Computer science and Technology, Harbin Institute of Technology, Harbin, China.
Computer science and Technology, Air Force Communication NCO Academy, DaLian, China.
PLoS One. 2019 Apr 26;14(4):e0216067. doi: 10.1371/journal.pone.0216067. eCollection 2019.
Elasticity is the key technique to provisioning resources dynamically in order to flexibly meet the users' demand. Namely, the elasticity is aimed at meeting the demand at any time. However, the aforementioned approaches usually provision virtual machines (VMs) in a coarse-grained manner just by the CPU utilization. Actually, two or more elements are needed for the performance metric, including the CPU and the memory. It is challenging to determine a suitable threshold to efficiently scale the resources up or down. In this paper we present an elastic scaling framework that is implemented by the cloud layer model. First we propose the elastic resource provisioning (ERP) approach on the performance threshold. The proposed threshold is based on the Grey relational analysis (GRA) policy, including the CPU and the memory. Secondly, according to the fixed threshold, we scale up the resources from different granularities, such as in the physical machine level (PM-level) or virtual machine level (VM-level). In contrast, we scale down the resources and shut down the spare machines. Finally, we evaluate the effectiveness of the proposed approach in real workloads. The extensive experiments show that the ERP algorithm performs the elastic strategy efficiently by reducing the overhead and response time.
弹性是为了动态提供资源以灵活满足用户需求的关键技术。也就是说，弹性旨在随时满足需求。然而，上述方法通常通过 CPU 利用率以粗粒度方式仅提供虚拟机 (VM)。实际上，性能指标需要两个或更多元素，包括 CPU 和内存。确定一个合适的阈值来有效地向上或向下扩展资源是具有挑战性的。在本文中，我们提出了一种弹性扩展框架，该框架通过云层模型实现。首先，我们提出了基于性能阈值的弹性资源供应 (ERP) 方法。所提出的阈值基于灰色关联分析 (GRA) 策略，包括 CPU 和内存。其次，根据固定阈值，我们从不同的粒度扩展资源，例如在物理机级别 (PM 级别) 或虚拟机级别 (VM 级别)。相比之下，我们缩小资源并关闭备用机器。最后，我们在实际工作负载中评估了所提出方法的有效性。广泛的实验表明，ERP 算法通过降低开销和响应时间来有效地执行弹性策略。
