云计算数据中心网络的端到端流量计算

云计算数据中心网络的流量特征是研究和设计云计算网络的基础,现有的流量测量研究方法通常要求交换机支持额外功能模块或具备可编程能力,而目前大多数云计算数据中心网络的交换机并不满足此要求。提出一种基于网络层析技术的端到端流量推理算法,仅使用交换机普遍支持的SNMP(简单的网络管理协议)数据,就能快速准确地计算出端到端的流量信息。并通过仿真实验与已有的网络层析算法进行比较,结果表明新算法更适用于大规模的云计算数据中心网络,可以在较短的时间内得到更准确的计算结果,从而为云计算网络的设计和研究提供了重要的参考依据。     

基于可编程数据平面的PFC算法实现

当前高吞吐量、超低延迟的高性能无损数据中心网络成为研究的热点。传统TCP/IP协议是为广域网设计的,在高速网络条件下(特别是随着10 Gb/s的网络接口的普及)会存在I/O瓶颈问题;远程直接数据存取技术RDMA(remote direct memory access)是为了解决网络传输中终端主机的数据处理延迟、降低CPU负载而产生的,最早应用在高性能计算领域。RDMA技术基于优先级流量控制PFC算法实现了无损传输网络。首先介绍了可编程数据平面技术和高性能数据中心网络的研究现状,并基于可编程数据平面以软件定义的方式实现了PFC算法,进而实现了可编程的无损数据中心网络,并在仿真网络环境下对实现的PFC算法进行了性能测试。实验结果显示在可编程数据平面下实现的PFC算法达到了无损传输的目标。同时证明了可编程数据平面技术在高性能数据中心网络的实现中可以发挥巨大作用,相对于传统的网络架构,可编程数据平面技术由于采用了软件定义的方式,因此更加灵活、高效。     

面向云计算的数据中心网络体系结构设计探析

最近几年来,云计算技术蓬勃发展起来,传统的数据中心网络运行机制存在一定的固化性,无法满足现有形势下对高性能和高性价比的需求,而且也无法支持目前云环境下的灵活的根据带宽租赁数据中心的运营方式.所以,构建一种低造价、灵活性强和高连通性的非树状数据中心网络十分必要.     

面向云计算的数据中心网络体系结构设计

利用云计算技术构建分层次的数据中心网络体系结构,在互联网接入层接入互联网数据资源,通过核心汇聚层连接高性能路由器与接入交换机,并将接入层传来的数据传输业务接入层的服务器。在业务接入层中设计网络带宽分配方法,利用运营管理层实现数据中心网络体系结构日常维护功能。     

面向流数据处理的数据中心网络拓扑研究

作为网络服务的底层硬件平台,数据中心的网络拓扑结构及工作机制对于上层的应用服务的性能具有决定性的作用。传统数据中心采用的树形分层结构已经难以满足新一代网络服务的需求,特别是在数据流的应用上传统的网络结构更加无法适用。因此,提出一种针对流数据处理的新型模块化数据中心拓扑构建方式并设计以服务器为中心的路由算法。该方案采用低造价的商业级交换机构建数据中心网络,在降低构建造价的同时提高网络的吞吐量及容错性能。实验表明,所提出的拓扑结构及路由算法在流数据处理上的网络吞吐量表现优于传统的树形结构并且具有更好的容错性能。     

主动网络的研究进展与展望

主动网络是一种新型的网络体系结构,与传统网络体系结构相比,主动网络增加了路由器或交换机等网络中间节点的计算能力和可编程能力,从而为用户提供了方便、灵活的网络服务定制能力.随着网络应用的日益增长和多样化,用户要求网络能够提供灵活、可扩展的网络服务.该文全面总结了主动网络的研究现状,结合主动网络技术的研究新动向和主动网络的应用,给出了今后的研究方向.     

网络虚拟化:可感知虚拟机的网络

虚拟化网络作为数据中心虚拟化的核心之一,其在数据中心虚拟化的过程中具有不可或缺的作用,对虚拟化数据中心的运营管理影响重大。网络虚拟化是虚拟化技术的重要组成部分,其虚拟机感知能力是新数据中心的必备能力。对软件虚拟交换机和支持虚拟化功能的物理交换机这两种虚拟化网络技术进行详细分析和比较。     

可编程虚拟路由器关键技术与原型系统

未来网络创新试验床和数据中心网络迫切需要具有虚拟化和可编程能力的路由器设备的支持,可编程虚拟路由器是构建未来网络试验床和数据中心网络的核心设备,逐渐成为研究热点.然而,可编程虚拟路由器在设计与实现中面临一系列挑战,其关键技术和原型系统研究对于可编程虚拟路由器研制具有十分重要的意义.文中从未来网络试验床和数据中心网络的需求出发,分析了可编程虚拟路由器的特性要求,归纳了可编程虚拟路由器在虚拟化、可编程性和高性能数据包转发等方面存在的技术挑战,并分类讨论了相关关键技术研究进展.论文最后评价和比较了国内外设计实现的可编程虚拟路由器原型系统,并讨论了可编程虚拟路由器中有待进一步解决的问题.     

基于改进胖树结构的数据中心网络设计

随着云服务的广泛应用,部署云服务的数据中心网络向着大型化,多路径的结构发展。胖树运用简单的拓扑模型为数据中心提供出色的聚合带宽性能。本文提出一种基于改进胖树结构的新型数据中心网络,该网络利用边缘交换机直接连接核心交换机,并为其设计了错误避免路由算法,为数据中心网络提供简单高效的路由生成办法。通过计算得知改进型胖树结构的数据中心网络有更短的路径,同时能简单快速构建路由表。     

移动运营商营业厅接入网络改造方案设计

根据移动运营商对于营业厅网络的实际需求,为解决目前网络存在的带宽不足、单点故障、网络架构不统一、带宽无法扩容等问题,利用高性价比EAPS以太环网技术,实现各种业务数据流的快速保护倒换,协同高中低端交换机推出整体的环网解决方案。本方案遵循网络带宽扩容性、提供网络冗余性、网络业务融合性等原则,通过采用目前在以太网建设技术最先进的城域以太环网技术:EAPS协议,可提供网络带宽的灵活拓展、提供网络的冗余性、实现多业务运营能力、提高网络的高维护性等优点。     

Exploring High-Performance Architecture for Data Center Networks

As a critical infrastructure of cloud computing, data center networks (DCNs) directly determine the service performance of data centers, which provide computing services for various applications such as big data processing and artificial intelligence. However, current architectures of data center networks suffer from a long routing path and a low fault tolerance between source and destination servers, which is hard to satisfy the requirements of high-performance data center networks. Based on dual-port servers and Clos network structure, this paper proposed a novel architecture to construct high-performance data center networks. Logically, the proposed architecture is constructed by inserting a dual-port server into each pair of adjacent switches in the fabric of switches, where switches are connected in the form of a ring Clos structure. We describe the structural properties of in terms of network scale, bisection bandwidth, and network diameter. architecture inherits characteristics of its embedded Clos network, which can accommodate a large number of servers with a small average path length. The proposed architecture embraces a high fault tolerance, which adapts to the construction of various data center networks. For example, the average path length between servers is 3.44, and the standardized bisection bandwidth is 0.8 in (32, 5). The result of numerical experiments shows that enjoys a small average path length and a high network fault tolerance, which is essential in the construction of high-performance data center networks.     

Improving performance by network-aware virtual machine clustering and consolidation

Modern data center consists of thousands of servers, racks and switches. Complicated structure means it requires well-designed algorithms to utilize resources of data centers efficiently. Current virtual machine scheduling algorithms mainly focus on the initial allocation of virtual machines based on the CPU, memory and network bandwidth requirements. However, when tasks finished or lease expired, related virtual machines would be deleted from the system which would generate resource fragments. Such fragments lead to unbalanced resource utilization and decline of communication performance. This paper investigates the network influence on typical applications in data centers and proposed a self-adaptive network-aware virtual machine clustering and consolidation algorithm to maintain an optimal system-wide status. Our consolidation algorithm periodically checks whether consolidation is necessary and then clusters and consolidates virtual machines to lower communication cost with an online heuristic. We used two benchmarks in a real environment to examine network influence on different tasks. To evaluate the advantages of the proposed algorithm, we also built a cloud computing testbed. Real workload trace-driven simulations and testbed-based experiments showed that, our algorithm greatly shortened the average finish time of map-reduce tasks and reduced time delay of web applications. Simulation results showed that our algorithm considerably reduced the amount of high-delay jobs, lowered the average traffic passed through aggregate switches and improved the communication ability among virtual machines.     

Review:Data center network architecture in cloud computing:review,taxonomy, and open research issues

The data center network(DCN), which is an important component of data centers, consists of a large number of hosted servers and switches connected with high speed communication links. A DCN enables the deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-based services and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN architecture lacks aggregate bandwidth, scalability, and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud data centers. Therefore, the design of a novel DCN architecture with the features of scalability, low cost, robustness, and energy conservation is required. This paper reviews the recent research findings and technologies of DCN architectures to identify the issues in the existing DCN architectures for cloud computing. We develop a taxonomy for the classification of the current DCN architectures, and also qualitatively analyze the traditional and contemporary DCN architectures. Moreover, the DCN architectures are compared on the basis of the significant characteristics, such as bandwidth, fault tolerance, scalability, overhead, and deployment cost. Finally, we put forward open research issues in the deployment of scalable, low-cost, robust, and energy-efficient DCN architecture, for data centers in computational clouds.     

面向移动云计算的自适应虚拟机调度算法

随着移动云计算的快速发展和应用普及,如何对移动云中心资源进行有效管理同时又降低能耗、确保资源高可用是目前移动云计算数据中心的热点问题之一.本文从CPU、内存、网络带宽和磁盘四个维度,建立了基于多目标优化的虚拟机调度模型VMSM-EUN(Virtual Machine Scheduling Model based on Energy consumption,Utility and minimum Number of servers),将最小化数据中心能耗、最大化数据中心效用以及最小化服务器数量作为调度目标.设计了基于改进粒子群的自适应参数调整的虚拟机调度算法VMSA-IPSO(Virtual Machine Scheduling Algorithm based on Improved Particle Swarm Optimization)来求解该模型.最后通过仿真实验验证了本文提出的调度算法的可行性与有效性.对比实验结果表明,本文设计的基于改进粒子群的自适应虚拟机调度算法在进行虚拟机调度时,能在降低能耗的同时提高数据中心效用.     

Incrementally upgradable data center architecture using hyperbolic tessellations

Current trends in cloud computing suggest that both large, public clouds and small, private clouds will proliferate in the near future. Operational requirements, such as high bandwidth, dependability and smooth manageability, are similar for both types of clouds and their underlying data center architecture. Such requirements can be satisfied with utilizing fully distributed, low-overhead mechanisms at the algorithm level, and an efficient layer 2 implementation at the practical level. On the other hand, owners of evolving private data centers are in dire need of an incrementally upgradeable architecture which supports a small roll-out and continuous expansion in small quanta. In order to satisfy both requirements, we propose Poincaré, a data center architecture inspired by hyperbolic tessellations, which utilizes low-overhead, greedy routing. On one hand, Poincaré scales to support large data centers with low diameter, high bisection bandwidth, inherent multipath and multicast capabilities, and efficient error recovery. On the other hand, Poincaré supports incremental plug & play upgradability with regard to both servers and switches. We evaluate Poincaré using analysis, extensive simulations and a prototype implementation.     

Data center network architecture in cloud computing： review,taxonomy, and open research issues＊

The data center network （DCN）, which is an important component of data centers, consists of a large number of hosted servers and switches connected with high speed communication links. A DCN enables thc deployment of resources centralization and on-demand access of the information and services of data centers to users. In recent years, the scale of the DCN has constantly increased with the widespread use of cloud-based services and the unprecedented amount of data delivery in/between data centers, whereas the traditional DCN architecture lacks aggregate bandwidth, scalability, and cost effectiveness for coping with the increasing demands of tenants in accessing the services of cloud data centers. Therefore, the design of a novel DCN architecture with the features of scalability, low cost, robustness, and energy conservation is required. This paper reviews the recent research findings and technologies of DCN architectures to identify the issues in the existing DCN architectures for cloud computing. We develop a taxonomy for the classification of the current DCN architectures, and also qualitatively analyze the traditional and contemporary DCN architectures. Moreover, the DCN architectures are compared on the basis of the significant characteristics, such as bandwidth, fault tolerance, scalability, overhead, and deployment cost. Finally, we put forward open research issues in the deployment of scalable, low-cost, robust, and energy-efficient DCN architecture, for data centers in computational clouds.     

BCDC: A High-Performance,Server-Centric Data Center Network

The capability of the data center network largely decides the performance of cloud computing. However, the number of servers in the data center network becomes increasingly huge, because of the continuous growth of the application requirements. The performance improvement of cloud computing faces great challenges of how to connect a large number of servers in building a data center network with promising performance. Traditional tree-based data center networks have issues of bandwidth bottleneck, failure of single switch, etc. Recently proposed data center networks such as DCell, FiConn, and BCube, have larger bandwidth and better fault-tolerance with respect to traditional tree-based data center networks. Nonetheless, for DCell and FiConn, the fault-tolerant length of path between servers increases in case of failure of switches; BCube requires higher performance in switches when its scale is enlarged. Based on the above considerations, we propose a new server-centric data center network, called BCDC, based on crossed cube with excellent performance. Then, we study the connectivity of BCDC networks. Furthermore, we propose communication algorithms and fault-tolerant routing algorithm of BCDC networks. Moreover, we analyze the performance and time complexities of the proposed algorithms in BCDC networks. Our research will provide the basis for design and implementation of a new family of data center networks.     

基于云网络架构的虚拟网络映射算法研究

云网络架构采用控制与转发分离机制实现了资源的灵活分配,为了满足云网络架构的资源分配符合多业务的资源需求,提出了一种基于云网络架构的虚拟网络映射算法,提高了资源利用率。建立的虚拟网络映射算法模型,给出了虚拟网络映射算法的约束条件和优化目标。针对语音、视频和数据3种业务进行了仿真,结果表明,提出的算法提高了控制资源利用率、转发资源利用率和链路资源利用率。     

Layered virtual machine migration algorithm for network resource balancing in cloud computing

Due to the increasing sizes of cloud data centers, the number of virtual machines (VMs) and applications rises quickly. The rapid growth of large scale Internet services results in unbalanced load of network resource. The bandwidth utilization rate of some physical hosts is too high, and this causes network congestion. This paper presents a layered VM migration algorithm (LVMM). At first, the algorithm will divide the cloud data center into several regions according to the bandwidth utilization rate of the hosts. Then we balance the load of network resource of each region by VM migrations, and ultimately achieve the load balance of network resource in the cloud data center. Through simulation experiments in different environments, it is proved that the LVMMalgorithm can effectively balance the load of network resource in cloud computing.     

云数据中心资源调度机制研究

云数据中心包含大量计算机,运作成本很高。有效整合资源、提高资源利用率、节约能源、降低运行成本是云数据中心关注的热点。云数据中心通过虚拟化技术将计算资源、存储资源和网络资源构建成动态的虚拟资源池;使用虚拟资源管理技术实现云计算资源自动部署、动态扩展、按需分配;用户采用按需和即付即用的方式获取资源。因此,数据中心对提高资源利用率的迫切需求,促使人们寻求新的方式以建设下一代数据中心。