云计算中基于能耗比例模型的虚拟机调度算法

针对资源虚拟化环境中的混合型负载调度问题,提出一种基于能耗比例模型的虚拟机调度算法.该算法利用处理器的"性能计数器"机制来评估各个虚拟机的近期能耗状态,并采用"最近最小能耗比例优先"的策略进行调度.理论分析给出了该算法的有效性证明和相关特性.实验结果显示,当系统面对混合型负载时,基于能耗比例模型的调度算法在"调度偏差"和"相对能效"两方面明显优于现有的虚拟机调度算法.     

虚拟CPU负载预测算法性能评估

准确的虚拟CPU负载预测是提高虚拟机CPU调度性能的重要前提,然而,虚拟机操作系统环境下,虚拟CPU负载预测方法需要尽可能简单、有效.针对虚拟机CPU调度应用场景,以实际CPU负载为研究对象,选取五种简单的时间序列预测算法,详细评估其虚拟CPU负载预测性能,为虚拟机CPU调度的实现提供了研究基础.结果表明,平均移动法、...     

基于改进模拟退火算法的虚拟机调度优化方法

本文将着重分析改进模拟退火算法的虚拟机调度优化方法,了解虚拟及调度中的问题和问题特点,针对虚拟机调度优化进行实验并展开实验对比,旨在更好的提高改进模拟退火算法的虚拟机调度优化水平。     

网络资源的均衡化调度算法研究

针对传统轮询算法对网络资源的均衡化调度存在负载均衡性差、网络资源浪费严重以及资源调度效果差的问题,提出一种新的网络资源均衡化调度算法。基于网络资源的均衡化算法运行过程,设计异构集群的并行计算熵的计算矩阵,实现虚拟机的调度,对调度目标的物理节点进行分析,完成网络资源多线程负载均衡调度。采用基于处理时间的网络资源负载动态均衡算法,对每个物理节点建立负载调度机制,使物理节点按照内部最优调度方式实施均衡调度,实现对网络资源的均衡化调度。实验结果表明,所提算法的调度效率高,且具有较高的负载均衡稳定性,可以减少网络资源的浪费,增强网络资源的调度效果。     

延时触发的虚拟机调度策略研究

虚拟机在不同物理节点间的有效调度是云环境下充分利用服务器资源,降低能耗的有效手段。在对虚拟机调度策略的研究中,传统对负载阈值进行优化的方法有所不足,对于节点负载瞬时峰值所触发的虚拟机迁移未加考虑。这种迁移会导致额外的迁移开销,并增大了服务等级协议(SLA)的违反几率。针对此问题,文中提出了一种延时触发的虚拟机调度策略。仿真实验表明,延时触发策略能够减少虚拟机迁移次数,并降低迁移开销。     

DRS集群虚拟机迁移研究

在VMware虚拟化解决方案中,当客户部署的v Sphere数据中心需要进行维护时,要在不同VMware ESXi主机之间进行虚拟机迁移。若虚拟机没有运行业务,关闭虚拟机并进行虚拟机迁移则相对容易;若虚拟机有正在运行的业务,而且不能关闭,则难以进行虚拟机迁移。因此,重点运用负载均衡与轮询算法,在保证数据业务不受影响与分布式资源调度（distributed resource scheduling, DRS）集群整体性能稳定的情况下,在DRS集群中实现虚拟机迁移。     

基于工作负载预测的虚拟机整合算法

针对云计算环境下满足负载均衡、自动伸缩、绿色节能等需求时所面临的虚拟机(VM)迁移问题,该文设计一种面向云计算基础设施基于工作负载预测的整合调度算法。通过有机结合基于工作负载预测的主动控制技术和基于实际系统状态信息的被动控制技术,并采用指数平滑预测模型预测未来时刻的工作负载情况,提出虚拟机选择阶段最大未来工作负载优先和虚拟机安置阶段比较资源需求队列的虚拟机整合算法。仿真表明,该算法利用基于预测的资源整合方式减少了服务器使用量、虚拟机迁移次数和服务等级协议违例次数,有效提升了以数据中心为核心的云基础设施整体资源利用率。     

云计算环境下虚拟机资源均衡调度方法研究

虚拟机资源是云计算环境下的一类主要云服务资源,有效的资源调度方法对于改善负载均衡和提高资源利用率具有重要意义.针对虚拟机资源的调度问题进行分析,利用任务到达触发任务分配,当任务到达时,首先判断云服务资源池的运行状态,分为饱和与不饱和两种;然后针对当前任务请求下的云服务资源池状态,分别给出资源调度策略,并通过算例分析验证所提方法的有效性.所提虚拟机资源调度方法将有助于云服务资源池中虚拟机资源的均衡利用.     

云环境下基于虚拟机动态迁移的调度策略研究

针对目前云环境资源调度采用静态负载均衡策略易于导致资源浪费的问题,提出了一种双限定值的虚拟机动态迁移的调度策略.该策略将当前负载状况与负载过重或过轻时两个限定值比较,选择介于二者之间能耗较低的虚拟机迁移至目标节点.仿真实验表明,该策略能够减少迁移次数,降低虚拟机迁移能耗,从而尽可能达到负载均衡和满足服务等级协议的需求.     

云存储中一种基于布局的虚拟磁盘节能调度方法

 在云存储中,如何有效地调度用户请求到目标磁盘以实现绿色节能存储是一个热点问题.鉴于云存储对节能调度算法提出的新要求,如请求响应时间敏感与对动态优化的限制等,本文提出了一种基于布局的虚拟磁盘节能调度方法.该方法将磁盘阵列动态划分为工作区与就绪区,以工作区为主向用户分发资源,并以未连接虚拟机的虚拟磁盘为单位,根据实时负载情况对虚拟磁盘布局进行动态优化.实验结果表明,这种方法不仅能够降低磁盘阵列的能耗,而且能够有效地缓解响应时间延长的问题,还能够使虚拟磁盘布局达到更高的负载均衡水平.     

OASM: An overload‐aware workload scheduling method for cloud computing based on biogeographical optimization

With the daily increase in the number of cloud users and the volume of submitted workloads, load balancing (LB) over clouds followed by a reduction in users' response time is emerging as a vital issue. To successfully address the LB problem, we have optimized workload distribution among virtual machines (VMs). This approach consists of two parts: Firstly, a meta‐heuristic method based on biogeographical optimization for workload dispatching among VMs is introduced; secondly, we propose an innovative heuristic algorithm inspired by the “Banker algorithm” that runs in core scheduler to control and avoid VM overloads. The combination of these two (meta‐)heuristic algorithms constitutes an LB approach through which we have been able to reduce the value of the makespan to a reasonable time frame. Moreover, an information base repository (IBR) is introduced to maintain the online processing status of physical machines (PMs) and VMs. In our approach, data stored in IBR are retrieved when needed. This approach is compared with well‐known (non‐)evolutionary approaches, such as round‐robin, max‐min, MGGS, and TBSLB‐PSO. Experimental results reveal that our proposed approach outperforms its counterparts in a heterogeneous environment when the resources are smaller than the workloads. Moreover, the utilization of physical resources gradually increases. Therefore, optimal workload scheduling, as well as the lack of overload occurrence, results in a reduction in makespan.     

In brief: TRECC tackles on-demand computing

As more and more researchers turn to cluster-based systems to handle intensive computing tasks, the need to prioritize those tasks also increases. We are developing system software for scheduling computing cycles on demand. TRECC's approach to secure cluster computing on demand is a matter of managing workflow. TRECC's work in virtualization is in step with one of the hottest trends in cluster computing. Virtualization works by effectively splitting one or more cluster nodes into a greater number of virtual nodes. To do this, the scheduler needs additional information that lets it decide whether a given job can even run effectively under a virtual environment. The virtualization component currently lets researchers start and stop virtual machines on demand and launch as well as successfully run MPI (message passing interface) jobs inside a virtual machine.     

Enhancing virtual environments with QoS aware resource management

Nowadays, the consolidation of application servers is the most common use for current virtualization solutions. Each application server takes the form of a virtual machine (VM) that can be hosted into one physical machine. In a default Xen implementation, the scheduler is configured to handle equally all of the VMs that run on a single machine. As a consequence, the scheduler shares equally all of the available physical CPU resources among the running VMs. However, when the applications that run in the VM dynamically change their resource requirements, a different solution is needed. Furthermore, if the resource usage is associated with service-level agreements, a predefined equal share of the processor power is insufficient for the VMs. Within the Xen’s primitives, even though it is possible to tune the scheduler parameters, there is no tool to achieve the dynamic change of the share of the processor power assigned to each VM. A combination of a number of primitives, however, appears to be suited as a base for achieving this. In this paper, we present an approach to efficiently manage the quality of service (QoS) of virtualized resources in multicore machines. We evaluate different alternatives within Xen for building an enhanced management of virtual CPU resources. We compare these alternatives in terms of performance, flexibility, and ease of use. We devise an architecture to build a high-level service that combines interdomain communication mechanisms with monitoring and control primitives for local resource management. We achieve this by our solution, a local resource manager (LRM), which adjusts the resources needed by each VM according to an agreed QoS. The LRM has been implemented as a prototype and deployed on Xen-virtualized machines. By means of experiments, we show that the implemented management component can meet the service-level objectives even under dynamic conditions by adapting the resources assigned to the virtualized machines according to demand. With the LRM, we therefore achieve both fine-grain resource allocation and efficient assignment.     

基于包簇框架的云计算能耗优化算法

文中针对以虚拟机为中心的云计算分配模式中结构复杂、分配困难等问题,采用了一种基于包簇结构的分配框架。在此基础上提出了一个有效的能耗模型,并将二进制粒子群算法进行改进,通过调节自适应的权重,提高了包簇分配算法的速度和准确性。实验表明,改进的二进制粒子群算法在收敛速度和寻优能力方面更加优于传统的二进制粒子群算法。相较于以虚拟机为中心的分配算法,基于包簇框架下的改进二进制粒子群分配算法提升了CPU使用率,有效降低了能耗,更加绿色节能。     

考虑虚拟机间性能互扰的虚拟资源分配方法

提出了一种考虑虚拟机间性能互扰的虚拟资源分配方法.构建了虚拟机性能互扰度量模型,并给出了基于虚拟机负载模式的性能互扰度预测方法.在此基础上将对虚拟机间性能互扰的预测结果应用于资源分配方案性能互扰评价中,以作为虚拟资源分配的依据,能够有效保证虚拟资源分配方案的有效性.实验结果表明,和已有方法相比,提出的虚拟资源分配方法能够保证虚拟机上所部署应用的执行性能.     

Adaptive optimization of a time-sharing system's performance

The complex task of managing a virtual memory multiprogramming system is considered as one which can be achieved by allowing the operating system to make use of measurement data gathered on-line in the scheduling decisions it has to make. System performance optimization is achieved by continuous monitoring of critical system parameters and workload characteristics and by use of this information in a real-time adaptive feedback control policy. As a specific application of this approach, the maximization of system throughput by the regulation of the degree of multiprogramming in a virtual memory system is examined. The specific form of this performance measure as a function of the number of active processes sharing main memory is used in the design of an adaptive and statistical maximum-seeking algorithm designed to respond to abrupt changes in program locality. The data gathering and smoothing procedures and the optimization policy are then implemented in a simulator of a virtual memory time-sharing system and evaluated in simulation runs with a random and time-varying workload. These experiments are used to tune the various parameters of the algorithm and to demonstrate its ability to maintain the system at an optimal level of performance. Statistical confidence intervals for these simulation runs are given in order to provide a measure of significance to the experiments.     

虚拟机技术在高职计算机网络安全教学中的应用研究

文中针对虚拟机技术在高职计算机网络安全教学中的应用进行研究。为确保研究具有实践性,文中结合文献和相关资料,主要研究了虚拟机技术在高职计算机网络安全教学中的应用现状、应用效果、应用中的问题以及具体应用策略。利用虚拟相关技术进行电脑网络安全教学,可以实现教学质量优化的目标。但是,目前还存在教学模式落后、虚拟机技术衔接不顺畅等问题。因此,解决以上两点问题,对于提升网络安全教学的效果有重要的作用。     

云环境下基于迁移的虚拟机集群优化算法

针对云环境中虚拟机集群负载不均衡问题,提出一种基于虚拟机迁移的集群优化算法。通过对节点负载的实时监测,动态调整各种资源的权重,根据资源权重选择可最大程度降低主机负载的虚拟机进行迁移。该算法利用预测机制,消除主机资源利用率的临时越界引起的不必要的虚拟机迁移。在选择目标节点时,采用多目标决策法,兼顾多资源匹配率,服务级目标违背率(SLA)等多种管理目标。实验结果表明,与同类型的负载均衡算法相比,该算法能减少迁移次数,降低SLA违背率。     

Group-based user pairing for virtual MIMO in LTE

A user pairing method is proposed to improve the throughput gain of virtual multiple input multiple output (virtual MIMO) in 3G long-term evolution (LTE).The approach taken in this study separated the user pairing into four steps 1) the users are divided into two groups according to their signal-to-noise ratio (SNR), and different user pairing metrics are proposed for each group; 2) the scheduler chooses a user for transmitting by a given rule; 3) the scheduler searches the pairing candidate for the selected user in each group; 4) the scheduler chooses the final pairing user from the co-group and crossover-group candidates by using a marginal utility function, to balance fairness and efficiency.The method can improve the throughput of users with high SNR, and guarantee fairness for users with low SNR, so it can be used in 3G LTE systems.The article provides both theoretical analysis and simulation results to support the idea.     

基于KVM的虚拟机Post-Copy动态迁移算法稳定性优化

虚拟机动态迁移技术为虚拟化系统的资源调度提供了强有力的支撑,Post-Copy算法作为虚拟机动态迁移的两个核心算法之一,凭借其总体迁移时间稳定与迁移停机时间短的优点,一直是国内外学者研究的热点问题。对虚拟机的故障容错机制、迁移过程中的内存页面传输方式与缺页错误的关联性,以及QEMU-KVM平台源码进行了深入的研究,提出了基于事务同步的故障容错方法以提升Post-Copy迁移算法的稳定性。试验结果表明,提出的虚拟机Post-Copy迁移优化算法,能保证迁移过程中源端虚拟机故障、目标端虚拟机故障以及网络故障的迅速修复,能通过较小的代价解决稳定性问题,所提出的方法有效地提升了Post-Copy迁移算法的稳定性,也为以后的优化研究方向提供了参考。