Customer-oriented diagnosis of memory provisioning for IaaS clouds

R. Pfitscher, M. A. Pillon, R. Obelheiro
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引用次数: 10

Abstract

Infrastructure-as-a-service clouds enable customers to use computing resources in a flexible manner to satisfy their needs, and pay only for the allocated resources. One challenge for IaaS customers is the correct provisioning of their resources. Many users end up underprovisioning, hurting application performance, or overprovisioning, paying for resources that are not really necessary. Memory is an essential resource for any computing system, and is frequently a nperformance-limiting factor in cloud environments. In this work, we propose a model that enables cloud customers to determine whether the memory allocated to their virtual machines is correctly provisioned, underprovisioned, or overprovisioned. The model uses two metrics collected inside a VM, resident and committed memory, and defines thresholds for these metrics that characterize each provisioning level. Experimental results with Linux guests on Xen, running four benchmarks with different workloads and varying memory capacity, show that the model was able to accurately diagnose memory provisioning in 98% of the scenarios evaluated.
面向客户的IaaS云内存配置诊断
基础设施即服务云使客户能够以灵活的方式使用计算资源来满足他们的需求,并且只需为分配的资源付费。IaaS客户面临的一个挑战是正确配置资源。许多用户最终会导致配置不足,影响应用程序性能,或者过度配置,为并不真正必要的资源付费。内存是任何计算系统的基本资源,并且在云环境中经常是一个非性能限制因素。在这项工作中,我们提出了一个模型,该模型使云客户能够确定分配给其虚拟机的内存是配置正确、配置不足还是配置过多。该模型使用在VM中收集的两个指标,驻留内存和提交内存,并为这些指标定义表征每个配置级别的阈值。在Xen上使用Linux客户机运行四个具有不同工作负载和不同内存容量的基准测试的实验结果表明,该模型能够在98%的评估场景中准确诊断内存供应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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