基于容器技术的云计算资源合理调度方法研究

传统基于Hypervisor模型的云计算资源调度方法存在长时间得不到调度,调度性能低的问题。针对该问题,设计基于容器技术的云计算资源合理调度方法,设计了调度系统的架构以及调度流程。详细说明了虚拟机迁移时间判断流程以及被迁移虚拟机选择流程。采用Migrate方法完成虚拟机的迁移,资源统计过程通过调用Libvirt的接口实现通信,并通过近似的方式运算虚拟机CPU使用率,降低了云计算资源调度时的数据中心能耗。经过测试表明,所提方法稳定性高,总体性能优,达到了预期目标。     

基于K-means和蝙蝠算法的云计算虚拟机智能调度

针对云计算虚拟机调度中存在的资源分配不均衡问题,提出了一种基于K-means和蝙蝠算法的云计算虚拟机智能调度方法。该方法充分考虑物理节点空闲资源和虚拟机所需资源的互补性,以物理节点作为初始聚类中心,使用资源的相关性定义二者的距离,利用蝙蝠算法的全局寻优能力迭代寻优,达到合理调度虚拟机的目的。模拟实验仿真的结果表明,该方法在降低物理节点数量和提高资源利用率方面具有一定的优势,是一种可行的方法。     

基于改进蚁群算法的虚拟机放置方法研究

针对云计算资源调度中虚拟机到物理机上的部署问题,提出了基于剩余资源控制阈值和匹配度函数的虚拟机放置模型,该模型采用三重因子的目标函数,为剩余资源总量、新开物理机数量及剩余资源标准差提供了约束;为求解该模型,提出了基于大请求先安置原则的改进蚁群算法,并对算法的及参数进行了改进。仿真实验表明和其它几种算法比较,改进蚁群算法有更好的收敛性和更强的寻优能力;此外,实验结果也表明该放置模型能有效提高资源利用率,降低能耗。     

K均值聚类在云计算的应用

相对于传统的应用部署方式,云计算是基于互联网的一种并行处理技术,提供了一个高度可扩展和按需处理的服务。任务调度一直是云计算环境中的研究热点,在云计算环境中具有重要作用。能否合理分配任务到虚拟机资源上是重要问题之一。本文通过对任务请求的资源进行分析,对不同类型的任务进行聚类,将不同类型任务通过改进贪心调度算法合理分配到虚拟机资源上。通过Cloudsim平台模拟实验表明,该算法相对于Min-Min算法在节省能耗方面有较好的效果。     

基于虚拟机聚合的云平台高能效资源调度框架

本文面向实际生产的云计算环境,提出一种基于虚拟机聚合的云平台高能效资源调度框架。首先监测和预测虚拟机资源使用情况,而后应用高效的虚拟机迁移技术进行虚拟机聚合,通过降低物理主机过载概率以及有效减少活动物理主机数量,实现高效地调配云平台资源,从而达到优化云平台性能和节约能源消耗的目标。     

一种均衡物理资源和网络带宽的虚拟机部署方法研究

随着互联网的发展,云计算的发展越来越快,云资源管理平台也受到了越来越多的关注。云计算资源管理系统是云计算体系的"大脑",具有重要的研究价值。针对目前虚拟机部署未能均衡考虑能耗与网络流量的问题,文章设计了一种结合能耗和网络均衡优化的虚拟机部署算法。该算法采用K-均值聚类,利用改进的皮尔逊相关系数进行相似度计算,最终为虚拟机在所有的候选宿主机中选择合适的宿主机。     

基于改进混合遗传算法的云资源调度算法

在云计算中,系统规模和虚拟机迁移数量都是十分庞大的,需要高效的调度策略对其进行优化。将云计算的任务分配抽象为背包求解问题,可通过遗传算法进行求解。传统的遗传算法具有局部搜索能力差以及早熟现象的缺点,本文采用遗传和贪婪相结合的混合遗传算法。针对混合遗传算法在资源利用率与能源消耗的收敛速度较慢问题,本文通过改进适应度函数,改变了适应度函数在不同染色体间的差异度,从而提高了染色体在选择算子中的择优性能。仿真结果表明,该方法能够有效提高混合遗传算法在云计算资源优化中的收敛速度。     

基于萤火虫群优化的虚拟机放置方法

利用虚拟机放置策略对云数据中心的物理资源利用效率进行优化十分必要。提出了基于萤火虫群优化的虚拟机放置（glowworm swarm optimization based VM placement,Gso-wmp）方法。GSO-VMP方法将物理主机的处理器使用效率表示为荧光素值，当一个虚拟机被放置到物理主机上时，该物理主机的荧光素值都要进行更新；能够在局部径向范围内搜索到更多的可用物理主机，完成虚拟机放置，减少了虚拟机的迁移次数，从而间接地节省了物理主机的能量消耗。使用CloudSim作为GSO-VMP的仿真环境进行仿真，实验结果表明，GSO-VMP方法使得云数据中心的能耗降低、多维物理资源利用率提高。     

云计算Hadoop平台中基于遗传算法的高校师资培训管理

如何在提高云计算服务资源调度效率的同时尽量降低工作能耗,成为当前必须解决的问题。因此,提出一种基于遗传算法的云计算资源调度方法,适用于高校师资培训资源管理。首先,搭建基于云服务的高校教师师资培训系统模式;其次,利用具有较强NP问题解决能力的遗传算法设计大规模集群的资源调度方法,并采用粒子群算法对其全局寻优能力进行改进;最后,设计了由质量和成本构成的双指标约束适应度函数。CloudSim平台的Hadoop实验仿真结果显示,提出的遗传云计算资源调度方法在约束条件下获得了最佳的调度结果。     

基于遗传算法的虚拟机动态放置研究

在云数据中心（IDC）,虚拟机部署（VMP）策略是指如何在数据中心有限的物理资源中内放置合理的虚拟机（VM）。高效的虚拟机部署策略将更好的实现物理资源的整合和利用,最大化实现资源利用和能源节约的效果。该文中,根据虚拟机的存储和CPU两种资源为目标,研究资源利用率最大化的虚拟机放置策略。该策略基于遗传算法,根据虚拟机实际资源（内存和CPU）需求动态,动态的为虚拟机分配资源,实现最大限度降低数据中心资源利用不足和过度利用等概率。在最后,使用CloudSim仿真软件进行模拟实验,通过实验证明,较最佳拟合递减算法（BFD）相比,使用遗传算法对虚拟机进行动态分配,数据中心的资源利用率有较大的提升,同时也说明将多资源需求作为虚拟机放置策略考虑的重要性。     

VM migration algorithm for the balance of energy resource across data centers in cloud computing

Cloud computing makes it possible for users to share computing power. The framework of multiple data centers gains a greater popularity in modern cloud computing. Due to the uncertainty of the requests from users, the loads of CPU(Center Processing Unit) of different data centers differ. High CPU utilization rate of a data center affects the service provided for users, while low CPU utilization rate of a data center causes high energy consumption. Therefore, it is important to balance the CPU resource across data centers in modern cloud computing framework. A virtual machine(VM)migration algorithm was proposed to balance the CPU resource across data centers. The simulation results suggest that the proposed algorithm has a good performance in the balance of CPU resource across data centers and reducing energy consumption.     

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.     

A non‐cooperative game model for reliability‐based task scheduling in cloud computing

Cloud computing is a newly emerging distributed system. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes, so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum, or the overall system performance is optimal. Unlike task scheduling based on time or cost before, aiming at the special reliability requirements in cloud computing, we propose a non‐cooperative game model for reliability‐based task scheduling approach. This model takes the steady‐state availability that computing nodes provide as the target, takes the task slicing strategy of the schedulers as the game strategy, then finds the Nash equilibrium solution. We also design a task scheduling algorithm based on this model. It can be seen from the experiments that our task scheduling algorithm is better than the so‐called balanced scheduling algorithm.     

P2BED-C: A Novel Peer to Peer Load Balancing and Energy Efficient Technique for Data-Centers Over Cloud

In recent days, cloud computing data centres are considerably involved in performing operations. It accounts for the enormous energy consumption, which increases with an increase in computing capacity. Thinking with respect for the environment, reducing operating costs and energy consumption can prove to be beneficial. Previous works in data-centre energy optimization only involved scheduling the job between the servers based on thermal profiles or workload parameters. Dynamic power management by shutting down the free accessories of data centres was also considered in many models to reduce energy consumption. Further, the role of the communication fabric focused on energy consumption. The proposed work focuses on the minimization of energy consumption at both computing servers and communicating devices. Here, a parameter is defined named config to initialize the configuration of a system in a current state. The parameter will assist the existing Dynamic Voltage Frequency Scheduling (DVFS) scheme for assigning the tasks to a virtual machine to minimize energy consumption at computing servers. Moreover, it extends the Data-centre Energy-efficient Network-aware Scheduling (DENS) with the peer-to-peer load balancer to reduce energy consumption from networking components. The proposed system uses a scheduling algorithm for the cloud data centre, which reduces the energy consumption both at the server and the communication fabric level. Based on the number of samples for the energy consumption, 95% confidence achieve. Energy consumed by the proposed P2BED-C model is 1610.22 Wxh, while other existing approaches FCFS and Round Robin consumed 1684.32 and 1678.35, respectively. The results show considerable improvement in the power utilization of the server resulting in more power savings.

     

An Effective QoS-Constrained Scheduling Scheme for Cloud Computing Services

Cloud computing is becoming a hot topic of the information industry in recent years. Many companies provide the cloud services, such as Google Apps and Apple multimedia services. In general, by applying the virtualization technologies, the data center is built for cloud computing to provide users with the computing and storage resources, as well as the software environment. Thus, the quality of service (QoS) must be considered to satisfy users' requirements. This paper proposes a high efficiency scheduling scheme for supporting cloud computing. The virtual machine migration technique has been applied to the proposed scheduling scheme for improving the resources utilization and satisfying the QoS requirement of users. The experimental results show that in addition to satisfying the QoS requirement of users, the proposed scheme can improve the resources utilization effectively.     

优先级融合动态电压频率调节的云计算任务调度算法研究

为了在云计算任务调度过程中保证云设备效率的同时提高资源利用率,提出了一种基于优先级和动态电压频率调节的调度算法。首先,为调度算法定义了问题模型以规范异构服务器的性能;然后,利用优先级为任务提供可行的组合或调度;最后,利用动态电压频率调节为服务器提供适当的电压和频率供应,并且向虚拟机管理器发送分配结果。利用可扩展的模拟工具Cloud Sim进行实验评估了本文方法的能耗和调度时间,结果表明,本文方法的执行时间与MMF-DFVS方法相当,而能耗比MMF-DFVS降低了5%-25%。     

IaaS云中面向能源效率优化的虚拟机调度算法(英文)

In IaaS Cloud,different mapping relationships between virtual machines(VMs) and physical machines(PMs) cause different resource utilization,so how to place VMs on PMs to reduce energy consumption is becoming one of the major concerns for cloud providers.The existing VM scheduling schemes propose optimize PMs or network resources utilization,but few of them attempt to improve the energy efficiency of these two kinds of resources simultaneously.This paper proposes a VM scheduling scheme meeting multiple resource constraints,such as the physical server size(CPU,memory,storage,bandwidth,etc.) and network link capacity to reduce both the numbers of active PMs and network elements so as to finally reduce energy consumption.Since VM scheduling problem is abstracted as a combination of bin packing problem and quadratic assignment problem,which is also known as a classic combinatorial optimization and NP-hard problem.Accordingly,we design a twostage heuristic algorithm to solve the issue,and the simulations show that our solution outperforms the existing PM- or network-only optimization solutions.     

云环境下任务调度算法对比研究

云计算是完全基于互联网的新兴技术。云计算环境中的任务调度问题一直都是该领域的研究热点。合理高效的任务调度算法在云环境中能有效的缩短任务完成时间,提高系统负载均衡,更好的满足用户与云提供商的需求。本文研究了云平台的任务调度机制,探究了任务调度过程中的关键性指标。通过云仿真平台CloudSim实现并分析了顺序调度算法、Min-Min算法和Max-Min算法,对比其在随机生成用户任务负载与虚拟机计算资源的情况下的任务完成时间,实验证明Min-Min算法与Max-Min算法均优于顺序调度算法。以此为未来研究提供实验支撑和方向。     

Deadline based scheduling for data-intensive applications in clouds

Cloud computing emerges as a new computing pattern that can provide elastic services for any users around the world. It provides good chances to solve large scale scientific problems with fewer efforts. Application deployment remains an important issue in clouds. Appropriate scheduling mechanisms can shorten the total completion time of an application and therefore improve the quality of service (QoS) for cloud users. Unlike current scheduling algorithms which mostly focus on single task allocation, we propose a deadline based scheduling approach for data-intensive applications in clouds. It does not simply consider the total completion time of an application as the sum of all its subtasks’ completion time. Not only the computation capacity of virtual machine (VM) is considered, but also the communication delay and data access latencies are taken into account. Simulations show that our proposed approach has a decided advantage over the two other algorithms.