虚拟化数据中心能耗管理自适应系统的设计

发布时间：2018-04-18 14:04

本文选题：数据中心 + 虚拟化　；参考：《上海交通大学》2013年硕士论文

【摘要】：随着云计算和虚拟化的兴起和发展，数据中心在保证应用的服务质量的同时，能耗的管理也越来越受到重视。然而由于应用的复杂性增强，负载的动态变化性增多，能够在保证数据中心服务质量的同时，自适应地调节数据中心的资源分配，并进一步的降低数据中心能耗的系统还有待进一步的研究。本文分析了现有的虚拟化数据中心能耗管理的技术和方法，提出了基于智能控制理论进化控制算法的数据中心能耗管理的自适应控制系统。系统由资源监测器、资源协调器和资源分配器组成。本文的主要创新点包括以下三点： 1）系统能够实现能耗的自适应的管理。能够根据负载的动态变化进行资源的动态分配。系统能够实现能耗自适应管理的前提是能够进行能耗和性能的监测以及精度较高的在线预测。监测由资源监测器实现，资源监测器负责监测物理集群的整体能耗和每个虚拟机的性能，反馈给模型预估器进行预估。模型预估器采用微粒蚁群算法对物理集群的能耗和虚拟机资源分配之间的模型以及虚拟机的吞吐量和虚拟机资源分配之间的模型进行在线辨识，并把辨识的能耗和性能模型的结果反馈给资源优化器。 2）系统能够实现能耗和性能的统一管理。在保证数据中心服务质量的前提下，进一步地降低能耗。系统能耗的降低主要通过资源优化器进行。资源优化器按照预估模型，采用遗传算法对关于能耗和性能的目的效用函数进行优化，得到分配给每个虚拟机的资源优化结果，然后通过Xen虚拟机管理器进行资源的分配，以此来实现能耗和性能的统一管理。 3）系统具有较高的可扩展性。系统分别使用微粒蚁群算法和遗传算法进行模型的建模和优化，由于智能控制理论算法在模型求解过程中，不需要考虑模型的具体特性，使得该方法能够适应复杂的非线性系统和模型。同时，在算法的实现过程中，有多个参数可供设定和调节，适用于约束条件较多的模型，提高了系统的改进度和可扩展性。本文为了对系统的有效性和稳定性进行验证，搭建了基于Xen的测试平台，使用TPC-W进行负载的生成。通过对实验结果的分析，本系统能够保证应用的服务质量，自适应地进行数据中心虚拟机的资源的分配，同时带来了一定的能耗节约。
[Abstract]:With the rise and development of cloud computing and virtualization, data centers pay more and more attention to the management of energy consumption while ensuring the service quality of applications.However, due to the complexity of the application and the increasing dynamic variation of the load, the resource allocation of the data center can be adjusted adaptively while ensuring the quality of service in the data center.And the system of further reducing the energy consumption of data center still needs further research.This paper analyzes the existing technologies and methods of virtual data center energy management, and proposes an adaptive control system for data center energy management based on intelligent control theory evolutionary control algorithm.The system consists of resource monitor, resource coordinator and resource allocator.The main innovations of this paper are as follows:1) the system can realize adaptive management of energy consumption.The dynamic allocation of resources can be made according to the dynamic change of load.The premise that the system can realize adaptive management of energy consumption is to monitor energy consumption and performance and to predict on line with high precision.The monitoring is implemented by a resource monitor, which is responsible for monitoring the overall energy consumption of the physical cluster and the performance of each virtual machine, and feedbacks to the model predictor for prediction.The model predictor uses particle ant colony algorithm to identify the model between the energy consumption of physical cluster and the allocation of virtual machine resources, and the model between throughput of virtual machine and resource allocation of virtual machine.The results of the identified energy consumption and performance models are fed back to the resource optimizer.2) the system can realize unified management of energy consumption and performance.Under the premise of ensuring the service quality of the data center, the energy consumption is further reduced.The reduction of system energy consumption is mainly carried out by the resource optimizer.According to the prediction model, the resource optimizer uses genetic algorithm to optimize the purpose utility function about energy consumption and performance, and obtains the optimization results of resources allocated to each virtual machine, and then allocates the resources through the Xen virtual machine manager.To achieve the unified management of energy consumption and performance.3) the system has high expansibility.The system uses particle ant colony algorithm and genetic algorithm to model and optimize the model, because the intelligent control theory algorithm does not need to consider the specific characteristics of the model in the process of solving the model.The method can adapt to complex nonlinear systems and models.At the same time, in the implementation of the algorithm, there are many parameters to be set and adjusted, which is suitable for the model with more constraints, and improves the improvement and expansibility of the system.In order to verify the validity and stability of the system, a test platform based on Xen is built and the load is generated by TPC-W.Through the analysis of the experimental results, the system can guarantee the quality of service of the application, adaptively allocate the resources of the virtual machine in the data center, and at the same time bring about some energy saving.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP308

【参考文献】