应用托管环境下的虚拟机优化调度方法

在面对云服务中典型的应用托管需求时,现有的基础设施即服务IaaS大多采用应用无关的方式进行虚拟机调度,无法针对类型不同的应用托管需求调整调度策略,从而会产生虚拟机集群的负载倾斜及资源利用率不高的情况,甚至会影响所托管的应用。针对上述问题,提出了一种应用托管环境下的虚拟机优化调度方法,通过对所托管应用的分析和物理资源的监控,以贪心方式实现虚拟机周期性的调度策略。实际项目中的应用托管实例表明,该虚拟机调度方法可有效减少所使用的物理服务器数量,并提高物理服务器资源利用率。     

支持随机服务请求的云虚拟机按需物理资源分配方法

本文针对云平台按负载峰值需求配置处理机资源、提供单一的服务应用和资源需求动态变化导致资源利用率低下的问题,采用云虚拟机中心来同时提供多种服务应用.利用灰色波形预测算法对未来时间段内到达虚拟机的服务请求量进行预测,给出兼顾资源需求和服务优先等级的虚拟机服务效用函数,以最大化物理机的服务效用值为目标,为物理机内的各虚拟机动态配置物理资源.通过同类虚拟机间的全局负载均衡和多次物理机内各虚拟机的物理资源再分配,进一步增加服务请求量较大的相应类型的虚拟机的物理资源分配量.最后,给出了虚拟机中心基于灰色波形预测的按需资源分配算法ODRGWF.模拟实验表明所提算法能够有效提高云平台中处理机的资源利用率,对提高用户请求完成率以及服务质量都具有实际意义.     

基于二次指数平滑预测的虚拟机调度方法研究*

针对数据中心的高能耗问题,提出了一种基于负载感知和预测的虚拟机调度方法,采用二次指数平滑法预测物理主机资源负载情况,利用MMT和MM相结合的策略选择待迁虚拟机,使用资源最佳适配策略（BRF）选择目标物理主机。该调度方法的预测模型能提高迁移触发准确率,随着调度轮数的增加,对资源需求互补的虚拟机会被整合到相同物理主机上,从而减少迁移次数;最后,通过CloudSim仿真平台与FT_MMT、CDLC、AR_MMT调度策略进行了对比,结果表明该调度方法在能耗节约、迁移次数方面均有提升。     

能耗感知下云资源三支粒度调度策略研究

云资源调度是云数据中心的一种重要节能方式。然而,实际云平台中,受单一物理机资源限制,存在虚拟机资源竞争和利用率低的问题。对此,通过分析虚拟机负载相似性及资源占有度问题,提出一种基于三支决策的能耗感知虚拟机迁移策略。首先,在虚拟机迁移过程中,设计云资源的三支划分策略,并使用K-means算法在划分区域选择待迁移的虚拟机序列;其次,依据虚拟机与物理机的负载相似度,获取虚拟机放置顺序;最后,依托CloudSimPlus云仿真平台验证了所提方法的有效性。实验结果表明,所提方法能够有效降低云能耗,实现资源充分利用。     

基于负载特征的虚拟机迁移调度策略

为提高虚拟机迁移时的资源利用率及服务可用性,提出一种基于负载特征的虚拟机迁移调度策略。针对节点的触发类型和虚拟机的负载特征,采用多阈值方式触发迁移,完成对拟迁移的虚拟机以及迁移目标节点的选择。实验结果表明,该策略能够实现虚拟机迁移的自主管理,并能提升资源的使用效率,具有较好的自适应性。     

面向托管的数据库即服务系统及资源优化技术

数据库即服务（DBaaS）是云计算的一个研究热点,而数据应用托管则是当前DBaaS的一个重要应用领域。为满足行业数据应用托管中对DBaaS提出的数据隔离、性能隔离及可靠性保障等方面的要求,提出一种无共享架构下基于虚拟机、支持副本的多租户数据托管方法及相应的数据库即服务系统。针对该系统中面向租户的虚拟机资源（CPU、内存等）动态优化这一核心问题,建立了基于虚拟机的系统资源效用函数和数据库性能计算模型,并在基础上给出了一种根据租户数据请求负载并采用贪心方式的虚拟机资源动态优化算法。结合科技信息服务数据库托管应用示例进行了实验,实验结果表明提出的方法可以根据各个租户的数据库负载动态优化虚拟机的资源分配,能够在满足性能需求同时达到了提高系统资源利用率的目的。     

一种基于网络感知的虚拟机再调度算法

有效的虚拟机调度策略能够提高数据中心的资源利用率,降低运行时能耗.现有调度算法综合考虑了虚拟机在CPU、内存和网络方面的需求,通过合理部署虚拟机,以期最小化计算、存储与网络的代价.然而,在线的虚拟机部署策略较少考虑由于虚拟机退出所造成的资源利用率下降与网络延迟上升的问题.为此,文中深入研究面向网络感知的周期性资源重配置问题,提出了面向网络感知的虚拟机再调度算法,通过适当的虚拟机迁移,提高部署在虚拟机上任务的性能以及数据中心整体的网络通信效率.算法通过尽可能低代价的虚拟机迁移来提高虚拟机之间的网络通信能力,以提升虚拟机组的整体运行效率,并保持物理机占用但不显著提高.作者通过两个测试平台在真实环境中验证了算法的有效性;通过真实的数据集和模拟实验,在多种虚拟机部署算法下,对比了应用虚拟机再调度算法前后虚拟机的部署效果,验证了该算法能够以较小的代价使得高网络通信代价的任务数明显减少,虚拟机组的网络通信能力显著提高.     

云计算架构下的深层次动态资源调配

云计算是当前学术界和工业界都十分关注的热点,被广泛应用于针对海量数据和用户的大规模计算。云计算的特点要求计算机系统能够提供可伸缩的计算能力,而虚拟化技术正是其中的关键层次,在资源管理、服务器整合、提高资源利用率等方面发挥了巨大的作用。通过虚拟化技术,可以实现一个多层次的资源调度机制,以保证高资源利用率和系统性能:首先面向虚拟机的应用特征建立资源预测模型,然后依据预测结果建立资源分配策略,最终通过虚拟机间的资源动态优化技术,实现在同一物理主机或不同物理主机上虚拟机间动态的资源优化使用。这里,不仅要以物理机的宏观资源利用率作为管理依据,更需要关注虚拟机上应用程序在运行过程中的资源需求变化特征,从而为云计算提供一整套的虚拟化资源优化技术及使用方案,从静态部署、动态预测、单机资源动态调配、多机资源动态均衡调度、在线迁移等多个层次为云计算提供全面、有机的支撑。     

Xen虚拟机资源调度优化算法

针对Xen虚拟化平台中虚拟机资源分配不合理的问题,提出了两种资源调度优化算法,即细粒度优化算法和粗粒度优化算法.细粒度优化算法主要解决单个物理节点上虚拟机资源分配不合理问题,能够根据物理节点上运行的各虚拟机的资源利用情况来调整资源分配量,适当增加利用率较高的虚拟机的资源,减少资源利用率低的虚拟机的资源,从而优化资源分配,提高资源利用效率,避免不必要的虚拟机迁移.粗粒度优化算法是针对集群中多个物理节点之间虚拟机负载不均衡问题而提出的.该算法结合粒子群优化技术,选择将集群系统中热点物理机上的部分虚拟机迁移到最适合的冷点物理机上,从而避免高载物理机宕机.实验结果表明,这两种资源调度优化算法能够有效解决虚拟机资源分配不合理的问题,具有较好的适用性和应用前景.     

医疗云平台资源调度策略研究

针对云平台中资源调度策略过于简单,不能有效适应医疗业务需求的问题,分析不同医疗系统对资源的不同需求,以此为根据提出IB-Choose资源调度策略。基于Open Stack平台构建包含医生诊疗系统、实验科检验系统和影像归档系统的医疗云平台,并在该平台上实现IB-Choose策略。实验结果表明,与Open Stack默认资源调度策略Chance相比,IB-Choose可将启动虚拟机的服务时间缩短25%~30%,同时减少云资源开销并提高其利用率。     

Dynamic forecast scheduling algorithm for virtual machine placement in cloud computing environment

In most cloud computing platforms, the virtual machine quotas are seldom changed once initialized, although the current allocated resources are not efficiently utilized. The average utilization of cloud servers in most datacenters can be improved through virtual machine placement optimization. How to dynamically forecast the resource usage becomes a key problem. This paper proposes a scheduling algorithm called virtual machine dynamic forecast scheduling (VM-DFS) to deploy virtual machines in a cloud computing environment. In this algorithm, through analysis of historical memory consumption, the most suitable physical machine can be selected to place a virtual machine according to future consumption forecast. This paper formalizes the virtual machine placement problem as a bin-packing problem, which can be solved by the first-fit decreasing scheme. Through this method, for specific virtual machine requirements of applications, we can minimize the number of physical machines. The VM-DFS algorithm is verified through the CloudSim simulator. Our experiments are carried out on different numbers of virtual machine requests. Through analysis of the experimental results, we find that VM-DFS can save 17.08 % physical machines on the average, which outperforms most of the state-of-the-art systems.     

Reliable self‐deployment of distributed cloud applications

Cloud applications consist of a set of interconnected software elements distributed over several virtual machines, themselves hosted on remote physical servers. Most existing solutions for deploying such applications require human intervention to configure parts of the system, do not conform to functional dependencies among elements that must be respected when starting them, and do not handle virtual machine failures that can occur when deploying an application. This paper presents a self‐deployment protocol that was designed to automatically configure a set of software elements to be deployed on different virtual machines. This protocol works in a decentralized way, that is, there is no need for a centralized server. It also starts the software elements in a certain order, respecting important architectural invariants. This protocol supports virtual machine and network failures and always succeeds in deploying an application when faced with a finite number of failures. Designing such highly parallel management protocols is difficult; therefore, formal modeling techniques and verification tools were used for validation purposes. The protocol was implemented in Java and was used to deploy industrial applications. Copyright © 2016 John Wiley & Sons, Ltd.     

申威架构下的虚拟机访存特征提取方法

虚拟化技术是云服务的重要支柱之一,虚拟化充分扩展了物理资源的灵活性,提升了物理资源的利用率。随着国家信息化水平的发展,云服务器核心技术自主可控、安全高效的要求不断提高。近年来,作为国产服务器的典型代表,申威架构服务器的功能不断完善。提出了申威架构上的虚拟机访存特征提取方法,充分利用了申威架构独特优势,实时测算虚拟机的内存缺失率曲线,并最终计算工作集大小,同时利用热页集机制大幅度减少页面追踪的性能开销。实验结果表明,该方法可以准确计算虚拟机工作集大小,平均误差低于3%,平均性能开销不高于8.3%。本工作为申威虚拟机内存动态分配提供条件,最终目标是提高申威云服务器整体性能和内存利用率。     

Affinity-aware modeling of CPU usage with communicating virtual machines

Use of virtualization in Infrastructure as a Service (IaaS) environments provides benefits to both users and providers: users can make use of resources following a pay-per-use model and negotiate performance guarantees, whereas providers can provide quick, scalable and hardware-fault tolerant service and also utilize resources efficiently and economically. With increased acceptance of virtualization-based systems, an important issue is that of virtual machine migration-enabled consolidation and dynamic resource provisioning. Effective resource provisioning can result in higher gains for users and providers alike. Most hosted applications (for example, web services) are multi-tiered and can benefit from their various tiers being hosted on different virtual machines. These mutually communicating virtual machines may get colocated on the same physical machine or placed on different machines, as part of consolidation and flexible provisioning strategies. In this work, we argue the need for network affinity-awareness in resource provisioning for virtual machines. First, we empirically quantify the change in CPU resource usage due to colocation or dispersion of communicating virtual machines for both Xen and KVM virtualization technologies. Next, we build models based on these empirical measurements to predict the change in CPU utilization when transitioning between colocated and dispersed placements. Due to the modeling process being independent of virtualization technology and specific applications, the resultant model is generic and application-agnostic. Via extensive experimentation, we evaluate the applicability of our models for synthetic and benchmark application workloads. We find that the models have high prediction accuracy — maximum prediction error within 2% absolute CPU usage.     

Energy-aware capacity scaling in virtualized environments with performance guarantees

We investigate the trade-off between performance and power consumption in servers hosting virtual machines running IT services. The performance behavior of such servers is modeled through Generalized Processor Sharing (GPS) queues enhanced with a green speed-scaling mechanism that controls the processing capacity to use depending on the number of active virtual machines. When the number of virtual machines grows large, we show that the stochastic evolution of our model converges to a system of ordinary differential equations for which we derive a closed-form formula for its unique stationary point. This point is a function of the capacity and the shares that characterize the GPS mechanism. It allows us to show that speed-scaling mechanisms can provide large reduction in power consumption having only small performance degradation in terms of the delays experienced in the virtual machines. In addition, we derive the optimal choice for the shares of the GPS discipline, which turns out to be non-trivial. Finally, we show how our asymptotic analysis can be applied to the dimensioning and service partitioning in data-centers. Experimental results show that our asymptotic formulas are accurate even when the number of virtual machines is small.     

云应用资源交付与分裂聚类调度方法

针对私有云环境中资源交付与调度的高效实现问题,提出一种面向云应用的资源交付调度及实现方法。该方法以云应用元模型为核心,根据模型中虚拟机间数据交互特征生成最小生成树,采用图论分裂聚类算法拆分虚拟机集合并映射物理主机。通过对比分析面向虚拟机调度的实验数据,证明该方法能在提升云应用整体性能的同时降低云应用对物理网络设备及带宽资源的占用。     

基于MapReduce虚拟集群的能耗优化算法

随着全球能源危机的出现,许多研究者开始关注数据中心的能耗问题。在满足用户需求的前提下,减少数据中心的活跃节点个数能够有效地降低其能耗。传统的减少活跃节点的方式是虚拟机迁移,但虚拟机迁移会造成极大的系统开销。提出一种基于MapReduce虚拟集群的能耗优化算法--在线时间平衡算法OTBA,能够减少活跃物理节点数,有效降低数据中心的能耗,并且避免了虚拟机的迁移。通过建立云数据中心的能耗模型、用户提交服务的排队模型和评价作业完成质量的作业运行模型,确定了数据中心节能模型的目标函数和变量因子。在线时间平衡算法是基于虚拟云环境和在线MapReduce作业的一种节能调度算法,能够在虚拟机的生命周期和资源利用率之间做出权衡,使数据中心激活的服务器达到最少,能耗降到最低。此外,该结果通过仿真和Hadoop平台上的实验得到了验证。     

Profile-based dynamic application assignment with a repairing genetic algorithm for greener data centers

Data centers have become essential to modern society by catering to increasing number of Internet users and technologies. This results in significant challenges in terms of escalating energy consumption. Research on green initiatives that reduce energy consumption while maintaining performance levels is exigent for data centers. However, energy efficiency and resource utilization are conflicting in general. Thus, it is imperative to develop an application assignment strategy that maintains a trade-off between energy and quality of service. To address this problem, a profile-based dynamic energy management framework is presented in this paper for dynamic application assignment to virtual machines (VMs). It estimates application finishing times and addresses real-time issues in application resource provisioning. The framework implements a dynamic assignment strategy by a repairing genetic algorithm (RGA), which employs realistic profiles of applications, virtual machines and physical servers. The RGA is integrated into a three-layer energy management system incorporating VM placement to derive actual energy savings. Experiments are conducted to demonstrate the effectiveness of the dynamic approach to application management. The dynamic approach produces up to 48% better energy savings than existing application assignment approaches under investigated scenarios. It also performs better than the static application management approach with 10% higher resource utilization efficiency and lower degree of imbalance.     

双层虚拟化云架构下任务调度能耗优化算法

虚拟机上部署容器的双层虚拟化云架构在云数据中心中的使用越来越广泛。为了解决该架构下云数据中心的能耗问题,提出了一种工作流任务调度算法TUMS-RTC。针对有截止时间约束的并行工作流,算法将调度过程划分为时间利用率最大化调度和运行时间压缩两个阶段。时间利用率最大化调度通过充分使用给定的时间范围减少完成工作流所需的虚拟机和服务器数量;运行时间压缩阶段通过压缩虚拟机空闲时间以缩短虚拟机和服务器的工作时间,最终达到降低能耗的目标。使用大量特征可控的随机工作流对TUMS-RTC算法的性能进行了测试。实验结果表明,TUMS-RTC算法相较于对比算法有更高的资源利用率,虚拟机数量减少率和能耗节省率,并且可以很好地处理云计算中规模大且并行度高的工作流。     

基于资源特征匹配的虚拟机放置算法

针对目前数据中心的资源低效利用问题,提出了一种基于资源消耗特征匹配的虚拟机放置算法VMP-RUFM (virtual machines placement algorithm based on resource utilization feature-matching).算法在虚拟机应用的性能表现和访问模式两个层面上,建立虚拟机资源特征模型,进而选择资源消耗特征与物理机资源配置相匹配的虚拟机集合.实验结果表明,该算法对满足条件的虚拟机进行关联后,能够显著优化虚拟机整体资源消耗和对应物理机资源配置的匹配程度.