A survey of Cloud monitoring tools: Taxonomy,capabilities and objectives

The efficient management of Cloud infrastructure and deployments is a topic that is currently attracting significant interest. Complex Cloud deployments can result in an intricate layered structure. Understanding the behaviour of these hierarchical systems and how to manage them optimally are challenging tasks that can be facilitated by pervasive monitoring. Monitoring tools and techniques have an important role to play in this area by gathering the information required to make informed decisions. A broad variety of monitoring tools are available, from general-purpose infrastructure monitoring tools that predate Cloud computing, to high-level application monitoring services that are themselves hosted in the Cloud. Surveying the capabilities of monitoring tools can identify the fitness of these tools in serving certain objectives. Monitoring tools are essential components to deal with various objectives of both Cloud providers and consumers in different Cloud operational areas. We have identified the practical capabilities that an ideal monitoring tool should possess to serve the objectives in these operational areas. Based on these identified capabilities, we present a taxonomy and analyse the monitoring tools to determine their strength and weaknesses. In conclusion, we present our reflections on the analysis, discuss challenges and identify future research trends in the area of Cloud monitoring.     

DARGOS: A highly adaptable and scalable monitoring architecture for multi-tenant Clouds

One of the most important features in Cloud environments is to know the status and the availability of the physical resources and services present in the current infrastructure. A full knowledge and control of the current status of those resources enables Cloud administrators to design better Cloud provisioning strategies and to avoid SLA violations. However, it is not easy to manage such information in a reliable and scalable way, especially when we consider Cloud environments used and shared by several tenants and when we need to harmonize their different monitoring needs at different Cloud software stack layers. To cope with these issues, we propose Distributed Architecture for Resource manaGement and mOnitoring in cloudS (DARGOS), a completely distributed and highly efficient Cloud monitoring architecture to disseminate resource monitoring information. DARGOS ensures an accurate measurement of physical and virtual resources in the Cloud keeping at the same time a low overhead. In addition, DARGOS is flexible and adaptable and allows defining and monitoring new metrics easily. The proposed monitoring architecture and related tools have been integrated into a real Cloud deployment based on the OpenStack platform: they are openly available for the research community and include a Web-based customizable Cloud monitoring console. We report experimental results to assess our architecture and quantitatively compare it with a selection of other Cloud monitoring systems similar to ours showing that DARGOS introduces a very limited and scalable monitoring overhead.     

QoS约束的云经济资源管理模型研究

提供用户满意的、具有QoS约束的云计算应用是云计算面临的一大难题。提出了以商品市场为原型的云计算经济资源管理模型,其通过云用户与供应商的SLA协商,实现应用服务层QoS到资源设备层QoS的映射,最后利用效用函数的管理策略实现资源的优化调度。     

低资源成本的云计算自适应实时资源监控研究

分布式云计算的云端数据量巨大,而云端资源监控则需对大数据采样,因而产生大量的计算开销与资源消耗,对此,提出了一种两阶段的自适应监控方案。首先,在训练阶段,采集适量的资源样本,计算其资源变化相关的阈值参数,并计算资源变化的质量值;然后,在自适应监控阶段,使用训练阶段的算法,根据不同的资源变化剧烈程度更新采样周期,从而实现动态的采样周期调节。试验结果证明,与静态监控算法相比,该算法具有较好的监控性能,同时具有较低的资源开销。     

异构云平台性能监控与分析研究

云计算是近年来兴起的一种新型的计算和服务模式,它采用多租户策略和按需付费计算模型,弹性地向用户提供看似无限的计算和存储资源。当前,云计算已经成为互联网环境下新型计算模式研究的焦点和热点。但是,云计算刚刚处于起步阶段,还没有形成统一的、公认的云计算标准。如何比较和评价多个不同公司、不同类型云平台的性能,为用户选择云平台提供依据,成为现在云计算性能评估的一个迫切需要解决的问题。针对以上问题,设计并构建了跨云平台的性能监控系统,该系统根据用户性能需求定义为多种云平台提供实时性能数据监控,进行跨云平台的性能比较分析,并且图形化地展示给用户。     

云计算的基础结构设计和云应用服务

云计算是基于云结构的信息技术资源消费与利用系统,也是基于互联网的链路传输系统与终端服务系统的集成应用模型。典型的云服务模型包括Saa S/Aaa S、Iaa S、Paa S、Daa S,这些参考模型同时组成云系统应用服务基础架构。云计算开发标准与规则是云结构发展与云数据库的开发参考基准,也是云管理与云进化的参考结构。云系统服务质量关系云应用发展,而云管理的服务配置与监视服务提供反馈信息。云应用服务维护运行与数据安全是云安全提供的基础保障服务。云备份作为领导性企业云的高级服务功能应能适应云灵活多变性与容灾免灾设计,文中深入分析云数据备份技术应用与程序算法,细述实用数据应用处理方法。动态开发环境是企业云对云计算开放研究平台,在全球推进云环境的开发测试。云计算风险分析与管理同时促进云应用进化。     

基于车联网应用的云平台任务调度算法

在云平台车联网优化调度问题的研究中,车联网具有多用户、多业务、高并发等特点.为了保障车联网应用在云平台上快速、稳定和可靠的运行,在云计算的基础上,提出一种基于车联网应用的MCT-LB-GSA(Minimum Completion Time-Load Balance-Greedy Scheduling Algorithm)任务调度算法.算法以虚拟机资源的当前负载作为约束条件,依照贪心策略将任务调度到当前负载较轻且具有最小任务完成时间上的虚拟机资源上.在CloudSim环境下进行了仿真,结果表明,改进算法在保证最优任务调度跨度的同时也有效地实现了资源负载均衡,提高了资源利用率.     

Cost-benefit analysis of an SLA mapping approach for defining standardized Cloud computing goods

Due to the large variety in computing resources and, consequently, the large number of different types of service level agreements (SLAs), computing resource markets face the problem of a low market liquidity. Restricting the number of different resource types to a small set of standardized computing resources seems to be the appropriate solution to counteract this problem. Standardized computing resources are defined through an SLA template. An SLA template defines the structure of an SLA, the service attributes, the names of the service attributes, and the service attribute values. However, since existing research results have only introduced static SLA templates so far, the SLA templates cannot reflect changes in user needs and market structures. To address this shortcoming, we present a novel approach of adaptive SLA matching. This approach adapts SLA templates based on SLA mappings of users. It allows Cloud users to define mappings between a public SLA template, which is available in the Cloud market, and their private SLA templates, which are used for various in-house business processes of the Cloud user. Besides showing how public SLA templates are adapted to the demand of Cloud users, we also analyze the costs and benefits of this approach. Costs are incurred every time a user has to define a new SLA mapping to a public SLA template due to its adaptation. In particular, we investigate how the costs differ with respect to the public SLA template adaptation method. The simulation results show that the use of heuristics within adaptation methods allows balancing the costs and benefits of the SLA mapping approach.     

GMonE: A complete approach to cloud monitoring

The inherent complexity of modern cloud infrastructures has created the need for innovative monitoring approaches, as state-of-the-art solutions used for other large-scale environments do not address specific cloud features. Although cloud monitoring is nowadays an active research field, a comprehensive study covering all its aspects has not been presented yet. This paper provides a deep insight into cloud monitoring. It proposes a unified cloud monitoring taxonomy, based on which it defines a layered cloud monitoring architecture. To illustrate it, we have implemented GMonE, a general-purpose cloud monitoring tool which covers all aspects of cloud monitoring by specifically addressing the needs of modern cloud infrastructures. Furthermore, we have evaluated the performance, scalability and overhead of GMonE with Yahoo Cloud Serving Benchmark (YCSB), by using the OpenNebula cloud middleware on the Grid’5000 experimental testbed. The results of this evaluation demonstrate the benefits of our approach, surpassing the monitoring performance and capabilities of cloud monitoring alternatives such as those present in state-of-the-art systems such as Amazon EC2 and OpenNebula.     

“云计算”概念的分析研究——协同智能计算系统的一个特例

本文作者拟从云计算提出的源头及其可能的新发展两个方面来对它做一个系统的分析和展望。旨在为云计算理论与技术的普及和提高开辟新途径。自2006谷歌101项目公开提出云计算（cloud computing）至今,信息产业界说它是一种新的商业模式,信息技术界却说它是软件服务（SaaS）、平台服务（PaaS）和基础设施服务（IaaS）三个不同层次的互联网技术的融合,通信与计算机科学界则说它是一种有别于既有范式的新的计算模式,另外还有各种各样的具体说法。然而就是缺乏一个系统周全而又简明扼要的理论描述或定义,尤其缺乏英汉双语对照的解释。笔者认为,这对云计算理论与技术的普及和提高均为不利。     

基于社区模型的云资源监测

云计算是一种新兴的商业计算模型,资源性能和负载监测是其重要的研究点。分析了传统的分布式计算资源监测策略,针对云计算环境,引入社区模型设计了层次式社区监测,提出了基于敏感因子的监测方法,以解决全局监控可能会带来的数据繁冗和无效问题。仿真实验表明,模型和策略在理论上是合理的,在效率上较传统监测系统有一定的提高。     

云测试综述

针对云计算的定义及云计算的特点做出分析。认为云计算有着良好的发展趋势,并在次基础上展开对云测试的讨论。主要涉及云测试的特点、优点、应用现状,云测试平台以及云测试技术,并探讨云测试的对现有测试服务模式的影响及存在的缺陷,预测云测试的发展趋势。     

一种物流云系统的架构方法

为实现物流领域云服务,满足物流产业相关企业对云服务个性化,快速开发和多样化应用的需求,在云计算机应用架构的原理基础上,根据现代物流企业对物流云服务的特点,提出了一种集底层软硬件管理平台、云服务应用环境平台、云服务开发平台等为一体的物流云PaaS平台架构.讨论和分析了该架构的设计原理和实施细节,为进一步搭建物流领域多样化的SaaS应用提供了快速开发、统一协调和调度的平台,进而满足用户“一站式”个性化服务需求.     

云测试综述

针对云计算的定义及云计算的特点做出分析。认为云计算有着良好的发展趋势,并在次基础上展开对云测试的讨论,主要涉及云测试的特点、优点、应用现状,云测试平台以及云测试技术,并探讨云测试的对现有测试服务模式的影响及存在的缺陷,预测云测试的发展趋势。     

开源云计算技术应用

本文介绍开源云计算技术Openstack。通过对我院现有的虚拟化方案改进,采用Openstack逐步构建属于企业内部自己的私有云平台。为企业提供云存储和云虚拟化服务器平台。     

浅析云计算安全技术

在互联网技术高速发展的今天,云计算已成为热门技术。在云计算中,一切资源放在云端,故云安全问题显得尤为突出。介绍了云计算概念,阐述了云计算面临的安全挑战,并从技术层面提出解决云安全的防范措施,打造一个用户放心的云计算安全平台。     

基于数字化校园的云安全方案

阐述数字化校园的历史背景以及其面临的信息安全问题。并详细介绍当今云计算的基本内容以及其中的安全问题。基于数字化校园的建设,提出一种云计算安全的解决方案．以解决当数字化校园搭载在云上时所面临的安全问题。     

云安全面临的挑战及其解决策略

作为一种新型的商业计算模式,云计算的应用和推广会给用户带来巨大的经济效益,与此同时,传统的安全模式已不适用于云环境中,其面临的安全挑战错综复杂,需要技术、标准、监管、法律等各方面结合起来,多管齐下才能解决云计算的安全问题。     

云计算技术的应用及发展趋势综述

云计算是当前信息产业的热点领域,具有广阔的应用前景。从定义、特征和模型3个方面,介绍了云计算的基本概念,对云计算的虚拟化、资源管理、安全性、分布式编程、数据存储等核心技术和云计算标准进行综述,并对分析主流的云计算技术应用及发展趋势进行分析。