中心节能动态虚拟机整合的基于预测的未充分利用和目标主机选择方法

在保证服务质量 (QoS) 的同时提高能效是大规模数据中心高效资源管理的主要挑战之一。动态虚拟机 (VM) 整合是一种很有前景的方法,旨在通过动态地将 VM 重新分配到主机来降低能耗。以前的工作大多只考虑了动态 VM 整合过程中的当前资源利用率,这会强加不必要的迁移和主机电源模式转换。此外,他们采用保守的方法选择 VM 迁移的目的地以保持服务级别协议,这与在较少的物理主机上打包 VM 不符。在本文中,我们提出了一种基于回归的方法,它根据历史数据预测 VM 和主机的资源利用率,并在整个过程的不同问题中使用这些预测。预测未来的利用率提供了选择具有更高利用率的主机作为 VM 迁移目标的机会,从 VM 整合的角度来看,这会导致更好的 VM 放置。结果表明,与该地区的其他工作相比,我们提出的方法将建模数据中心的能耗降低了 38%,同时保证了相同的 QoS。此外,结果显示出比所有其他方法更好的可扩展性。我们提出的方法即使在最大的模拟基准测试中也能提高能效,并且在拥有 7600 台物理主机的数据中心执行所需的时间开销不到 5%。
更新日期:2020-03-16
down
wechat
bug