SHMA:一种云平台的监控框架

由于云平台架构的复杂性和工作负载的不可预测性,云监控对保证云平台的高可用性有重要意义,云平台上资源的动态性、多样性以及资源规模巨大给云平台监控带来了一定的困难。研究并提出一种面向云平台的可扩展多层次监控框架SHMA,SHMA采用微服务架构构建监控系统中可扩展的各个相互独立的服务组件,实现对云平台不同层次的应用服务、中间件和基础设施资源的监控。通过对部署并运行在CloudStack基础设施云平台上的某综合减灾空间信息服务应用系统进行案例分析,验证了该监控框架的有效性。     

云资源状态监控研究综述

云计算通过网络共享成功实现了计算资源的高效利用。云资源分配的动态性、随机性、开放性使得云平台的服务质量保障难题日益突出。云环境下资源状态的监控技术通过深入挖掘分析监控数据,及时发现计算资源的异常运行状态。根据历史运行数据等对资源的未来使用状态做出预测。以便及时发现潜在的性能瓶颈和安全威胁,为用户提供可靠稳定的云服务。结合实例介绍了在资源状态监控方面有代表性的研究方法,包括概率分析、方程拟合、聚类分析等方法,并对比了各类方法的性能特点及局限性。最后讨论了云资源状态监控技术在数据复杂度和规模等方面所面临的技术挑战,并指出了原始数据去冗降维、算法设计突出非监督化、计算任务向终端推送、分析结果协同增效等未来发展趋势。     

ASP还是MSP?

应用程序服务提供商(ASP)是提供应用软件远程托管服务的机构,按照使用次数向软件使用单位收取费用。ASP近来大出风头,被商家和媒体捧为未来企业计算机应用的解决之道。 ASP有不少显而易见的好处:无需购买,租赁即可:节省了安装和升级费用;对应用软件比较深入的了解。目前许多公司感到最为迫     

复杂路面小尺度行人检测综述

针对云平台中对应用程序的性能监控方法存在全流程收集分析异常能力不足的问题, 提出一种基于云平台服务组件的应用程序异常检测和瓶颈识别系统(AAD-PSC), 可对多层架构云平台上的应用程序提供可自定义指标值的监控分析能力. 系统首先在前端应用服务层收集云平台服务调用数据并与异常事件相关联; 然后为应用程序适配定制化的异常检测方法, 达到最优检测效果; 最后查明由非工作负载变化引起的性能异常, 并对其进行瓶颈识别. 实验结果表明, 监控系统可快速准确检测不同类别的异常事件并识别性能瓶颈, 能够满足云平台下对应用程序的性能监控需求.     

基于云基础设施的服务部署优化问题建模

目前典型的云基础设施提供商所提供的虚拟机资源都是有限的规格类型,当同一个服务部署在不同配置的虚拟机上时,会体现出不同的性能。通过对基于云基础设施的SaaS层服务部署情况进行研究和分析,提出对服务部署策略进行优化的必要性,并将该服务部署优化问题抽象为多目标组合优化问题,对其进行数学建模;然后利用排队论,得出优化指标的计算方法,并进行实验验证。     

面向云应用系统的容错即服务优化提供方法

通过提供高效且持续可用的容错服务以保障云应用系统的可靠运行是至关重要的.采用容错即服务的模式,提出了一种优化的云容错服务动态提供方法,从云应用组件的可靠性及响应时间等方面描述云应用容错需求,以常用的复制、检查点和NVP（N-version programming）等容错技术为基础,充分考虑容错服务动态切换开销,分别针对支撑容错服务的底层云资源是否足够的场景,给出可用容错即服务提供方案的最优化求解方法.实验结果表明,所提方法降低了云应用系统支付的容错服务费用及支撑容错服务的底层云资源的开销,提高了容错服务提供商为多个云应用实施高效、可靠容错即服务的能力.     

基于混合云的虚拟学习环境初探

为了更好地支持教师教学和学生学习,教育机构需要不断优化IT基础设施、资源和业务流程。一种新兴的计算范式"云计算(cloud computing)"是最佳的解决方案,它通过提供基础设施、平台和教育服务,改变IT资源的利用和消费模式,将对教育领域产生巨大影响。文章构建一种基于混合云的虚拟学习环境,探讨实现该学习环境面临的挑战,从而提高应用程序及服务的可扩展性和可靠性,削减教育机构软硬件资源的开销,减少软件更新和数据中心维护的费用。     

基于开源软件的云监控平台设计与实现

最近几年,基于云计算的互联网服务迅速增长,数据中心的基础设施和应用资源不断增加。需要及时、高效的监控和管理资源的可用性,保证云服务的可靠性。通过对云监控的内容和特性的研究,提出一种管理云计算资源的云监控系统架构,重点对分布式架构、逻辑处理模块和云监控接口进行设计,实例中完成对私有云中物理主机、虚拟机、应用服务的批量监控,实现数据的存储和展示。     

基于在线实例配置的服务功能链部署方法

为应对实际环境中网络流量的动态变化,同时降低运营商的运营成本,提出基于在线服务功能扩展的服务功能链部署方法。将空置状态虚拟功能实例的生命周期管理问题建模为雪橇租赁模型,采用雪橇租赁问题的最优解配置空置服务功能实例的生命周期,并设计自适应扩展开销最小化的在线服务功能实例扩展算法。以服务功能实例自适应扩展的结果作为约束条件,将每一时刻的服务功能链部署问题建模为整数线性规划问题,利用遗传算法求解带宽开销最小化的服务功能路径,实现动态场景下的服务功能链部署。仿真结果表明,该方法能根据网络流量变化动态调整虚拟资源,降低服务功能实例自适应扩展成本,节省带宽资源。     

边缘计算场景下基于强化学习的应用最优部署

为有效解决城市范围内智能公共交通应用程序的布局问题,制定总代价最小化的应用布局优化策略MIN-COST,以降低应用程序部署的总代价为目标,同时满足应用程序服务延时要求。通过提出一个基于深度强化学习技术优化公交边缘应用程序部署的一般框架,可以从历史经验中学习到最优化部署方法,相对于一般启发式算法更加快速。将仿真结果与其它部署策略进行比较,验证了所提策略可以在保证服务时延的基础上有效降低应用程序服务总代价。     

Enabling cost-aware and adaptive elasticity of multi-tier cloud applications

Elasticity (on-demand scaling) of applications is one of the most important features of cloud computing. This elasticity is the ability to adaptively scale resources up and down in order to meet varying application demands. To date, most existing scaling techniques can maintain applications’ Quality of Service (QoS) but do not adequately address issues relating to minimizing the costs of using the service. In this paper, we propose an elastic scaling approach that makes use of cost-aware criteria to detect and analyse the bottlenecks within multi-tier cloud-based applications. We present an adaptive scaling algorithm that reduces the costs incurred by users of cloud infrastructure services, allowing them to scale their applications only at bottleneck tiers, and present the design of an intelligent platform that automates the scaling process. Our approach is generic for a wide class of multi-tier applications, and we demonstrate its effectiveness against other approaches by studying the behaviour of an example e-commerce application using a standard workload benchmark.     

基于Xen虚拟化技术的弹性云架构

云计算通过虚拟化技术为用户提供基础架构即服务(IaaS),IaaS平台上应用和服务的负载是动态变化的,这就导致其对虚拟资源的需求也是动态变化的.因此收集和分析云平台内部虚拟资源的占用量,根据需求对其进行弹性调度就成为提高整个云计算平台服务性能和资源利用率的关键.从负载均衡和降低云平台使用者成本的角度出发,根据云平台内部...     

Dynamic cloud resource management for efficient media applications in mobile computing environments

Single-instruction-set architecture (Single-ISA) heterogeneous multi-core processors (HMP) are superior to Symmetric Multi-core processors in performance per watt. They are popular in many aspects of the Internet of Things, including mobile multimedia cloud computing platforms. One Single-ISA HMP integrates both fast out-of-order cores and slow simpler cores, while all cores are sharing the same ISA. The quality of service (QoS) is most important for virtual machine (VM) resource management in multimedia mobile computing, particularly in Single-ISA heterogeneous multi-core cloud computing platforms. Therefore, in this paper, we propose a dynamic cloud resource management (DCRM) policy to improve the QoS in multimedia mobile computing. DCRM dynamically and optimally partitions shared resources according to service or application requirements. Moreover, DCRM combines resource-aware VM allocation to maximize the effectiveness of the heterogeneous multi-core cloud platform. The basic idea for this performance improvement is to balance the shared resource allocations with these resources requirements. The experimental results show that DCRM behaves better in both response time and QoS, thus proving that DCRM is good at shared resource management in mobile media cloud computing.     

基于公共云的 HPC 集群实现及自动伸缩闲时计算研究

对于HPC用户来说,计算成本是迁云所考虑的重要因素之一,阿里云上提供的抢占式实例,是一种按需实例,旨在降低使用公共云计算资源成本,抢占式实例市场价格是波动的,通常远低于正常的按需实例,甚至达到正常按需实例的一折。抢占式实例一般会在创建时为用户保留一段最短时间,过后有可能会被释放,所以一般适用于无状态的应用场景。提出在公共云上的自动伸缩策略,其面向通用的HPC集群调度器,基于用户的应用软件类型、提交作业规律以及用户对性能和成本等多方面需求,自动在云上部署扩容计算资源,控制成本。对用户来说,可以做到"only pay for what you want and what you use"。基于公共云上丰富的资源规格类型和售卖方式,利用自动伸缩服务,抢占式实例,断点续算等技术可以配置低成本的公共云上HPC自动伸缩方案:用户提交作业的同时可以指定成本上限,自动伸缩服务自动在低于此成本的前提下寻找和扩容抢占式计算资源,同时利用断点续算功能保证作业在计算资源切换的时候可以继续运算。最后,通过LAMMPS和GROMACS两个高性能应用实例验证了该策略的可行性和有效性。     

Dynamic application placement in the Mobile Cloud Network

To meet the challenges of consistent performance, low communication latency, and a high degree of user mobility, cloud and Telecom infrastructure vendors and operators foresee a Mobile Cloud Network that incorporates public cloud infrastructures with cloud augmented Telecom nodes in forthcoming mobile access networks. A Mobile Cloud Network is composed of distributed cost- and capacity-heterogeneous resources that host applications that in turn are subject to a spatially and quantitatively rapidly changing demand. Such an infrastructure requires a holistic management approach that ensures that the resident applications’ performance requirements are met while sustainably supported by the underlying infrastructure. The contribution of this paper is three-fold. Firstly, this paper contributes with a model that captures the cost- and capacity-heterogeneity of a Mobile Cloud Network infrastructure. The model bridges the Mobile Edge Computing and Distributed Cloud paradigms by modelling multiple tiers of resources across the network and serves not just mobile devices but any client beyond and within the network. A set of resource management challenges is presented based on this model. Secondly, an algorithm that holistically and optimally solves these challenges is proposed. The algorithm is formulated as an application placement method that incorporates aspects of network link capacity, desired user latency and user mobility, as well as data centre resource utilisation and server provisioning costs. Thirdly, to address scalability, a tractable locally optimal algorithm is presented. The evaluation demonstrates that the placement algorithm significantly improves latency, resource utilisation skewness while minimising the operational cost of the system. Additionally, the proposed model and evaluation method demonstrate the viability of dynamic resource management of the Mobile Cloud Network and the need for accommodating rapidly mobile demand in a holistic manner.     

Adaptable,model-driven security engineering for SaaS cloud-based applications

Software-as-a-service (SaaS) multi-tenancy in cloud-based applications helps service providers to save cost, improve resource utilization, and reduce service customization and maintenance time. This is achieved by sharing of resources and service instances among multiple “tenants” of the cloud-hosted application. However, supporting multi-tenancy adds more complexity to SaaS applications required capabilities. Security is one of these key requirements that must be addressed when engineering multi-tenant SaaS applications. The sharing of resources among tenants—i.e. multi-tenancy—increases tenants’ concerns about the security of their cloud-hosted assets. Compounding this, existing traditional security engineering approaches do not fit well with the multi-tenancy application model where tenants and their security requirements often emerge after the applications and services were first developed. The resultant applications do not usually support diverse security capabilities based on different tenants’ needs, some of which may change at run-time i.e. after cloud application deployment. We introduce a novel model-driven security engineering approach for multi-tenant, cloud-hosted SaaS applications. Our approach is based on externalizing security from the underlying SaaS application, allowing both application/service and security to evolve at runtime. Multiple security sets can be enforced on the same application instance based on different tenants’ security requirements. We use abstract models to capture service provider and multiple tenants’ security requirements and then generate security integration and configurations at runtime. We use dependency injection and dynamic weaving via Aspect-Oriented Programming (AOP) to integrate security within critical application/service entities at runtime. We explain our approach, architecture and implementation details, discuss a usage example, and present an evaluation of our approach on a set of open source web applications.     

Performance evaluation of cloud computing platforms using statistical methods

Cloud computing is a very attractive research topic. Many studies have examined the infrastructure as a service and software as a service aspects of cloud computing; however, few studies have focused on platform as a service (PaaS). According to recent reports, demand for enterprise PaaS solutions will increase continuously. However, different sectors require different types of PaaS applications and computing resources. Therefore, an evaluation and ranking framework for PaaS solutions according to application needs is required. To address this need, this study presents the most essential aspects of PaaS solutions and provides a framework for evaluating the performance of PaaS providers. It also proposes a suitable set of benchmarking algorithms that can help determine the most appropriate PaaS provider based on different resource needs and application requirements. Performance evaluations of three well-known cloud computing PaaS providers were conducted using the analytic hierarchy process and the logic scoring of preference methods.     

基于虚拟机部署策略的云平台容错即服务方法

针对如何充分利用云基础架构层资源,满足上层云应用系统租户对应用系统容错的需求多样性和高可靠性要求的问题,提出一种面向租户和云服务提供商的、基于虚拟机部署策略的云平台容错即服务方法。该方法根据租户的特定容错需求适配适合的容错方法及容错级别,据此计算并最优化云服务提供商的收益和资源使用量,在此基础上对提供容错服务的虚拟机进行优化部署,充分利用底层虚拟机资源为租户的云应用系统提供更为可靠的容错服务。实验结果表明,所提方法能够在保障云服务提供商收益的基础上,为多租户云应用系统实现更灵活且可靠性更高的容错服务。     

Elastic resource provisioning in hybrid mobile cloud for computationally intensive mobile applications

The paper addresses the integration of hybrid cloud with mobile applications. The challenge about hybrid mobile cloud resource provisioning is the trade-offs between energy consumption, performance provided to users and how resources, such as processing power and network, are being utilized. The proposed elastic hybrid mobile cloud resource provisioning model is jointly optimized to improve mobile user experience within the constraints of available resources and user QoS requirement. The paper presents the system utility of hybrid cloud system involving local cloud and public cloud infrastructure. From the perspectives of both mobile applications and cloud providers, the proposed system utility is optimized to improve the performance of mobile applications and the utilization of cloud resources. The proposed elastic hybrid mobile cloud resource provisioning algorithm includes two sub-algorithms. To evaluate and validate performance of the proposed algorithm, a series of experiments are conducted. The comparison results and analyses are discussed. The experimental results show the improvement to previous works.