基于云计算的智能电网负荷预测平台架构研究

提出一种基于云计算的智能电网负荷预测平台架构.深入地阐述了云计算关键技术、负荷预测云架构、云平台负荷预测服务等.采用Hadoop云计算技术,对负荷预测的资源调度和计算进行虚拟云计算仿真,验证了以电力私有云为基础建立的负荷预测机制更优良.同时在云平台负荷预测服务中引入多维多级协调优化机制,对原始预测结果进行修正与协调优化,显著地提升了基于云计算的智能电网负荷预测水平.由此搭建的平台架构将为智能电网负荷预测提供广阔的思路与有力的技术支持.     

Cloud Virtual Machine Lifecycle Security Framework Based on Trusted Computing

As a foundation component of cloud computing platforms, Virtual Machines(VMs) are confronted with numerous security threats. However, existing solutions tend to focus on solving threats in a specific state of the VM. In this paper, we propose a novel VM lifecycle security protection framework based on trusted computing to solve the security threats to VMs throughout their entire lifecycle. Specifically, a concept of the VM lifecycle is presented divided up by the different active conditions of the VM. Then, a trusted computing based security protection framework is developed, which can extend the trusted relationship from trusted platform module to the VM and protect the security and reliability of the VM throughout its lifecycle. The theoretical analysis shows that our proposed framework can provide comprehensive safety to VM in all of its states. Furthermore, experiment results demonstrate that the proposed framework is feasible and achieves a higher level of security compared with some state-of-the-art schemes.     

A Multi-Objective Optimization Method of Initial Virtual Machine Fault-Tolerant Placement for Star Topological Data Centers of Cloud Systems

Virtualization is the most important technology in the unified resource layer of cloud computing systems.Static placement and dynamic management are two types of Virtual Machine(VM) management methods.VM dynamic management is based on the structure of the initial VM placement,and this initial structure will affect the efficiency of VM dynamic management.When a VM fails,cloud applications deployed on the faulty VM will crash if fault tolerance is not considered.In this study,a model of initial VM fault-tolerant placement for star topological data centers of cloud systems is built on the basis of multiple factors,including the service-level agreement violation rate,resource remaining rate,power consumption rate,failure rate,and fault tolerance cost.Then,a heuristic ant colony algorithm is proposed to solve the model.The service-providing VMs are placed by the ant colony algorithms,and the redundant VMs are placed by the conventional heuristic algorithms.The experimental results obtained from the simulation,real cluster,and fault injection experiments show that the proposed method can achieve better VM fault-tolerant placement solution than that of the traditional first fit or best fit descending method.     

Efficient Multi-Tenant Virtual Machine Allocation in Cloud Data Centers

Virtual Machine(VM) allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant's VMs may considerably impact the tenant's Quality of Service(Qo S). In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs' network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem(LP-MKP). The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the Qo S among tenants, and improves tenants' overall Qo S in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.     

云计算资源优化问题求解的萤火虫算法

为了提高云计算资源利用率,结合云计算资源优化问题的特点,设计一种云计算资源优化问题求解的萤火虫算法.首先建立云计算资源负载调度问题的约束条件,以用户任务完成时间最少作为云计算资源负载调度优化的目标函数;然后通过萤火虫算法找到目标函数值最优的资源调度策略;最后在CloudSim平台上实现云计算资源负载调度仿真实验.实验结果表明,萤火虫算法减少了云计算任务完成的时间,均衡了云计算资源的负载,使云计算资源得到合理分配,且比其他算法优势明显.     

云计算中基于拍卖的虚拟机动态供应和分配算法

当前云计算供应商通过定价算法或类似拍卖的算法来分配虚拟机（virtual machine,VM）。针对这些算法大多要求虚拟机静态供应,无法准确预测用户需求,导致资源未得到充分利用的问题,提出一种基于组合拍卖的虚拟机动态供应和分配算法,在做出虚拟机供应决策时考虑用户对虚拟机的需求。该算法将可用的计算资源看成是“流体”资源,且这些资源根据用户请求可分为不同数量、不同类型的虚拟机实例。然后可根据用户的估价决定分配策略,直到所有资源分配完毕。基于并行工作负载存档（parallel workload archive,PWA）的真实工作负载数据进行了仿真实验,结果表明该方法可保证为云供应商带来更高收入,提高资源利用率。     

VirtCO:Joint Coflow Scheduling and Virtual Machine Placement in Cloud Data Centers

Cloud data centers, such as Amazon EC2, host myriad big data applications using Virtual Machines(VMs). As these applications are communication-intensive, optimizing network transfer between VMs is critical to the performance of these applications and network utilization of data centers. Previous studies have addressed this issue by scheduling network flows with coflow semantics or optimizing VM placement with traffic considerations.However, coflow scheduling and VM placement have been conducted orthogonally. In fact, these two mechanisms are mutually dependent, and optimizing these two complementary degrees of freedom independently turns out to be suboptimal. In this paper, we present VirtCO, a practical framework that jointly schedules coflows and places VMs ahead of VM launch to optimize the overall performance of data center applications. We model the joint coflow scheduling and VM placement optimization problem, and propose effective heuristics for solving it. We further implement VirtCO with OpenStack and deploy it in a testbed environment. Extensive evaluation of real-world traces shows that compared with state-of-the-art solutions, VirtCO greatly reduces the average coflow completion time by up to 36.5%. This new framework is also compatible with and readily deployable within existing data center architectures.     

云数据中心基于负载预测的物理主机状态检测策略

针对云数据中心现有物理主机状态检测算法对提高云数据中心物理资源的利用率效果不明显问题,提出了基于负载预测的物理主机状态检测策略(load prediction based physical host status detection,LP-PHSD),LP-PHSD利用时间序列和二次指数平滑法预测出物理主机在未来一段时间内的资源利用率情况,同时结合绝对中位差方法,确定资源利用率动态阈值边界,选择适当的时刻进行迁移,提高物理资源的利用效率,降低能量消耗.LP-PHSD包括源物理主机状态检测和目标物理主机状态检测2个部分,可以很好地判断出虚拟机迁移的时刻.实验表明,经LP-PHSD策略优化后的新虚拟机迁移方法与近几年的BenchMark迁移模型比较起来,云数据中心的总体能量消耗降低,虚拟机迁移次数减少,云服务质量明显提高.     

基于贝叶斯信任模型的资源调度策略

在科技迅速发展的今天,云平台正在迅速成为科学应用的重要平台.由于云平台下的节点资源数量非常多,显然他们有很多是不可靠的,这些节点会对EXE的执行和调度任务产生很大的影响.在本文中,受贝叶斯认知模型的启发和社会学的信任关系模型的引导,笔者首先提出了一种新的基于贝叶斯方法的认知信任模型,然后,将这种模型应用到资源调度系统中.理论分析和仿真实验证明,笔者提出的方法能有效的满足云计算对节点资源的信任要求,并且牺牲较少的时间成本,确保在一个相对安全的节点资源池中执行云计算任务.     

A less resource-consumed security architecture on cloud platform

Traditional security framework in cloud platform usually brings self-vulnerability and considerable additional resource consumption. To solve these problems, we propose an external processes monitoring architecture for current popular cloud platform OpenStack with kernel-based virtual machine (KVM). With this architecture, we can monitor all active processes in online virtual machine (VMs) and scan them for their potential maliciousness in OpenStack with no agent, and can also detect hidden processes in offline VMs’ memory snapshots and notice the user to decide whether to kill them when VMs become active. Analysis and experimental results show that our architecture is able to reduce consumption of CPU, memory and bandwidth in cloud platform and can detect viruses and hidden processes effectively in VMs.     

A Security-Improved Scheme for Virtual TPM Based on KVM

Virtual trusted platform module(vTPM) is an important part in building trusted cloud environment. Aiming at the remediation of lack of effective security assurances of vTPM instances in the existing virtual TPM architecture, this paper presents a security-improved scheme for virtual TPM based on kernel-based virtual machine(KVM). By realizing the TPM 2.0 specification in hardware and software, we add protection for vTPM's secrets using the asymmetric encryption algorithm of TPM. This scheme supports the safety migration of a TPM key during VM-vTPM migration and the security association for different virtual machines(VMs) with vTPM instances. We implement a virtual trusted platform with higher security based on KVM virtual infrastructure. The experiments show that the proposed scheme can enhance the security of virtual trusted platform and has fewer additional performance loss for the VM migration with vTPM.     

面向互联网应用的云操作系统的架构设计

云计算的核心理念是按需提供IT服务, 这对基础设施提供商带来巨大的影响. 介绍以中小企业为主要服务群体的云操作系统INACloud的架构设计, 并且综合考虑实际运维问题、自主创新和开源软件等因素以及面向互联网应用动态多变的资源需求, 重点探讨调度策略、监控设计和计费管理等方面.     

Hadoop加速比性能模型研究

MapReduce编程模型被看作是Hadoop的核心组件,一直以来都受到广泛的应用和研究,但针对Hadoop云平台的性能研究主要集中在配置调优和分布式算法等方面,缺乏对Hadoop加速比性能模型的理论研究.针对此问题提出了Hadoop云平台加速比性能模型,并分别在同构机群和异构机群架构上对该模型进行了分析.针对同构机群提出了3种I/O负载模型,并指出了其对加速比的影响;指出了异构机群不适合做Hadoop云平台的原因及异构性对Hadoop云平台造成的影响.这些研究对Hadoop云计算平台的搭建及如何提高Hadoop云平台加速比性能有很好的指导作用,提供了理论依据.     

改进蚁群算法的云计算资源调度模型

针对当前云计算系统资源调度算法的资源利用率低、浪费严重等缺陷,提出一种基于改进蚁群算法的云计算资源调度优化模型,以获得更理想的云计算资源调度方案.首先对云计算资源调度的工作原理进行分析,建立云计算资源调度优化目标函数;然后利用蚁群优化算法模拟蚁群找到一条从起点到目的地的路径,即云计算资源调度目标函数的最优解,并结合目标函数对蚁群算法进行相应地改进;最后采用MATLAB2014R编程实现云计算资源调度优化模型.实验结果表明,该模型在短时间内可找到云计算资源调度的最优解,使资源利用率得到了改善.     

Trusted Attestation Architecture on an Infrastructure-as-a-Service

Trusted attestation is the main obstruction preventing large-scale promotion of cloud computing.How to extend a trusted relationship from a single physical node to an Infrastructure-as-a-Service(IaaS) platform is a problem that must be solved.The IaaS platform provides the Virtual Machine(VM),and the Trusted VM,equipped with a virtual Trusted Platform Module(vTPM),is the foundation of the trusted IaaS platform.We propose a multi-dimensional trusted attestation architecture that can collect and verify trusted attestation information from the computing nodes,and manage the information centrally on a cloud management platform.The architecture verifies the IaaS's trusted attestation by apprising the VM,Hypervisor,and host Operating System's(OS) trusted status.The theory and the technology roadmap were introduced,and the key technologies were analyzed.The key technologies include dynamic measurement of the Hypervisor at the process level,the protection of vTPM instances,the reinforcement of Hypervisor security,and the verification of the IaaS trusted attestation.A prototype was deployed to verify the feasibility of the system.The advantages of the prototype system were compared with the Open CIT(Intel Cloud attestation solution).A performance analysis experiment was performed on computing nodes and the results show that the performance loss is within an acceptable range.     

一种融合虚拟机选择的虚拟机放置方法

为了节省云数据中心的能量消耗,提出一种融合虚拟机选择的虚拟机放置方法.该策略将虚拟机迁移过程划分为物理主机状态检测,虚拟机选择和虚拟机放置3个步骤;在物理主机状态检测和虚拟机选择阶段,选取了Cloudsim项目中已有的且被证明是优秀的鲁棒局部归约检测方法和最小迁移时间选择方法;在最后的虚拟机放置阶段,以虚拟机和物理主机...     

FlexCore: Dynamic Virtual Machine Scheduling Using VCPU Ballooning

As multi-core processors become the de-facto configuration in modern computers, the adoption of SMP Virtual Machines(VMs) has been increasing, allowing for more efficient use of computing resources. However,because of existence of schedulers in both the hypervisor and the guest VMs, this creates a new research problem,viz., double scheduling. Although double scheduling may cause many issues including lock-holder preemption,v CPU stacking, CPU fragmentation, and priority inversion, prior approaches have either introduced new problems and/or addressed the problem incompletely. In this paper, we describe the design and implementation of Flex Core,a new scheduling scheme using v CPU ballooning, which dynamically adjusts the number of v CPUs of a VM at runtime. This essentially eliminates unnecessary scheduling in the hypervisor layer, and thus, boosts performance significantly. An evaluation using a complete KVM-based implementation shows that the average performance improvement for PARSEC applications on a 12-core Intel machine is approximately 52.9%, ranging from 35.4% to79.6%.     

一种基于粒子群优化的虚拟资源分配方法

针对云计算环境下存在虚拟化资源利用率不高?延迟?性能衰减等问题,提出一种基于粒子群的虚拟资源分配优化方法?根据云计算优势,对虚拟化资源 进行描述及状态定义,分析了虚拟环境下的服务质量(quality of services,QoS)组成和计算方法;同时通过用户与云提供商收益最大化的平衡关系,建立一种虚拟化操作方法,以确保虚拟化资源被合理分配和回收;采用粒子群算法使用户和虚拟服务提供方都能趋于收益平衡?在此基础上设计的一款云播放器与直接嵌入到Web播放器相比得出,提出的粒子群优化方法有效且将资源利用率至少提高10%,与QoS约束的二元整数规划方法相比,提出的方法操作虚拟资源更有效?     

RAQPSO算法的云计算资源调度策略

在"互联网+"时代,云计算代表了一种新的商业模式,而云系统中用户任务与计算节点的调度问题极大地影响着系统的性能和云竞争力。为此,提出了一种改进的量子粒子群算法——反向自适应量子粒子群算法(RAQPSO),通过对惯性权值参数的调整和加入反向学习算子来提高算法的全局搜索能力,并将其应用于云计算资源调度中,仿真验证了算法的有效性。建立了云计算资源调度问题的模型;采用自适应机制,将适应度函数的变化程度作为惯性权值的更新因子,避免了单纯地根据迭代次数的线性函数来取值,从而使粒子不易陷入局部最优;随后加入粒子反向学习算子,加强了粒子全局搜索能力。实验结果表明,RAQPSO算法大大节约了任务完成时间,并且保持了良好的计算节点负载平衡。     

基于任务分类思维的云计算海量资源改进调度

对云计算海量数据下的资源调度的研究过程中,进行资源调度时资源分配无法到达合理化调度,存在资源调度效率低的问题。提出分类思维的云计算海量数据资源优化调度方法。该方法引入膜计算概念,将云计算下的海量资源调度的总任务划分为多个子任务,并详细计算每个子任务的资源调度任务量。将优化调度系统内部分解为主膜和辅助膜,利用蝙蝠算法在辅助膜内进行资源分配个体寻优,并将优化后的资源分配最优个体传送到主膜间进行云计算海量数据下的资源分配优化。实验仿真证明,基于改进膜计算蝙蝠算法的云计算海量数据下的资源优化调度方法调度效率高,分配较为均衡。