An Adaptive Learning Approach for Efficient Resource Provisioning in Cloud Services
An Adaptive Learning Approach for Efficient Resource Provisioning in Cloud Services
复制标题
云服务中高效资源配置的自适应学习方法
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Cathy H. Xia
中科院分区：
文献类型：
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作者：
Yue Tan;Cathy H. Xia
新兴的云计算服务市场旨在以高质量的互联网提供计算资源作为实用程序。它具有不明的需求，通常是高度不确定的。传统的供应方法要么使需求分布的理想化假设或依赖于对历史数据的广泛离线统计分析。在本文中，我们提出了一种在线自适应学习方法，以解决最佳的资源供应问题。基于云服务的随机损失模型，我们从收入管理的角度提出了供应问题，并提出了一种基于随机梯度的学习算法，该算法可以随着对需求的观察而适应性地调整供应解决方案。我们表明，我们的自适应学习算法可以保证最佳性，并通过模拟证明它们可以迅速适应非平稳需求。
The emerging cloud computing service market aims at delivering computing resources as a utility over the Internet with a high quality. It has evolving unknown demand that is typically highly uncertain. Traditional provisioning methods either make idealized assumption of the demand distribution or rely on extensive offline statistical analysis of historical data. In this paper, we present an online adaptive learning approach to address the optimal resource provisioning problem. Based on a stochastic loss model of the cloud services, we formulate the provisioning problem from a revenue management perspective, and present a stochastic gradient-based learning algorithm that adaptively adjusts the provisioning solution as observations of the demand are continuously made. We show that our adaptive learning algorithm guarantees optimality and demonstrate through simulation that they can adapt quickly to non-stationary demand.