基于混合预测的云平台自适应资源分配方法研究

发布时间：2018-03-28 04:12

本文选题：云平台　切入点：资源分配　出处：《哈尔滨工程大学》2014年硕士论文

【摘要】：随着云计算的发展,按需付费的使用模式逐渐成为趋势。在按需付费的模式下,云计算平台为了提高经济效益就必须具备按用户需求自适应资源分配的能力。基于预测的云平台自适应资源分配技术能够根据云平台中应用的历史运行数据对其未来的资源需求做出预测,从而准确的对该应用的资源做出动态调整。目前,对云平台应用资源需求预测的研究主要集中在单一预测模型或者方法上,缺乏对预测样本的分类,导致预测结果不够精确;对自适应资源分配的研究着重于虚拟机自适应资源配置,没有将虚拟机资源调整与虚拟机重置结合起来。据此本文提出一种基于混合预测的云平台自适应资源分配方法,通过混合预测模型和多粒度自适应资源分配,获得更高的云平台资源利用效率并保障用户的 SLA(Service-Level Agreement)。本文首先对当前云平台的资源分配现状进行研究与分析,在其基础上研究了虚拟机放置问题的建模方法和虚拟机重置问题成本分析方法,并且通过对现有预测模型的分析研究,选择了更适合当前云平台应用特点的Markov Chain预测方法和FFT预测方法作为混合预测模型的基础,讨论了这两种算法相结合进行混合资源需求预测的方法。所提出的基于混合预测的云平台自适应资源分配方法,按照应用资源需求变化的周期性特点进行分类,对周期性或非周期性应用采用不同的预测模型;以预测结果为基础,分别采用基于混合预测的虚拟机资源动态分配策略,基于混合预测的虚拟机在线迁移策略,基于混合预测的虚拟机动态重置策略三种策略进行多粒度的云平台自适应资源分配,以有效适应应用需求变化,减少虚拟机迁移的数量,降低违反SLA概率,减少虚拟机占用的物理机数量,最终达到提高云平台系统资源利用效率的目的。最后通过实验证明,基于混合预测的云平台自适应资源分配方法可以有效的进行应用资源需求的预测并自适应资源分配,在提高虚拟机资源利用效率、减少物理机占用和降低SLA方面达到了预期效果。
[Abstract]:With the development of cloud computing, pay-on-demand is becoming a trend. In order to improve economic benefits, cloud computing platform must have the ability of adaptive resource allocation according to user's demand. The technology of adaptive resource allocation in cloud platform based on prediction can be based on the historical running data of cloud platform application. Future resource needs are predicted, At present, the research on cloud platform application resource demand forecasting is mainly focused on a single prediction model or method, the lack of classification of forecasting samples, resulting in the prediction results are not accurate; The research of adaptive resource allocation focuses on the adaptive resource allocation of virtual machine, and does not combine the adjustment of virtual machine resources with the reset of virtual machine. Based on this, an adaptive resource allocation method for cloud platform based on mixed prediction is proposed. Through mixed prediction model and multi-granularity adaptive resource allocation, we can obtain higher resource utilization efficiency of cloud platform and ensure user's SLA(Service-Level agreement. Firstly, this paper studies and analyzes the current situation of resource allocation in cloud platform. On the basis of it, the modeling method of virtual machine placement problem and the cost analysis method of virtual machine reset problem are studied, and the existing prediction models are analyzed and studied. The Markov Chain forecasting method and the FFT forecasting method, which are more suitable for the current cloud platform application characteristics, are selected as the basis of the hybrid prediction model. This paper discusses the method of combining these two algorithms to forecast the demand of mixed resources. The proposed adaptive resource allocation method based on hybrid prediction is classified according to the periodicity of the change of resource demand. Different prediction models are used for periodic or aperiodic applications, based on prediction results, virtual machine resource dynamic allocation strategy based on hybrid prediction and virtual machine online migration strategy based on hybrid prediction are adopted, respectively. The dynamic reset strategy of virtual machine based on mixed prediction is applied to multi-granularity adaptive resource allocation of cloud platform to adapt to the change of application requirements, reduce the number of virtual machine migration, and reduce the probability of violating SLA. Reduce the number of physical machines consumed by virtual machines, and finally achieve the purpose of improving the efficiency of resource utilization of cloud platform system. Finally, through experiments, it is proved that, The adaptive resource allocation method of cloud platform based on hybrid prediction can effectively predict the application resource requirements and adaptively allocate resources, which can improve the efficiency of virtual machine resource utilization. The expected results are achieved in terms of reducing the footprint of physical computers and reducing SLA.
【学位授予单位】：哈尔滨工程大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP393.09

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