XML-based data processing in network supported collaborative design

In the course of network supported collaborative design, the data processing plays a very vital role. Much effort has been spent in this area, and many kinds of approaches have been proposed. Based on the correlative materials, this paper presents extensible markup language(XML)based strategy for several important problems of data processing in network supported collaborative design, such as the representation of standard for the exchange of product model data(STEP)with XML in the product information expression and the management of XML documents using relational database. The paper gives a detailed exposition on how to clarify the mapping between XML structure and the relationship database structure and how XML-QL queries can be translated into structured query language(SQL) queries. Finally, the structure of data processing system based on XML is presented.     

A neural network approach for data masking

In this letter we present a neural network based data masking solution, in which the database information remains internally consistent yet is not inadvertently exposed in an interpretable state. The system differs from the classic data masking in the sense that it can understand the semantics of the original data and mask it using a neural network which is a priori trained by some rules. Our adaptive data masking (ADM) concentrates on data masking techniques such as shuffling, substitution, masking and number variance in an intelligent fashion with the help of adaptive neural network. The very nature of being adaptive makes data masking easier and content agnostic, and thus finds place in various vertical domains and systems.     

SEED: solar energy‐aware efficient scheduling for data centers

It is well known that data centers are consuming a large amount of energy that incurs significant financial and environmental costs. Recently, there has been an increasing interest in utilizing green energy for data centers, where green energy sources include solar and wind. This paper studies the crucial problem of maximizing the utilization of green energy through scheduling complex jobs in data centers in order to reduce the use of traditional brown energy. However, it is highly challenging for data centers to make use of green energy. First, the availability of typical green energy is variable to dynamic changes of natural environments, for example, weather. Second, although predictions can be made for the future availability of green energy, it is inevitable that such predictions have errors. Third, jobs are associated with strict deadlines, and it is required that jobs are completed before their deadlines. Finally, because the reliability in a data center relies upon temperature, the awareness of temperature should be taken into account while maximizing the green energy. In this paper, we consider online scheduling of jobs whose arrivals to the data center system dynamically. In addition, we explicitly take the power consumption of switches into account when scheduling jobs onto computing nodes. Two solar energy‐aware algorithms called SEEDMin and SEEDMax have been proposed. Then, we extend SEED to RSEED with the awareness of reliability. To evaluate the effectiveness of the proposed algorithms, comprehensive simulations have been conducted, and the proposed algorithms are compared with other state‐of‐art algorithms. Experimental results demonstrate that both SEEDMin and SEEDMax can significantly increase the utilization of solar energy without violating job deadlines and overall energy budget. The amount of solar energy utilized by SEEDMin and SEEDMax is 33.4%and35.3% larger than that of two traditional scheduling algorithms, MinMin and MinMax, respectively. Also, it can be seen that RSEED greatly improves the reliability by decreasing the temperature. Copyright © 2013 John Wiley & Sons, Ltd.     

基于软件定义网络的虚拟数据中心管理平台

针对已有虚拟数据中心（VDC）管理平台具有代码固化、后续升级困难等缺陷,设计和实现一种基于软件定义网络（SDN）的VDC管理平台。该平台由VDC管理子系统（VDCM）、VDC计算资源控制子系统（VDCCRC）和VDC网络资源控制子系统（VDCNRC）组成,子系统之间通过RESTful API交互建立起松耦合架构。VDCNRC通过SDN控制器管理数据中心网络资源,VDCCRC通过开源云平台管理数据中心计算资源,VDC管理子系统中内置VDC管理算法框架,可快速开发适用于实际生产环境的VDC管理算法。使用Mininet、Openstack、Floodlight搭建了测试环境,验证了该平台可通过Openstack来控制虚拟机的启动、迁移和删除,可通过Openflow控制器实现VDC网络带宽资源隔离,并支持VDC创建、删除和修改等操作。     

面向大规模数据中心的常量度数互连网络研究

如何高效互连大规模服务器是数据中心网络面临的一个重要挑战.目前提出的新型数据中心网络结构主要是通过增加服务器的网络端口数来扩展数据中心的规模,导致扩展的局限性和管理的复杂性.为此,如何设计由固定网络端口数的服务器互连而成的、具有常量度数的数据中心网络结构意义重大.提出了一种新型的面向大规模数据中心的常量度数互连网络结构CH(conjugate hypercube),该结构以固定网络端口数的服务器为中心,采用多层次互连实现了可扩展性和性能之间的平衡.理论分析和实验结果表明,该互连网络在不增加服务器网络端口数的前提下,可有效支持大规模数据中心高带宽、高容错的多模式数据通信;同时,具有良好的可部署性和可维护性.     

基于流调度选择的DCN动态负载均衡算法

针对数据中心网络(data center network,DCN)动态调度导致的负载不均衡问题,提出了基于流调度选择的动态负载均衡(dynamic load balancing based on flow scheduling selection,DLBFSS)算法。该算法首先计算拥塞链路上各条大流的等价最短路径,并删除不满足流带宽需求的路径;然后计算剩余路径的可用吞吐量,选择可用吞吐量最大的路径作为最优调度路径;最后根据大流的带宽和最优路径的负载定义调度的拥塞概率,将拥塞概率作为大流调度选择的依据。实验结果表明,与传统ECMP（equal-cost multi-path）路由和现有大流调度算法相比,DLBFSS能够减小网络时延,提高流的带宽利用率,保证了更好的负载均衡。     

数据中心网络光电交换架构下的流调度算法研究

研究了基于多级Clos数据中心网络的光电交换架构下的流量调度算法,以减少数据传输时延,同时也保证无丢包。传统ADAPT调度算法能实现加速比最小化,但仍然有一些空闲时间槽,而造成带宽未被充分利用。为了解决该问题,在多级Clos网络中,提出了一个多跳路由和调度（Multi-Hop Routing and Scheduling,MHRS）算法,该算法可以在不增加加速比的同时充分利用空闲的带宽。与ADAPT算法类似,MHRS算法先将流量矩阵分解为商矩阵和余矩阵,然后实现两步调度,即将单跳作为第一步,将多跳作为第二步。第一步将余矩阵中的一些数据包调度到商矩阵所形成的配置矩阵的空闲时间槽中,而当这些数据包不能在第一步中被直接调度时,则采用第二步绕道的多跳调度过程传输数据包。仿真结果证明,在多级Clos网络中MHRS算法比ADAPT算法性能更好。     

基于蜂窝网络结构的数据融合算法

针对无线传感网中节点能耗同数据精确度之间不均衡的问题,提出一种能够基于蜂窝网络结构的数据融合算法（DFACN）。在基于蜂窝网络的分簇结构中,首先筛选最小能耗的簇头;之后通过数据精确度和节点能耗的计算判断融合因子的大小,动态选取参与融合的簇内节点数;最后簇头完成数据的融合处理。在OPNET仿真环境下,与EECDA和IDDOA算法进行实验对比,DFACN算法的数据精确度分别提高了2.6%和4.7%,节点能耗分别降低了2.7%与3.4%。结果表明,DFACN算法在降低能耗的同时,有效地提高了数据的融合精确度,并且延长了网络的生命周期。     

软件定义网络架构下基于流调度代价的数据中心网络拥塞控制路由算法

针对传统数据中心网络极易发生拥塞的问题,提出了在软件定义网络（SDN）的架构下设计基于流调度代价的拥塞控制路由算法加以解决。首先,进行拥塞链路上的大小流区分,并对所有大流的各条等价路径进行路径开销权重的计算,选择权重最小的路径作为可用调度路径;然后,使用调度后路径开销变化量和流占用带宽比例来共同定义流调度代价;最终选择调度代价最小的流进行调度。仿真结果表明,所提算法能在网络发生拥塞时降低了拥塞链路上的负荷,并且与仅进行流路径选择的拥塞控制算法相比,提高了链路利用率,减少了流传输时间,使得网络链路资源得到更好的利用。     

弹性光数据中心网络中收益最大化的带宽分配算法

弹性光数据中心网络中的带宽分配需要兼顾云服务提供商收益和用户连接请求性能,对此提出收益最大化的带宽分配算法,其以子载波为基本单位将光纤带宽有效地分配给多个用户连接请求。首先,基于收益模型和虚拟子载波的概念建立带宽分配问题的整数线性规划模型;接着提出一种基于动态规划的带宽分配算法以及一种启发式的带宽分配算法,前者致力于获得最大的分配收益,后者则力求获得更好的时间效率;最后,通过仿真实验对所设计的带宽算法的性能进行验证。实验结果表明,相较于已有的弹性光数据中心网络中的带宽分配算法,设计的两种算法均能在带宽分配中获得更高的系统收益。     

具有丢失数据的贝叶斯网络结构学习算法

学习具有丢失数据的贝叶斯网络结构主要采用结合 EM 算法的打分一搜索方法,其效率和可靠性比较低.针对此问题建立一个新的具有丢失数据的贝叶斯网络结构学习算法.该方法首先用 Kullback-Leibler(KL)散度来表示同一结点的各个案例之间的相似程度,然后根据 Gibbs 取样来得出丢失数据的取值.最后,用启发式搜索完成贝叶斯网络结构的学习.该方法能够有效避免标准 Gibbs 取样的指数复杂性问题和现有学习方法存在的主要问题.     

数据中心中TCP连接建立过程的优化方法

针对数据中心中由于SYN包丢失而引起的TCP连接被延迟从而错过任务时间限制的问题,在无需更换现有设备以及无需修改应用和TCP的前提下,提出一种基于加权随机早期检测（WRED）协议的TCP连接初始化的优化方法。该方法解决了连接优化的三个关键问题：如何识别和标记SYN包,如何在交换机上为SYN包预留空间以及需要预留多少空间。与原TCP相比,优化后TCP连接建立的时间极大地减少。实验表明TCP连接初始化优化方法可以解决任务错过规定时间限制的问题。     

On Maximum Elastic Scheduling in Cloud-Based Data Center Networks for Virtual Machines with the Hose Model

Journal of Computer Science and Technology - With the growing popularity of cloud-based data center networks (DCNs), task resource allocation has become more and more important to the efficient use...     

数据中心网络可靠组播的研究

通过分析数据中心网络特征和云计算应用需求,提出一种适合数据中心网络的可靠组播机制R2M。针对云计算应用同时强调数据完整性和传输实时性的要求,R2M充分利用数据中心网络服务器间存在多路径的特点,通过组织多棵不相交组播树并行传输数据,并在相邻节点间采用P2P技术建立跨树的恢复关系,实现恢复路径与组播传输路径完全隔离。选择最邻近节点重传数据和较小的路径差错相关性使得R2M能够快速恢复丢失的数据包。基于BCube的仿真实验结果显示R2M具有高可靠性和较小的恢复延迟。     

移动传感器网络中基于概率的数据传输策略*

移动传感器网络间歇连通,为了在拓扑动态变化的环境中将数据有效传递到汇聚点,提出了一种基于节点传输概率的数据传输策略PBD。该策略基于传感器节点近期与汇聚点的距离来计算节点将数据成功传递给汇聚点的可能性,并以此作为消息传输时选择下一跳的依据;同时还引入了基于消息优先权的缓存队列管理机制。实验结果证明了PBD的有效性,也表明PBD较好地实现了数据传输成功率与传输能耗、传输延迟之间的平衡。     

Monitoring Power Data: A first step towards a unified energy efficiency evaluation toolset for HPC data centers

The energy consumption of High Performance Computing (HPC) systems, which are the key technology for many modern computation-intensive applications, is rapidly increasing in parallel with their performance improvements. This increase leads HPC data centers to focus on three major challenges: the reduction of overall environmental impacts, which is driven by policy makers; the reduction of operating costs, which are increasing due to rising system density and electrical energy costs; and the 20 MW power consumption boundary for Exascale computing systems, which represent the next thousandfold increase in computing capability beyond the currently existing petascale systems. Energy efficiency improvements will play a major part in addressing these challenges.This paper presents a toolset, called Power Data Aggregation Monitor (PowerDAM), which collects and evaluates data from all aspects of the HPC data center (e.g. environmental information, site infrastructure, information technology systems, resource management systems, and applications). The aim of PowerDAM is not to improve the HPC data center's energy efficiency, but is to collect energy relevant data for analysis without which energy efficiency improvements would be non-trivial and incomplete. Thus, PowerDAM represents a first step towards a truly unified energy efficiency evaluation toolset needed for improving the overall energy efficiency of HPC data centers.     

基于软件定义网络的数据中心网络负载均衡算法研究

针对传统网络的分布式架构使得负载均衡技术难以满足低成本、高灵活性、自适应调整的要求,提出一种基于SDN的数据中心网络负载均衡算法。首先,根据路径当前负载状况和链路负载波动为路径设置了一个权重,并以此作为路径选择依据;其次,设置了一个负载均衡度用于衡量网络负载状况;最后,针对需要调度的流,进一步限定了其流量大小范围,保证了高效的流调度。仿真结果表明,与其他算法相比,所提算法能有效提高网络资源利用率并均衡全网负载。     

Design and optimization of VLC based small-world data centers

The visible light communication (VLC) has the potential to provide dense and fast connectivity at low cost. In this paper, we propose a novel VLC enabled Wireless Small-World Data Center (WSWDC). It employs VLC links to achieve a fully wireless data center network (DCN) across racks for the first time. The using of VLC links eliminates hierarchical switches and inter-rack cables, and thus reducing hardware investment, as well as maintenance cost. More precisely, to simplify the configuration and control operations, we propose three DCN design rationales: (1) fully-wireless, all inter-rack links are wireless; (2) easy-deployable, it is not necessary to change the existing infrastructure inside data center; (3) plug-and-play, no extra centralized control operations are required. Previous proposals, however, cannot achieve the three rationales simultaneously. To this end, we first use regular VLC links to interconnect racks as a regular grid DCN and optimize the rack placement to shorten the average path length and the network diameter. To further exploiting the benefits of VLC links, a few random VLC links are carefully introduced to update the wireless grid DCN as a wireless small-world DCN. To avoid the potential interference among VLC links, we deploy VLC transceivers at different heights on the top of each rack. In this way, VLC links would not interfere with others at each height level. Moreover, we design a greedy but efficient routing method for any pair of racks using their identifiers as inputs. Comprehensive evaluation results indicate that our WSWDC exhibits good topological properties and network performance.     

基于概率的大数据查询系统——Probery

针对大数据环境下完整性查询时间代价消耗过高的问题,提出了一种采用近似完整性查询方法的系统——Probery。Probery所采用的近似完整性查询方法不同于传统的近似查询,其近似性主要体现为数据查全的可能性,是一种新型的数据查询方法。Probery首先将存入系统的数据划分为多个数据分段;然后,根据概率放置模型将各个数据分段的数据存储在分布式文件系统中;最后,对于给定的查询条件,Probery采用一种启发式查询方法进行概率查询。通过与其他主流的非关系型数据管理系统的查询性能进行比较,对Probery进行验证,Probery在损失8%查询完整性的情形下,查询时间较HBase相比节约了51%,较Cassandra相比节约了23%,较MongoDB相比节约了12%,较Hive相比节约了3%。实验结果表明,Probery可以适当地损失查询完整性来提高数据的查询性能,具有较好的通用性、适应性和可扩展性。     

浅谈数据中心网络架构的发展

网络是数据中心的一个重要基础设施,而通常分为两大部分：前端计算网络以及后端存储网络.后端的存储网络目前主要是FC网络,不过随着IP技术的发展,采用FCoE技术融合前后端网络成为一种趋势.本文主要关注前端计算网络.