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.     

Dynamic resource allocation in cloud download service

Cloud download service, as a new application which downloads the requested content offline and reserves it in cloud storage until users retrieve it, has recently become a trend attracting millions of users in China. In the face of the dilemma between the growth of download requests and the limitation of storage resource, the cloud servers have to design an efficient resource allocation scheme to enhance the utilization of storage as well as to satisfy users' needs like a short download time. When a user's churn behavior is considered as a Markov chain process, it is found that a proper allocation of download speed can optimize the storage resource utilization. Accordingly, two dynamic resource allocation schemes including a speed switching (SS) scheme and a speed increasing (SI) scheme are proposed. Both theoretical analysis and simulation results prove that our schemes can effectively reduce the consumption of storage resource and keep the download time short enough for a good user experience.     

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.     

基于强化学习的虚拟机资源自动配置

虚拟机技术允许多个虚拟机在同一台物理主机上共享资源.为了响应应用需求的变化或者是资源供应的变化,分配到虚拟机上的资源应该能够动态的重新配置.为此,本文提出了一个基于强化学习的算法来自动处理配置进程,即（Standard Reinforcement Learning Auto-Configuration）. 强调了基于算法的模型来解决在资源管理系统的稳定性和适应性问题.这里通过在一个云环境仿真软件CloudSim在基于虚拟机的云测试床实施具有代表性的服务器负载的实验,结果证明了的有效性.这个方法可以在小规模系统里发现最优（或接近最优）的配置策略,并且表现了很好的稳定性和适应性.     

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.     

云计算环境下可信虚拟机管理模型

为了解决云计算环境下虚拟机管理存在的管理域特权过于集中和用户策略易被恶意篡改等问题,提出了一种可信虚拟机管理模型。模型首先对虚拟机管理域进行了细粒度的划分,赋予管理员和用户不同的管理特权,防止管理员随意访问用户的数据;利用可信计算技术建立可信通道分发用户策略,防止管理员恶意篡改用户策略。安全性分析与实验测试表明,该模型可以有效保护用户数据和用户策略的安全性。     

A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing

The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.     

A tree‐based algorithm for virtual infrastructure allocation with joint virtual machine and network requirements

Cloud providers have introduced the on‐demand provisioning of virtual infrastructures (VIs) to deliver virtual networks of computing resources as a service. By combining network and computing virtualization, providers allow traffic isolation between hosted VIs. Taking advantage of this opportunity, tenants have deployed private VIs with application‐optimized network topologies to increase quality of experience of final users. One of the main open challenges in this scenario is the allocation of physical resources to host VIs in accordance with quality of service computing (eg, virtual CPUs and memory) and network requirements (guaranteed bandwidth and specific network topology). Moreover, a VI can be allocated anywhere atop a network datacenter, and because of its NP‐hard complexity, the search for optimal solutions has a limited applicability in cloud providers as requesting users seek an immediate response. The present work proposes an algorithm to accomplish the VI allocation by applying tree‐based heuristics to reduce the search space, performing a joint allocation of computing and network resources. So as to accomplish this goal, the mechanism includes a strategy to convert physical and virtual graphs to trees, which later are pruned by a grouped accounting algorithm. These innovations reduce the number of comparisons required to allocate a VI. Experimental results indicate that the proposed algorithm finds an allocation on feasible time for different cloud scenarios and VI topologies, while maintaining a high acceptance rate and a moderate physical infrastructure fragmentation.     

云计算中的服务可用性保障机制简

提出了一种基于滑动窗口的资源预留SWRR(sliding window based resource reservation)算法,它将预留资源在整个资源池中所占的比例称为窗口。窗口的“滑动”包含2层含义：1）窗口大小动态变化;2）窗口中的资源动态刷新。SWRR已被应用于一个大型的云计算应用平台。实验数据表明,SWRR通过合理资源预留,在兼顾所有任务调度的基础上,可为特定用户提供有效的服务可用性保障。     

基于合作博弈的多虚拟机实时迁移带宽分配机制

IaaS云计算平台采用虚拟机实时迁移技术进行资源动态调度和管理。在实际应用场景下,需要并行实时迁移多个虚拟机。由于实时迁移算法本身以最大利用带宽的方式进行数据传输,存在着迁移进程间竞争带宽的问题,无法保证带宽全局最优分配,影响整体迁移的性能。提出一种基于合作博弈的多虚拟机实时迁移带宽分配机制,将带宽分配问题建模为一个纳什议价,通过求解纳什议价解得到帕累托最优的带宽分配方案,并在实际的虚拟化平台上进行了实现。实验结果表明,相比标准的并行实时迁移,所提出的带宽分配机制能够公平有效地分配带宽,提高了并行实时迁移的性能。     

基于扩展自动机的服务组合静态与动态验证方法

针对目前Web服务组合研究中缺少对定量属性的验证以及在服务运行过程中缺乏对出现异常时的故障处理等问题,提出了一种基于扩展有限自动机的Web服务组合静态与动态验证方法。该方法首先对有限自动机进行扩展,建立了一个可以描述数据及时间等信息的Web服务组合形式化模型;基于该模型,采用计算树逻辑(CTL)描述相关属性,并利用模型检测工具UPPAAL对Web服务组合的行为属性、时间属性以及数据属性等进行了验证;最后结合所建立的诊断模型,给出了一种能够对Web服务组合运行过程中出现异常时进行有效处理的错误诊断算法。     

云计算环境下虚拟机在线迁移优化

在线迁移在云计算平台下已广泛使用,虚拟机内存迁移主要采用的是预拷贝算法.传统预拷贝算法在迭代过程中会将脏页反复重传导致迁移时间较长,针对这一问题文章提出了在拷贝过程中增加脏页预测算法和内存压缩算法相结合的方法.脏页预测算法是采用统计方法,依据内存页历史操作情况对改动频繁的内存页进行标记,根据标记决定是否在最后一轮内存迁移中传输,为了减少迁移传输量,先通过内存压缩算法将其压缩,然后再传输.实验结果表明,改进后的方法有效地降低了停机时间和迁移时间,提高了迁移效率,达到了更快迁移的目的.     

基于云环境下的高性能服务平台研究与应用

云计算的核心部分是平台即服务(Platfromasa Service,Paa S),Paa S是以服务的方式提供计算平台和软件组合。文中提出一种高性能服务平台,介绍了高性能服务平台的体系结构、服务接口、用户与服务管理,重点分析了云调度服务、云统一授权服务、云消息服务的工作方法。     

一种面向云计算环境下虚拟机的威胁建模方法

针对云计算环境中虚拟机平台存在的弱点和漏洞,分析研究了虚拟机可能面临的威胁和攻击,基于STRIDE建模技术构建了云计算环境下虚拟机平台的安全威胁模型。并对威胁发生的可能性和严重程度进行量化,从而进一步评估整个云计算系统面临的安全威胁。     

云计算中虚拟机计算环境安全防护方案

提出了一种虚拟机计算环境的安全防护方案,该方案采用虚拟机内外监控相结合的方式对虚拟机的计算环境进行持续、动态的监控和度量,可对虚拟机进行反馈控制,保障虚拟机计算环境的安全,提升虚拟机的动态适应能力。对比现有安全防护案,该方案充分考虑了云计算中虚拟机效率损耗问题,安全性和执行效率较高,适用于虚拟计算环境。     

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.     

基于云计算的URP系统设计研究

URP(大学资源计划)是建立在信息技术基础上,其信息服务是以一种松散耦合方式出现.在传统意义的URP系统中,各个应用系统都拥有自己的独立数据库与数据结构,通过接口程序与信息平台对接,为客户端用户提供所需的信息服务.而在各个应用系统间,从物理到逻辑上,它们彼此可能是相对孤立的,只是通过URP登录平台集成给客户端用户.这种方式虽然具有很强的灵活性,但是无法控制整个系统的运行效果,且在系统部署和运维时要耗费大量的人力和物力,而基于云计算的URP系统能较好地解决这一难题.     

云桌面在高校中的应用

云桌面融合了企业级服务器与虚拟化平台产品的功能和优势,能够将用户的桌面环境以虚拟机的形式托管到数据中心内的高性能服务器上.管理员可以实现对所有云桌面资源进行可视化的便捷管理,快速地批量部署云桌面满足大量用户的需求等操作.用户可以通过PC、瘦客户机等多种终端使用自己的云桌面,方便地完成日常办公、教学、管理等任务.     

基于非线性服务曲线的RSVP资源分配方式

本文主要针对基于RSVP的保证服务，讨论了如何利用非线性服务曲线进行资源分配，以实现带宽和时延要求的解耦。提出了一种简单有效的非线性服务曲线，数值分析结果表明就资源利用率而言，其性能远远优于传统的线性服务曲线。最后论文讨论了未来的发展趋势并给出了有待研究的一些课题。     

基于CSP的能耗高效云计算资源调度模型与算法

利用约束满足问题对异构云数据中心的能耗优化资源调度问题建模,通过求解建立的约束模型可以获得能耗最优的资源分配方式,并在此基础上提出了能耗优化的资源分配算法dynamicpower (DY)。与已有的算法MinPM、FFD、BFD相比,算法DY考虑了资源的异构性,能够降低云数据中心物理服务器的能耗。最后,利用Choco实现了提出的算法DY,并将DY与MinPM、FFD、BFD进行实验比较,实验结果表明,提出的算法在能耗上有明显优势。