Optimal Cloud Resource Auto-Scaling for Web Applications
Optimal Cloud Resource Auto-Scaling for Web Applications
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DOI：
10.1109/ccgrid.2013.73
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发表时间：
2013-05
期刊：
影响因子：
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通讯作者：
Jing Jiang;Jie Lu;Guangquan Zhang;Guodong Long
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文献类型：
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作者：
Jing Jiang;Jie Lu;Guangquan Zhang;Guodong Long
在按需云环境中，Web应用程序提供商有可能扩大或缩小虚拟资源，以实现经济高效的结果。然而，按使用付费的云业务模式尚未实现真正的弹性和成本效益。为了解决这个问题，我们提出了一种新的云资源自动伸缩计划在虚拟机（VM）的Web应用程序提供商的水平。该方案自动预测Web请求的数量，并发现一个最佳的云资源需求与成本延迟权衡。基于此需求，该方案在每个时间单元重新分配中做出向上或向下或NOP（无操作）的资源缩放决策。我们已经在亚马逊云平台上实现了该方案，并使用三个真实世界的Web日志数据集对其进行了评估。我们的实验结果表明，该方案实现了资源的自动缩放与最佳的成本-延迟权衡，以及低SLA违规。
In the on-demand cloud environment, web application providers have the potential to scale virtual resources up or down to achieve cost-effective outcomes. True elasticity and cost-effectiveness in the pay-per-use cloud business model, however, have not yet been achieved. To address this challenge, we propose a novel cloud resource auto-scaling scheme at the virtual machine (VM) level for web application providers. The scheme automatically predicts the number of web requests and discovers an optimal cloud resource demand with cost-latency trade-off. Based on this demand, the scheme makes a resource scaling decision that is up or down or NOP (no operation) in each time-unit re-allocation. We have implemented the scheme on the Amazon cloud platform and evaluated it using three real-world web log datasets. Our experiment results demonstrate that the proposed scheme achieves resource auto-scaling with an optimal cost-latency trade-off, as well as low SLA violations.