Automated and dynamic application accuracy management and resource provisioning in a cloud environment

S. Vijayakumar, Qian Zhu, G. Agrawal
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引用次数: 10

Abstract

The recent emergence of cloud computing is making the vision of utility computing realizable, i.e., computing resources and services from a cloud can be delivered, utilized, and paid for in the same fashion as utilities like water or electricity. This, however, creates new resource provisioning problems. Because of the pay-as-you-go model, resource provisioning should be performed carefully. Resource provisioning can be particularly challenging for adaptive applications, where there can be a tradeoff between the application Quality of Service (QoS), or accuracy, and the resource costs incurred. In this paper, we consider adaptive streaming applications where a user wants to achieve the minimum resource costs while maintaining a specified accuracy goal. We present a dynamic and automated framework which can adapt the adaptive parameters to meet the specific accuracy goal, and then dynamically converge to near-optimal resource allocation. Our solution can handle unexpected changes in the data distribution characteristics and/or rates. We evaluate our approach using two streaming applications and demonstrate the effectiveness of our framework.
云环境中的自动化和动态应用程序准确性管理和资源配置
最近云计算的出现使效用计算的愿景成为可能,也就是说,来自云的计算资源和服务可以像水电等公用事业一样交付、利用和付费。然而,这产生了新的资源供应问题。由于采用了现收现付模型,所以应该谨慎地执行资源供应。对于自适应应用程序来说,资源供应尤其具有挑战性,因为在应用程序服务质量(QoS)或准确性与所产生的资源成本之间可能存在权衡。在本文中,我们考虑自适应流应用程序,其中用户希望在保持指定精度目标的同时实现最小的资源成本。提出了一种动态自动化的框架,该框架可以根据特定的精度目标调整自适应参数,然后动态收敛到接近最优的资源分配。我们的解决方案可以处理数据分布特征和/或速率的意外变化。我们使用两个流应用程序来评估我们的方法,并演示我们框架的有效性。
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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