企业级私有云中的虚拟化实现

在云存储的概念被提出之后,虚拟化技术在云存储系统中得到了广泛的应用,出现了全新的数据中心部署和管理方式。实现了一个企业级私有云存储系统WFS,在WFS云存储系统的四层架构中,分别采用了统一存储管理、虚拟路径、数据与元数据分离、云存储接口、多路径访问等虚拟化技术。通过对该系统进行分析,说明了虚拟化技术在云存储系统中的应用方式、特点以及在提高资源利用率、增强系统安全性、易用性等方面的重要作用。     

基于在线机制设计的私有云资源分配研究

针对企业内部处于随时来也可以随时走(coming on-the-fly)的动态环境中的私有云用户资源分配问题,首先构建了一个在线私有云拍卖框架,支持能够随来随走的不同用户的工作任务要求;其次提出了一个能保证参与用户能够报真实类型的在线机制,理论分析证明了该机制的占优策略激励兼容性质(DSIC),计算了该机制的竞争比;最后通过实验仿真表明了本机制的有效性。     

服务水平协议感知的云中心利润最大化资源配置

针对云计算中心优化资源配置以实现利润最大化的问题,提出基于服务水平协议(SLA)感知的云中心服务器数量和运行速率优化的分析模型,同时考虑了云中心能耗成本、服务器租用成本、用户等待时间、SLA违规惩罚等重要因素。通过数值仿真分析了服务器数量、运行速率变化对云中心利润的影响。数值仿真结果表明:在请求速率固定时,通过优化服务器数量、运行速率可使云中心获得最大利润;而随着请求速率的增加,优化服务器数量和运行速率将会使云中心利润呈线性增长。分析结果可为云服务提供商获得更好的收益提供参考方法。     

云计算虚拟化技术的发展与趋势

云计算是一种融合了多项计算机技术的以数据和处理能力为中心的密集型计算模式,其中以虚拟化、分布式数据存储、分布式并发编程模型、大规模数据管理和分布式资源管理技术最为关键。经过十多年的发展,云计算技术已经从发展培育期步入快速成长期,越来越多的企业已经开始使用云计算服务。与此同时,云计算的核心技术也在发生着巨大的变化,新一代的技术正在改进甚至取代前一代技术。容器虚拟化技术以其轻便、灵活和快速部署等特性对传统的基于虚拟机的虚拟化技术带来了颠覆性的挑战,正在改变着基础设施即服务（IaaS）平台和平台即服务（PaaS）平台的架构和实现。对容器虚拟化技术进行深入介绍,并通过分析和比较阐述容器虚拟化技术和虚拟机虚拟化技术各自的优势、适应场景和亟待解决的问题,然后对云计算虚拟化技术的下一步研究方向和发展趋势进行展望。     

基于虚拟化技术的私有云计算平台设计

为提高分布式集群系统的硬件资源利用率,避免闲置设备造成的经济损失,结合虚拟化技术,提出了一种基于多种框架技术的私有云平台实现方案.该方案整合底层硬件资源,实现了对资源的按需分割、动态分配及动态迁移,并针对传统的虚拟机部署方法中的负载不均衡问题,提出了基于动态分配决策的虚拟机部署机制,该策略根据虚拟机资源的特点,结合现有物理节点的负载情况,对虚拟机进行了动态部署.最后设计实现了灵活性强、可扩展性能好的私有云计算服务平台,以石油勘探中的傅里叶有限差分叠前深度偏移为测试用例进行了应用测试,证明了私有云平台的可行性和有效性,并对虚拟机的部署机制进行了测试.实验结果表明,动态分配决策能够在部署大量虚拟机的同时,较好地保持私有云平台的负载平衡.     

Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future.     

Resource virtualization and service selection in cloud logistics

Cloud computing and Internet of Things have promoted a new logistics service mode, i.e., the cloud logistics mode. This work studies the resource virtualization and service encapsulation of a logistics center, and focuses on the technologies of resource expression and service encapsulation. After the resources of a logistics center are encapsulated in web services, how to find the “best” concrete web service among many is a critically important issue. This work considers service selection as an optimization problem and establishes a Particle Swarm Optimization (PSO)-based web service selection model with quality of service (QoS) constraints. It can be used to address the horizontal adaptation issues from the composite web services. The feasibility and effectiveness of the model are verified by several experiments.     

Integrating QoS awareness with virtualization in cloud computing systems for delay-sensitive applications

Cloud computing provides scalable computing and storage resources over the Internet. These scalable resources can be dynamically organized as many virtual machines (VMs) to run user applications based on a pay-per-use basis. The required resources of a VM are sliced from a physical machine (PM) in the cloud computing system. A PM may hold one or more VMs. When a cloud provider would like to create a number of VMs, the main concerned issue is the VM placement problem, such that how to place these VMs at appropriate PMs to provision their required resources of VMs. However, if two or more VMs are placed at the same PM, there exists certain degree of interference between these VMs due to sharing non-sliceable resources, e.g. I/O resources. This phenomenon is called as the VM interference. The VM interference will affect the performance of applications running in VMs, especially the delay-sensitive applications. The delay-sensitive applications have quality of service (QoS) requirements in their data access delays. This paper investigates how to integrate QoS awareness with virtualization in cloud computing systems, such as the QoS-aware VM placement (QAVMP) problem. In addition to fully exploiting the resources of PMs, the QAVMP problem considers the QoS requirements of user applications and the VM interference reduction. Therefore, in the QAVMP problem, there are following three factors: resource utilization, application QoS, and VM interference. We first formulate the QAVMP problem as an Integer Linear Programming (ILP) model by integrating the three factors as the profit of cloud provider. Due to the computation complexity of the ILP model, we propose a polynomial-time heuristic algorithm to efficiently solve the QAVMP problem. In the heuristic algorithm, a bipartite graph is modeled to represent all the possible placement relationships between VMs and PMs. Then, the VMs are gradually placed at their preferable PMs to maximize the profit of cloud provider as much as possible. Finally, simulation experiments are performed to demonstrate the effectiveness of the proposed heuristic algorithm by comparing with other VM placement algorithms.     

On improvement of cloud virtual machine availability with virtualization fault tolerance mechanism

Virtualization, particularly in the field of cloud computing, is a common strategy to improve existing computing resources. Hadoop, one of the Apache projects, is designed to scale up from single servers to thousands of machines, each offering local computation and storage capabilities. However, how to guarantee both stability and reliability of virtualization have become important topics. In this article, to reach this goal we used current open-source software and platforms, for instance, the Xen-Hypervisor virtualization technology, and the OpenNebula virtual machines management tool. After extending components capabilities, we developed a mechanism to support our ideas and reached high availability with Hadoop that is also called as virtualization fault tolerance (VFT). We considered a practical problem, i.e., the single-point-of-failure issue that occurs frequently in virtualization systems, and the experimental results confirm that the downtime interval can be greatly shortened even if failure occurred. As a result, VFT is useful not only for Hadoop applications, but also for more areas in cluster-based systems.     

面向云计算虚拟化的信息安全防护方案研究

云计算通过虚拟化技术实现网络按需调用集中共享资源,也引入了新的信息安全风险。文章分析了虚拟化面临各种威胁所对应的解决技术,通过无代理方式,从虚拟机外部为虚拟机中运行的系统提供高级保护。同时,从入侵检测、通信访问控制、防恶意软件以及防病毒等方面制定有效、全面的安全防护方案,完善企业云计算虚拟化信息安全防护体系,提高企业云计算虚拟化信息安全防护水平。     

Resource virtualization methodology for on-demand allocation in cloud computing systems

The resources’ heterogeneity and unbalanced capability, together with the diversity of resource requirements in cloud computing systems, have produced great contradictions between resources’ tight coupling characteristics and user’s multi-granularities requirements. We propose a resource virtualization model and its on-demand allocation oriented infrastructure mainly providing computing services to solve that problem. A loosely coupled resource environment centered on resource users is created to complete a mapping from physical view of resources to logic view of resources. Heuristic resource combination algorithm (HRCA) is proposed to transform physical resources to logic resources, which meets two requirements: randomness in combination and fluctuation control to the size of resources granularities. On the basis of the appraisal indexes presented for the on-demand allocation, resource matching algorithm (RMA), targeting at resource satisfaction with the highest resource utilization, is designed to reuse resources. RMA can satisfy users’ requirement in limited time and keep resource satisfaction in the highest level in the condition of logic resources granularities being less than their required size. Resource reconfiguration algorithm (RRA) is presented to implement resource matching in the condition that virtual computing resource pool cannot match granularities of resource requirements. RRA assures the lowest resource refusal rate and the greatest resource satisfaction. We verify the effectiveness, performance and accuracy of algorithms in implementing the goal of resource virtualization centered on resource users and on-demand allocation.     

A file-deduplicated private cloud storage service with CDMI standard

The emergence of cloud environments makes users convenient to synchronize files across platform and devices. However, the data security and privacy are still critical issues in public cloud environments. In this paper, a private cloud storage service with the potential for security and performance concerns is proposed. A data deduplication scheme is designed in the proposed private cloud storage system to reduce cost and increase the storage efficiency. Moreover, the Cloud Data Management Interface (CDMI) standard is implemented in the proposed system to increase the interoperability. The proposed service provides an easy way to for user to establish the system and access data across devices conveniently. The experiment results also show the superiority of the proposed interoperable private cloud storage service in terms of data transmission and storage efficiency. By comparing with the existing system Gluster Swift, the proposed system is demonstrated much suitable for the service environment where most of the transmitted data are small files.     

Feedback-based optimization of a private cloud

The optimization problem addressed by this paper involves the allocation of resources in a private cloud such that cost to the provider is minimized (through the maximization of resource sharing) while attempting to meet all client application requirements (as specified in the SLAs). At the heart of any optimization based resource allocation algorithm, there are two models: one that relates the application level quality of service to the given set of resources and one that maps a given service level and resource consumption to profit metrics. In this paper we investigate the optimization loop in which each application’s performance model is dynamically updated at runtime to adapt to the changes in the system. These changes could be perturbations in the environment that had not been included in the model. Through experimentation we show that using these tracking models in the optimization loop will result in a more accurate optimization and thus result in the generation of greater profit.     

Model-based cloud resource management with TOSCA and OCCI

Software and Systems Modeling - With the advent of cloud computing, different cloud providers with heterogeneous cloud services (compute, storage, network, applications, etc.) and their related...     

CyberGuarder: A virtualization security assurance architecture for green cloud computing

As the sizes of IT infrastructure continue to grow, cloud computing is a natural extension of virtualisation technologies that enable scalable management of virtual machines over a plethora of physically connected systems. The so-called virtualisation-based cloud computing paradigm offers a practical approach to green IT/clouds, which emphasise the construction and deployment of scalable, energy-efficient network software applications (NetApp) by virtue of improved utilisation of the underlying resources. The latter is typically achieved through increased sharing of hardware and data in a multi-tenant cloud architecture/environment and, as such, accentuates the critical requirement for enhanced security services as an integrated component of the virtual infrastructure management strategy. This paper analyses the key security challenges faced by contemporary green cloud computing environments, and proposes a virtualisation security assurance architecture, CyberGuarder, which is designed to address several key security problems within the ‘green’ cloud computing context. In particular, CyberGuarder provides three different kinds of services; namely, a virtual machine security service, a virtual network security service and a policy based trust management service. Specifically, the proposed virtual machine security service incorporates a number of new techniques which include (1) a VMM-based integrity measurement approach for NetApp trusted loading, (2) a multi-granularity NetApp isolation mechanism to enable OS user isolation, and (3) a dynamic approach to virtual machine and network isolation for multiple NetApp’s based on energy-efficiency and security requirements. Secondly, a virtual network security service has been developed successfully to provide an adaptive virtual security appliance deployment in a NetApp execution environment, whereby traditional security services such as IDS and firewalls can be encapsulated as VM images and deployed over a virtual security network in accordance with the practical configuration of the virtualised infrastructure. Thirdly, a security service providing policy based trust management is proposed to facilitate access control to the resources pool and a trust federation mechanism to support/optimise task privacy and cost requirements across multiple resource pools. Preliminary studies of these services have been carried out on our iVIC platform, with promising results. As part of our ongoing research in large-scale, energy-efficient/green cloud computing, we are currently developing a virtual laboratory for our campus courses using the virtualisation infrastructure of iVIC, which incorporates the important results and experience of CyberGuarder in a practical context.     

Packet aggregation based network I/O virtualization for cloud computing

Virtualization is a key technology to enable cloud computing. Driver domain based model for network virtualization offers isolation and high levels of flexibility. However, it suffers from poor performance and lacks scalability. In this paper, we evaluate networking performance of virtual machines within Xen. The I/O channel transferring packets between the driver domain and the virtual machines is shown to be the bottleneck. To overcome this limitation, we proposed a packet aggregation based mechanism to transfer packets from the driver domain to the virtual machines. Packet aggregation, combined with an efficient core allocation, allows virtual machines throughput to scale up by 700%, while minimizing both memory and CPU consumption. Besides, aggregation impact on packets delay and jitter remains acceptable. Hence, the proposed I/O virtualization model satisfies infrastructure providers to offer Cloud computing services.     

网络功能虚拟化环境中大规模资源状态监测策略

在网络功能虚拟化（NFV）环境中,为了提高网络中基础设施资源利用率,高效动态部署服务功能链,编排管理域需要对网络中底层资源及虚拟网络功能状态进行实时监测,但实时监测会产生大量通信开销。提出了网络通信开销最小化的智能分布式监测策略,通过改进的标签传播算法智能划分子网并选择代理监测节点,实现了对资源和虚拟功能状态的高效监测,并使监测信息通信开销最小。仿真结果表明,所提监测策略使网络中监测信息通信开销降低约13%。图4 不同算法下子网节点数量方差图     

Simulating peer-to-peer cloud resource scheduling

Resource scheduling in large-scale distributed systems, such as grids and clouds, is difficult due to the size, dynamism, and volatility of resources. These resources are eclectic and autonomous, and may exhibit different usage policies, levels of participation, capabilities, local load, and reliability. Moreover, applications are likely to exhibit various patterns and levels, and distributed resources may organize into various different overlay topologies for information and query dissemination. Researchers have proposed a wide variety of approaches and policies for mapping offered load onto resources and for solving the various component parts of the scheduling problem. However, production clouds and grids may be underutilized, and may not exhibit the load to effectively characterize all of the scheduling system inputs. The composition of large-scale systems is also changing, potentially to include more individual and peer-to-peer resources. These factors will influence the effectiveness of proposed scheduling solutions. Therefore, a simulation environment is necessary to study different approaches under different scenarios, especially those that are expected, but that are not currently characteristic of existing systems. This article describes a general-purpose peer-to-peer simulation environment that allows a wide variety of parameters, protocols, strategies and policies to be varied and studied. To provide a proof of concept, utilization of the simulation environment is presented in a large-scale distributed system problem that includes a core model and related mechanisms. In particular, this article presents a definition and possible peer-to-peer solutions for the large-scale scheduling problem. Moreover, this article describes a general simulation model, some policies that can be varied, an implementation, and some sample results.     

SLA-driven dynamic cloud resource management

As the size and complexity of Cloud systems increase, the manual management of these solutions becomes a challenging issue as more personnel, resources and expertise are needed. Service Level Agreement (SLA)-aware autonomic cloud solutions enable managing large scale infrastructure management meanwhile supporting multiple dynamic requirement from users. This paper contributes to these topics by the introduction of Cloudcompaas, a SLA-aware PaaS Cloud platform that manages the complete resource lifecycle. This platform features an extension of the SLA specification WS-Agreement, tailored to the specific needs of Cloud Computing. In particular, Cloudcompaas enables Cloud providers with a generic SLA model to deal with higher-level metrics, closer to end-user perception, and with flexible composition of the requirements of multiple actors in the computational scene. Moreover, Cloudcompaas provides a framework for general Cloud computing applications that could be dynamically adapted to correct the QoS violations by using the elasticity features of Cloud infrastructures. The effectiveness of this solution is demonstrated in this paper through a simulation that considers several realistic workload profiles, where Cloudcompaas achieves minimum cost and maximum efficiency, under highly heterogeneous utilization patterns.