云数据中心的虚拟机放置问题

总结了虚拟机放置所面临的算法、优化目标、算法评估、基准模型及验证工具等关键问题,阐述了虚拟机放置问题的典型算法,深入分析了骨干互联网中虚拟机放置问题的特点及对云数据中心工程实践的借鉴意义,并从算法和工程相互适配的角度,对云数据中心设计、运营中所涉及的监控和计量、业务模型、SLA和资费设计、云资源池设计、资源池规模和架构、业务分区和迁移控制的设计、资源池的资源均衡性、逻辑网络和物理网络基础功能设计及可能的创新等关键问题提出了若干重要原则。     

面向云数据中心的能耗管理

云计算作为一种新的分布计算技术,变得越来越流行,正快速改变不同应用的计算环境.在云数据中心中,电力的消耗越来越严重,随着全球能源的涨价,数据中心的能耗成本也在增加.云计算的一个主要目标是通过规模经济为用户提供省钱的服务,而这些云服务的能耗成本已经非常显著,因此,如何提高整个云平台的能耗效率变得越来越重要.本文分析了云计算中心能耗的组成,对云数据中心能耗管理的相关研究进行分类、分析与总结,最后,对未来的研究发展趋势提出观点.     

云数据中心高能效的虚拟机迁移整合算法研究

基于多资源能效模型,通过改进CPU双阈值法提出了多资源双阈值法触发虚拟机迁移,并将基于粒子群算法的虚拟机放置算法应用于虚拟机的能效整合.仿真实验结果表明,与传统的启发式算法相比,该算法有效地减少了物理节点的启用数量和虚拟机迁移次数,使系统资源利用率更加均衡.     

基于云技术的数据中心技术研究分析

云技术是一新计算模式,就目前来看,基于云技术的运用存在两个弊端,一是数据安全问题,包括数据不被丢失和不会泄漏;二是网络延迟或者中断。文章就针对基于云技术的数据中心进行分析。     

一种通信距离最小化的虚拟机分配算法

为了解决云资源分配过程中虚拟机通信距离较大,造成用户计算任务完成时间延长问题,提出一种最短通信距离的虚拟机分配算法.云资源管理器能够根据用户指定的虚拟机条件,将计算任务分割到合适的数据中心及其内部服务器,大大缩短了虚拟机之间的通信距离.仿真实验表明,与现有的贪婪算法和随机方法相比,提出的方法通信量更少,执行速度更快.     

基于云计算技术的数据中心虚拟机管理平台的设计研究

当前随着社会经济科技的发展,计算机网络技术在社会生活中的应用范围也越来越广泛。而云计算技术作为计算机网络技术的重要组成部分,已经成为人们不断应用信息技术推动人类生活智能化发展的不可或缺的推动力。目前,数字化时代已经到来,而运用虚拟化手段提高管理效率也已经逐步在当前社会中应用起来。文章首先介绍了云计算,然后在此基础上进一步介绍了基于云计算技术的数据中心虚拟机管理平台,其次阐述了数据中心虚拟机管理系统的设计与搭建,最后进一步阐释了基于云计算技术的数据中心虚拟机管理系统操作流程,包括管理员操作流程以及用户操作流程,以期能够为提高管理效率提供参考。     

基于SDN的云数据中心

移动互联网、云计算等新型业务的高速增长,一方面促使传统的IDC向云计算数据中心转变,另一方面越来越多的应用和数据集中到云端,使得云数据中心的规模急剧增长,传统网络架构和技术已经难以适应云数据中心的发展,如多租户网络隔离、地址复用、VPC(虚拟专有云)、东西向流量控制、QoS和安全控制、自动化网络配置以及大二层网络需求等。SDN作为近期兴起的一种新型网络技术,被认为是解决云数据中心诸多网络问题的重要手段。首先分析云计算引入对数据中心网络带来的主要挑战和问题,然后提出解决这些问题的思路;在比较几种业界主流SDN技术方案后,提出了基于overlay SDN的云数据中心网络解决方案,并深入分析了其实现原理;最后探讨了云数据中心网络的发展趋势。     

云数据中心虚拟资源管理系统研究

云数据中心作为数据中心服务的演进,给数据中心产业带来按需使用,按量计费的全新软硬件的资源出租模式。由于数据中心规模的扩大和功能的多样性,随之出现的问题是数据中心的可靠性以及维护管理的巨大成本。对云数据中心虚拟资源系统进行研究,通过云数据中心资源整合,物理资源虚拟化,实现资源的统一、弹性调配,进而实现云数据中心的高效及低成本运营。     

大庆油田云数据中心建设思路

介绍了云计算的特点、优势及发展趋势,重点阐述大庆油田信息化现状,建设大庆油田云数据中心的思路,详细阐述了建设目标及总体思路、系统架构、技术方案,最后对云计算给大庆油田带来的效益进行了总结。     

探索云数据中心在证券行业的网上证券中的应用

对券商的网上证券业务来说,云数据中心除了可以提供类似IDC托管机房的互联网接入、主机托管等传统服务外,还可以提供虚拟化主机及云计算、大数据处理等高科技服务;网上证券系统需要从海量的结构化、非结构化数据源中筛取并形成满足客户需求的成品数据：网上证券的量化资讯系统需要大数据处理,特色交易系统需要云计算,行情系统需要虚拟主机;具有云计算、大数据处理的云数据中心在网上证券的应用,不但可以快速、高效满足客户需求,通过服务外包,还有利于券商优化管理、节约成本,让券商专注于核心业务的开拓发展。     

Toward a hierarchical and architecture‐based virtual machine allocation in cloud data centers

Number of cloud data centers which consists of hundreds of hosts has increased tremendously around the world due to increase in demands for cloud services. It is expected energy consumption of data centers will reach 139.8 billion Kwh by 2020. Many algorithms are proposed to reduce energy consumption as well as service level agreement violationby minimizing the number of active hosts. Current proposed algorithms do not consider data center architecture, the physical position of hosts, and energy consumption of numerous switches that are in data centers. In this paper, a novel hierarchical cloud resource management is proposed that not only minimizes the number of hosts but also aggregates virtual machines on a limited subset of data center racks and modules to minimize energy consumption. Experimental results with Cloudsim show that our proposed algorithm reduces energy consumption up to 26% and service level agreement violation up to 96%.     

Multi-service differentiated traffic management optimization strategy in cloud data center

In order to cope with the traffic management for multi-service differentiated in cloud data centers,improving network performance and service experience,the multi-service differentiated (MSD) traffic management model was designed that can suit operational requirements in cloud data center.Fibonacci tree optimization (FTO) algorithm was improved according to the MSD model.MSD-FTO traffic management strategy was proposed in SDN cloud data center.Simulation results show that the strategy takes advantage of FTO global optimization ability and multi-modal adaptive performance.Through the global local alternating optimization of the algorithm,differentiation traffic management schemes are obtained as needed,the problem of multi-services differentiated traffic management is solved in operator cloud data center that improve network performance and service experience in cloud data center effectively.     

一种数据中心网络虚拟机快速在线迁移算法

数据中心网络中,虚拟机在线迁移需要在网络核心链路上完成大量的数据传输,造成虚拟机承载的网络应用及其他应用性能下降.在继承现有相同内存页重传避免方法的基础上,引进带链表的计数型布隆过滤器查找结构,避免了内存页查找的假阳性问题.进一步提出了最大化剪枝算法,实现链表长度的最大化缩减,加速查找匹配过程,完成数据中心网络中机架级的虚拟机快速在线迁移.实验结果表明,该方法比现有方法的数据传输量更低,迁移时间更短,降低了迁移对网络应用性能造成的影响.     

IaaS 环境下改进能源效率和网络性能的虚拟机放置方法简

现在的虚拟机放置研究大多集中在物理服务器能源能耗或网络设备能耗的优化,然而随着这些资源的过度聚合,有可能会带来应用性能的下降。提出了一种虚拟机放置方案,主要有2个目的：最小化激活物理机和网络设备的个数来减少数据中心能源消耗;最小化最大链路利用率来改善网络性能。此方案在优化网络性能的同时,减少物理服务器和网络设备的能耗,使得能源效率与网络性能达到平衡。设计了一种新的二阶段启发式算法来求解,首先,利用基于最小割的层次聚类算法与最佳适应算法相结合来优化能源效率,然后,利用局部搜索算法再次优化虚拟机位置来最小化最大链路利用率。仿真实验结果表明,所提方案取得了良好的效果。     

添加热监控电路降低数据中心的能耗

所面临的挑战：降低数据中心的能耗 据EPA统计,2006年数据中心的能源消耗占整个美国电力消耗的1．5％（610×108kWh）。令人吃惊的是,IT设备本身的消耗只占该项电力消耗的一半;电源和致冷设备的能耗占用了另一半。然而,到目前为止,人们仍将减小数据中心能耗的注意力集中在问题的第一部分：IT设备中芯片和元器件的效率。为了有效降低数据中心的整体能耗,当务之急是降低致冷设备的能耗。     

数据中心能效优化策略研究

数据中心数量及规模的增长使得数据中心能耗问题愈发凸显,多国政府针对数据中心能耗问题制定了相应的政策、法规,以促进数据中心节能。对数据中心能耗构成及能效指标进行了研究,从供配电系统、制冷系统及IT设备3个方面对数据中心能效影响因素进行了分析,然后对当前主流的数据中心能效优化策略进行了总结;在此基础上,提出了数据中心即服务的能效优化策略,该能效优化策略充分考察了数据中心服务性能和能耗的匹配性,能够有效实现数据中心全局能效优化。     

A prediction‐based and power‐aware virtual machine allocation algorithm in three‐tier cloud data centers

With the increasing popularity of cloud computing services, the more number of cloud data centers are constructed over the globe. This makes the power consumption of cloud data center elements as a big challenge. Hereby, several software and hardware approaches have been proposed to handle this issue. However, this problem has not been optimally solved yet. In this paper, we propose an online cloud resource management with live migration of virtual machines (VMs) to reduce power consumption. To do so, a prediction‐based and power‐aware virtual machine allocation algorithm is proposed. Also, we present a three‐tier framework for energy‐efficient resource management in cloud data centers. Experimental results indicate that the proposed solution reduces the power consumption; at the same time, service‐level agreement violation (SLAV) is also improved.     

Correlation-based virtual machine migration in dynamic cloud environments

Virtual machine (VM) migration enables flexible and efficient resource management in modern data centers. Although various VM migration algorithms have been proposed to improve the utilization of physical resources in data centers, they generally focus on how to select VMs to be migrated only according to their resource requirements and ignore the relationship between the VMs and servers with respect to their varying resource usage as well as the time at which the VMs should be migrated. This may dramatically degrade the algorithm performance and increase the operating and the capital cost when the resource requirements of the VMs change dynamically over time. In this paper, we propose an integrated VM migration strategy to jointly consider and address these issues. First, we establish a service level agreement-based soft migration mechanism to significantly reduce the number of VM migrations. Then, we develop two algorithms to solve the VM and server selection issues, in which the correlation between the VMs and the servers is used to identify the appropriate VMs to be migrated and the destination servers for them. The experimental results obtained from extensive simulations show the effectiveness of the proposed algorithms compared to traditional schemes in terms of the rate of resource usage, the operating cost and the capital cost.     

A learning‐based approach for virtual machine placement in cloud data centers

In recent years, the increasing use of cloud services has led to the growth and importance of developing cloud data centers. One of the challenging issues in the cloud environments is high energy consumption in data centers, which has been ignored in the corporate competition for developing cloud data centers. The most important problems of using large cloud data centers are high energy costs and greenhouse gas emission. So, researchers are now struggling to find an effective approach to decreasing energy consumption in cloud data centers. One of the preferred techniques for reducing energy consumption is the virtual machines (VMs) placement. In this paper, we present a VM allocation algorithm to reduce energy consumption and Service Level Agreement Violation (SLAV). The proposed algorithm is based on best‐fit decreasing algorithm, which uses learning automata theory, correlation coefficient, and ensemble prediction algorithm to make better decisions in VM allocation. The experimental results indicated improvement regarding energy consumption and SLAV, compared with well‐familiar baseline VM allocation algorithms.     

Study on virtual resource allocation optimization in cloud computing environment

Most existing researches of resource allocation in data center did not take into full consideration how to decrease energy consumption.The energy efficiency virtual resource allocation for cloud computing as a multi-objective optimization problem was formulated,which was then solved by intelligent optimization algorithm.The simulation results reveal that the strategy can successfully generate schedule scheme of different numbers of servers-VM with diverse characteristics and decrease the total operating energy of data center effectively.