数据中心服务器资源监控系统的设计与实现

服务器是数据中心的核心组成部分,对服务器中的资源进行监控是保障数据中心正常运行的必要手段之一。随着数据中心设备数量和数据存储规模的不断增加,直观、高效地对服务器资源进行监控已经是一个亟待解决的问题。设计并实现一套服务器资源监控系统,实时监控服务器的计算、信息、服务资源运行状态,实现海量监控数据的实时处理和统计分析,满足数据中心服务器资源的全面监控和安全保障要求,并展示了监控系统的运行效果。     

基于利用率提升的服务器虚拟化技术应用探讨

企业的IT基础设施越来越多,由于服务器设备的增加,导致企业的中心机房管理难度越来越大,在这种情况下服务器虚拟化技术应运而生,服务器虚拟化技术能极大的提高企业数据利用率和管理水平.文章重点介绍了服务器虚拟化技术及其在企业数据中心的应用.     

VMware“共融”理念

正VMware通过"共融",能够让用户跨现有数据中心和云计算资源,实现在IT演进过程中传统应用、云计算和移动应用的高效部署及管理。让IT管理者高效满足IT安全和合规要求,同时为业务创新提供高效的自我部署管理功能和高弹性IT平台,且不再需要在"传统"和"新一代"之间做取舍。此外,VMware的"共融"还能够让用户在IT演进过程中,灵活、动态根据业务需求选择管理IT资源,以优化IT资源利用率。在"共融"的理念下,VMware软件定义数据中心为用户提供包括软件定义计算、软件定义存储(Virtual SAN)     

异构环境远程运维

目前,专业机房内存在大量服务器、工控机、路由器、交换机等IT设备。品牌众多、物理机和虚拟机共存,随着业务的不断拓展,服务器等关键设备将大量增加,如何对数据中心设备进行高效管理,对用户来说显得越来越重要。本文就向大家介绍异构环境下数据中心的远程运维经验。     

选择生产应用虚拟化服务器的重要考核因素

正生产应用虚拟化挑战在从传统的IT架构管理到IT服务自动化演进过程中,生产应用虚拟化是数据中心虚拟化过程的关键环节,未来12个月将成为中国用户生产应用虚拟化的强增长点。随着生产应用和业务关键型负载部署虚拟化在中国市场快速的普及,服务器的管理模式、资源利用率、资源动态配置,以及以负载为核心的集中统一管理功能,对于企业来说也愈发重要。在这种情况下,传统服务器的并行处理能力、内存空间、数据读取效率限制了生产应用虚拟化过程中     

下一代数据中心之关键:网络虚拟化

数据中心在面临预算不断紧缩和数据日益猛增的严峻压力下,越来越多的客户纷纷寄希望于虚拟数据中心,以达到提高硬件利用率、实现节能、提高数据的可用性和IT服务水平的目的。虚拟化数据中心被称为下一代数据中心（或DC2．0）,而支持其得以实现的核心技术包括：服务器虚拟化、存储虚拟化以及网络虚拟化。     

浅谈数据中心网络架构的发展

数据中心是数据大集中而形成的集成IT应用环境,是数据处理、数据存储和数据交换的中心。随着企业信息化的深入和新技术的广泛使用,传统数据中心已经无法满足后数据中心时代的高效、敏捷、易维护的需求。新一代数据中心解决方案,目标是在以太网和IP技术的基础上,实现数据中心基础网络架构的统一,以及安全策略的统一部署和数据中心资源的统一管理,以帮助用户简化传统数据中心的基础架构、加固核心数据的保护、优化数据中心的应用性能。为用户提供即可靠安全,又高效敏捷的的基于统一交换架构的新一代数据中心!     

DSView 3平台与LANDesk服务器无缝集成Avocent、LANDesk产品实现整合

7月2日,Avocent公司在北京宣布,公司用于访问和控制数据中心设备的DSView 3管理软件平台已与LANDesk服务器管理器(LANDesk Server Manager)实现集成,后者是一种用于管理和保护数据中心及非数据中心服务器的企业级软件解决方案.这两项解决方案的融合可以实现全面的带内和带外服务器及相关IT基础设施管理工作,确保最优的服务器和数据中心性能及可用性.DSView 3.4.1解决方案随之问世,真正实现了IT资源的统一管理.     

CloudEngine构筑未来数据中心网络

随着IT用户对高性能计算、数据挖掘、数据存储的需求不断增长,云数据中心的规模变得越来越大。面对激增的云计算心用、服务器和存储设备,传统的数据中心网络建设模式已经无法适应上述变化,迫切需要一张新型的“云”网络,不仅能够提供超强的扩展能力、资源按需分配能力,而且简单易维护。     

校园网虚拟化数据中心的构建及其应用

在数字化程度越来越高、信息化越来越深入的校园网环境中,传统的以物理服务器堆砌构建的数据中心,存在着管理复杂、资源利用率低、备份困难等问题,难以满足学校信息化不断发展的需要。数据中心向虚拟化、动态化转变,通过构建虚拟化统一管理平台,整合和动态调配物理服务器资源,大大提高了服务器的使用效率和管理执行力,数据和业务安全保障也变得简单易行。     

融合架构云服务器体系结构和关键技术

针对云计算、大数据等应用对异构资源管理和聚合的需求,提出了一种融合架构云服务器体系结构及其关键支撑技术。融合架构云服务器利用硬件资源池化技术,实现计算、存储、网络、供电、制冷和管理模块的解耦与融合重构,具有高密度、低功耗、易扩展、易管理,易维护特点,兼具横向扩展和纵向扩展优势,可优化系统部署、运维和能耗成本,显著降低总体拥有成本（TCO）。在金融、电信和互联网行业的实际应用案例表明,融合架构云服务器功耗降低超过15%,总体拥有成本降低近15%,为云计算、大数据等应用提供了更具性能功耗比优势的IT基础设施设计方案。     

面向云计算的数据中心网络体系结构设计

近年来,云计算技术的蓬勃发展为整个IT行业带来了巨大变革.传统数据中心网络拓扑构建方式及网络层控制平面的运行机制存在固化性,已经难以满足新形势下日益增长的高性能及高性价比需求,并且无法支持云环境下更加灵活的按带宽租赁数据中心网络的运营方式.因此,提出了一种通过低造价的可编程交换机来构建具有高连通性的非树状数据中心网络的方式,并设计了可编程交换机与服务器2.5层代理协同工作的基于凸优化的虚拟网络带宽控制管理机制,从而提供足够的灵活性以对资源虚拟化技术提供更好的支持.实验表明,新型体系结构在降低构建成本的同时大幅提高了数据中心网络的吞吐量并提供了更加灵活的网络带宽分配机制.     

Secure multi-server-aided data deduplication in cloud computing

Cloud computing enables on-demand and ubiquitous access to a centralized pool of configurable resources such as networks, applications, and services. This makes that huge of enterprises and individual users outsource their data into the cloud server. As a result, the data volume in the cloud server is growing extremely fast. How to efficiently manage the ever-increasing datum is a new security challenge in cloud computing. Recently, secure deduplication techniques have attracted considerable interests in the both academic and industrial communities. It can not only provide the optimal usage of the storage and network bandwidth resources of cloud storage providers, but also reduce the storage cost of users. Although convergent encryption has been extensively adopted for secure deduplication, it inevitably suffers from the off-line brute-force dictionary attacks since the message usually can be predictable in practice. In order to address the above weakness, the notion of DupLESS was proposed in which the user can generate the convergent key with the help of a key server. We argue that the DupLESS does not work when the key server is corrupted by the cloud server. In this paper, we propose a new multi-server-aided deduplication scheme based on the threshold blind signature, which can effectively resist the collusion attack between the cloud server and multiple key servers. Furthermore, we prove that our construction can achieve the desired security properties.     

常德烟草机械私有云IT流程自动化设计

本文研究常德烟草机械私有云IT 运维管理平台中IT 流程自动化的设计思想后,实现了对存储设备、UPS、网 络设备、服务器、客户机、网络基础结构、虚拟化平台、数据库、应用和中间件、账户系统和邮件系统等常用IT 流程的自动化,有 效提高了IT管理效率。     

视频监控联网系统中云媒体服务器的架构研究与设计

在视频监控联网系统中,传统独立部署的媒体服务器存在因并发导致的I／O负载严重、网络带宽不足以及负载分配不均等诸多问题。云媒体服务器将整体的调度、统一的管理和性能的优化融为一体,可实现资源利用的最优化和性能提升的最大化。本文研究并设计云媒体服务器的整体架构,基于此架构对服务器集群中媒体服务器的负载评判方法进行了设计。实验结果表明,相对于传统媒体服务架构的视频监控系统,云媒体服务器可较好地实现对业务的负载均衡、容错切换等功能,可以应对媒体服务器中对视频源的突发高并发访问。     

云计算集群服务器系统监控方法的研究

随着云计算技术越来越多地应用到信息产业的各个领域,云计算环境下集群服务器系统的监控与管理的需求越来越大。云计算下的集群服务器系统主要是通过一系列基于分布式架构的服务器集群组成,其下的服务器数量可能多达上万台。要管理好数量如此大的云计算集群服务器系统,保证其高性能运行,必然需要一套有效的云计算集群监控系统对其进行监测与调控。但是,传统的集群监测系统存在一些不足与弊端。本文对于云计算集群系统的高性能监测调度方案进行了研究,从云监控系统的架构、数据采集、负载均衡调度方面进行了探讨,构建了一个保证云计算集群系统高性能运营的云系统方案。     

New scoring formula to rank hypervisors’ performance complementing with statistical analysis using DOE

Hypervisors enable cloud computing model to provide scalable infrastructures and on-demand access to computing resources as they support multiple operating systems to run on one physical server concurrently. This mechanism enhances utilization of physical server thus reduces server count in the data center. Hypervisors also drive the benefits of reduced IT infrastructure setup and maintenance costs along with power savings. It is interesting to know different hypervisors’ performance for the consolidated application workloads. Three hypervisors ESXi, XenServer, and KVM are carefully chosen to represent three categories full virtualized, para-virtualized, and hybrid virtualized respectively for the experiment. We have created a private cloud using CloudStack. Hypervisors are deployed as hosts in the private cloud in the respective clusters. Each hypervisor is deployed with three virtual machines. Three applications web server, application server, and database servers are installed on three virtual machines. Experiments are designed using Design of Experiments (DOE) methodology. With concurrently running virtual machines, each hypervisor is stressed with the consolidated real-time workloads (web load, application load, and OLTP) and important system information is gathered using SIGAR framework. The paper proposes a new scoring formula for hypervisors’ performance in the private cloud for consolidated application workloads and the accuracy of the results are complemented with sound statistical analysis using DOE.     

Lightweight and Informative Traffic Metrics for Data Center Monitoring

In recent years, thousands of commodity servers have been deployed in Internet data centers to run large scale Internet applications or cloud computing services. Given the sheer volume of data communications between servers and millions of end users, it becomes a daunting task to continuously monitor the availability, performance and security of data centers in real-time operational environments. In this paper, we propose and evaluate a lightweight and informative traffic metric, streaming frequency, for network monitoring in Internet data centers. The power-series based metric that is extracted from the aggregated IP traffic streams, not only carries temporal characteristics of data center servers, but also helps uncover traffic patterns of these servers. We show the convergence and reconstructability properties of this metric through theoretical proof and algorithm analysis. Using real data-sets collected from multiple data centers of a large Internet content provider, we demonstrate its applications in detecting unwanted traffic towards data center servers. To the best of our knowledge, this paper is the first to introduce a streaming metric with a unique reconstruction capability that could aid data center operators in network management and security monitoring.     

服务器应用现状与服务器虚拟化分析

随着信息技术的高速发展,越来越多的企业有了一定规模的网络应用,企业信息化建设水平的不断提高,数据中心服务器对日常工作的影响越来越大,企业中不断增加的服务器设备在方便工作的同时也带来了管理复杂化、成本扩大化的问题。而服务器虚拟化技术简化了服务器管理系统,将多种应用整合,提高了服务器使用效率。本文就企业数据中心服务器应用现状、虚拟化技术的应用、服务器虚拟化面临的问题以及服务器虚拟化技术发展的意义展开。     

\upmu \mathrm{DC}^2: unified data collection for data centers

Modern data centers are playing an important role in a world full of information and communication technologies (ICTs). Many efforts have been paid to build a more efficient, cleaner data center for economic, social, and environmental benefits. This objective is being enabled by emerging technologies such as cloud computing and software-defined networking (SDN). However, a data center is inherently heterogeneous, consisting of servers, networking devices, cooling devices, power supply devices, etc., resulting in daunting challenges in its management and control. Previous approaches typically focus on only a single domain, for example, traditional cloud computing for server resource (e.g., computing resource and storage resource) management and SDN for network management. In a similar context of networking device heterogeneity, network function virtualization has been proposed to offer a standard abstract interface to manage all networking devices. In this research, we take the challenge of building a suit of unified middleware to monitor and control the three intrinsic subsystems in a data centre, including ICT, power, and cooling. Specifically, we present \(\upmu \mathrm{DC}^2\) , a unified scalable IP-based data collection system for data center management with elevated extensibility, as an initial step to offer a unified platform for data center operations. Our system consists of three main parts, i.e., data-source adapters for information collection over various subsystems in a data center, a unified message bus for data transferring, and a high-performance database for persistent data storage. We have conducted performance benchmark for the key building components, namely messaging server and database, confirming that our system is scalable for a data center with high device density and real-time management requirements. Key features, such as configuration files, dynamical module loading, and data compression, enhance our implementation with high extensibility and performance. The effectiveness of our proposed data collection system is verified by sample applications, such as, traffic flow migration for load balancing, VM migration for resource reservation, and server power management for hardware safety. This research lays out a foundation for a unified data centre management in future.