Cost-Aware Multi-Domain Virtual Data Center Embedding

Virtual data center is a new form of cloud computing concept applied to data center. As one of the most important challenges, virtual data center embedding problem has attracted much attention from researchers. In data centers, energy issue is very important for the reality that data center energy consumption has increased by dozens of times in the last decade. In this paper, we are concerned about the cost-aware multi-domain virtual data center embedding problem. In order to solve this problem, this paper first addresses the energy consumption model. The model includes the energy consumption model of the virtual machine node and the virtual switch node, to quantify the energy consumption in the virtual data center embedding process. Based on the energy consumption model above, this paper presents a heuristic algorithm for cost-aware multi-domain virtual data center embedding. The algorithm consists of two steps: inter-domain embedding and intra-domain embedding. Inter-domain virtual data center embedding refers to dividing virtual data center requests into several slices to select the appropriate single data center. Intra-domain virtual data center refers to embedding virtual data center requests in each data center. We first propose an inter-domain virtual data center embedding algorithm based on label propagation to select the appropriate single data center. We then propose a cost-aware virtual data center embedding algorithm to perform the intra-domain data center embedding. Extensive simulation results show that our proposed algorithm in this paper can effectively reduce the energy consumption while ensuring the success ratio of embedding.

Study on Building and Modeling of Virtual Data of Xiaosha River Artificial Wetland

From the viewpoint of systems science, this article takes Xiaosha River artificial wetland under planning and construction as object of study based on the systems theory and takes the accomplished and running project of Xinxuehe artificial wetland as reference. The virtual data of quantity and quality of inflow and the quality of outflow of Xiaosha River artificial wetland are built up according to the running experience, forecasting model and theoretical method of the reference project as well as the comparison analysis of the similarity and difference of the two example projects. The virtual data are used to study the building of forecasting model of BP neural network of Xiaosha River artificial wetland.

Estimation of Natural Mortality Coefficient from Fish Abundance and Catch Data Using Virtual Population Analysis(VPA)

Natural mortality coefficient (M) was estimated from fish abundance (N) and catch (C) data using a Virtual Population Analysis (VPA) model. Monte Carlo simulations were used to evaluate the impact of different error distributions for the simulated data on the estimates of M. Among the four error structures (normal, lognormal, Poisson and gamma), simulations of normally dis-tributed errors produced the most viable estimates for M, with the lowest relative estimation errors (REEs) and median mean absolute deviations (MADs) for the ratio of the true to the estimated Ms. In contrast, the lognormal distribution had the largest REE value. Errors with different coefficients of variation (CV) were added to N and C. In general, when CVs in the data were less than 10%, reliable estimates of M were obtained. For normal and lognormal distributions, the estimates of M were more sensitive to the CVs in N than in C; when only C had error the estimates were close to the true. For Poisson and gamma distributions, opposite results were obtained. For instance, the estimates were more sensitive to the CVs in C than in N, with the largest REE from the scenario of error only in C. Two scenarios of high and low fishing mortality coefficient (F) were generated, and the simulation results showed that the method performed better for the scenario with low F. This method was also applied to the published data for the anchovy (Engraulis japonicus) of the Yellow Sea. Viable estimates of M were obtained for young groups, which may be explained by the fact that the great uncertainties in N and C observed for older Yellow Sea anchovy introduced large variation in the corresponding estimates of M.

ROUTE SELECTION IN VIRTUAL CIRCUIT DATA NETWORKS BASED ON GENETIC ALGORITHMS

This paper addresses the problem of selecting a route for every pair of communicating nodes in a virtual circuit data network in order to minimize the average delay encountered by messages. The problem was previously modeled as a network of M/M/1 queues. Agenetic algorithm to solve this problem is presented. Extensive computational results across a variety of networks are reported. These results indicate that the presented solution procedure outperforms the other methods in the literature and is effective for a wide range of traffic loads.

Data Virtualization with SAP HANA Smart Data Access

Digital transformation has been corner stone of business innovation in the last decade, and these innovations have dramatically changed the definition and boundaries of enterprise business applications. Introduction of new products/ services, version management of existing products/ services, management of customer/partner connections, management of multi-channel service delivery (web, social media, web etc.), merger/acquisitions of new businesses and adoption of new innovations/technologies will drive data growth in business applications. These datasets exist in different sharing nothing business applications at different locations and in various forms. So, to make sense of this information and derive insight, it is essential to break the data silos, streamline data retrieval and simplify information access across the entire organization. The information access framework must support just-in-time processing capabilities to bring data from multiple sources, be fast and powerful enough to transform and process huge amounts of data quickly, and be agile enough to accommodate new data sources per user needs. This paper discusses the SAP HANA Smart Data Access data-virtualization technology to enable unified access to heterogenous data across the organization and analysis of huge volume of data in real-time using SAP HANA in-memory platform.

基于Data Fabric的多模数据管理方法

随着政府和企业在信息化向数字化演进历程中信息化程度的加深,各类应用系统生成的数据日益多模化、多源化、海量化,这对数据管理造成了新的挑战.为了解决这些挑战,数据管理领域涌现出了许多新的技术和理念,其中Data Fabric (数据编织)便是一种新兴的数据管理技术和方法,它将分布式数据存储、处理和应用整合为一个整体,并提供了一套可视化的接口进行管理.本文首先分析了Data Fabric的技术架构、技术特点、技术价值和对多模数据进行管理与应用的完整流程.其次,提出了基于时序指标的多模多源数据的异常监测方法、基于日志数据的多模多源数据的异常监测方法,它们通过Data Fabric技术的使用,处理速度分别提高了33.3%和42.2%, F1-score分别提高12.2个和14.8个百分点,进一步说明了Data Fabric技术和本文新提出方法的高效性和应用价值.

The Design and Implementation of Multi-user Data Sharing Platform for Remote Monitoring System Based on Virtual Instruments

Remote data monitoring system which adopts virtual instrument usually applies data sharing, acquisition and remote transmission technology via internet. It is able to finish concurrent data acquisition and processing for multi-user and multi-task and also build a personalized virtual testing environment for more people but with fewer instruments. In this paper, we＇ 11 elaborate on the design and implementation of information sharing platform through a typical example of how to build multi-user concurrent virtual testing environment based on the virtnal software LabVIEW.

Research on Application of Data Mining in Virtual Community of Foreign Language Learning

The construction of virtual community in foreign language learning is a comprehensive foreign language learning environment integrated with foreign language vocabulary database construction and vocabulary retrieval, combining the virtual reality technology to construct the language environment of foreign language learning. The virtual community of foreign language leaming can improve the sense of language authenticity in foreign language learning and improve the quality of foreign language teaching. A method of building a virtual community for foreign language learning is proposed based on data mining technology, data acquisition and feature preprocessing model for building semantic vocabulary of foreign language learning is constructed, the linguistic environment characteristics of the semantic vocabulary data of foreign language learning is analyzed, and the semantic noumenon structure model is obtained. Fuzzy clustering method is used for vocabulary clustering and comprehensive retrieval in the virtual community of foreign language learning, the performance of vocabulary classification in foreign language learning is improved, the adaptive semantic information fusion method is used to realize the vocabulary data mining in the virtual community of foreign language learning, information retrieval and access scheduling for virtual communities in foreign language learning are realized based on data mining results. The simulation results show that the accuracy of foreign language vocabulary retrieval is good, improve the efficiency of foreign language learning.

Transforming Data into Actionable Insights with Cognitive Computing and AI

How organizations analyze and use data for decision-making has been changed by cognitive computing and artificial intelligence (AI). Cognitive computing solutions can translate enormous amounts of data into valuable insights by utilizing the power of cutting-edge algorithms and machine learning, empowering enterprises to make deft decisions quickly and efficiently. This article explores the idea of cognitive computing and AI in decision-making, emphasizing its function in converting unvalued data into valuable knowledge. It details the advantages of utilizing these technologies, such as greater productivity, accuracy, and efficiency. Businesses may use cognitive computing and AI to their advantage to obtain a competitive edge in today’s data-driven world by knowing their capabilities and possibilities [1].

A Virtual Router Cluster System Based on the Separation of the Control Plane and the Data Plane

This paper proposes a virtual router cluster system based on the separation of the control plane and the data plane from multiple perspectives,such as architecture,key technologies,scenarios and standardization.To some extent,the virtual cluster simplifies network topology and management,achieves automatic conFig.uration and saves the IP address.It is a kind of low-cost expansion method of aggregation equipment port density.

The Path to Reshaping the Structure and Chain System of the Publishing Industry in the Era of Big Data

Today,we are living in the era of“big data”where massive amounts of data are used for quantitative decisions and communication management.With the continuous penetration of big data-based intelligent technology in all fields of human life,the enormous commercial value inherent in the data industry has become a crucial force that drives the aggregation of new industries.For the publishing industry,the introduction of big data and relevant intelligent technologies,such as data intelligence analysis and scenario services,into the structure and value system of the publishing industry,has become an effective path to expanding and reshaping the demand space of publishing products,content decisions,workflow chain,and marketing direction.In the integration and reconstruction of big data,cloud computing,artificial intelligence,and other related technologies,it is expected that a generalized publishing industry pattern dominated by virtual interaction will be formed in the future.

新质生产力赋能传统武术现代转型的实践向度

文章运用构建理论框架并分析实际案例,探讨了新质生产力在促进传统武术现代转型中的作用,展示了科技创新如何赋能传统武术文化的传承与发展。在数字化时代背景下,新质生产力,尤其是虚拟现实、大数据、社交媒体等媒介,成为推动传统武术跨越发展的重要力量。研究发现,数字化技术显著提高了教学效率与学习体验,拓宽了文化传播渠道,但也面临着维持传统文化精髓、技术接受度及文化适应性等挑战。为了解决这些问题,文章提出了相应策略,如,深化技术与武术教学的融合,提升人员技术能力,维护文化核心,制定行业标准及加强国际交流。指出,恰当借用新质生产力,结合文化传承与技术创新,可有效促进传统武术的现代化转型与全球化发展。

面向垂直行业定制的多模态网络编程技术

多模态网络(Polymorphic Network,PINet)支持多种用户服务在统一的物理环境中共存,实现用户定制的多样化网络协议和能力等。文章以增量式、全维度、一体化和高安全的网络编程为目标,提出一种多模态网络的编程系统方案和运行逻辑,探讨关键技术包括网络模态生成、安全功能验证和流水线柔性编译等,为各种网络模态的灵活、快速部署提供一种可行的方案。

医院安全移动应用平台的设计和实现

目的:降低医院移动办公和移动业务场景中的数据安全泄露风险。方法:基于虚拟手机技术,将移动办公和移动业务App实际安装和运行在医院内网的虚拟手机服务器上,而用户手持的移动终端只作为虚拟手机的显示和输入设备,同时部署数据安全网关,实现虚拟手机与移动终端的网络隔离,防止内部数据泄露到外部。结果:用户通过移动终端访问虚拟手机中的移动应用,所有的应用数据都只在虚拟手机中被处理和存储,从而避免数据从移动终端泄露。结论:建设安全移动应用平台可以降低医院移动办公和移动业务场景中的数据安全风险,减少医院对移动终端进行安全管控的要求,有助于推动移动业务的健康发展。

数据驱动的机身对接过程仿真技术研究

针对传统机身对接仿真中理论模型偏差较大、有限元模型消耗时间长等问题,开展数据驱动的机身对接过程仿真技术研究。首先通过虚实融合技术,构建六自由度平台运动学模型和机身–调姿平台位姿转换模型,通过数据交互实现仿真数据实时传输从而驱动虚拟模型,利用测量数据和虚拟模型进行了机身对接仿真,确定了对接过程位姿参数,为调姿平台参数修正提供数据基础。之后为提升数据求解效率,根据求解机身变形的有限元模型,计算出变形量并转化成机身位置变化量,将机身姿态角和位置变化量作为输入输出值构建代理模型,并验证方法有效性。最后开发了机身对接过程仿真系统,以机身试验件的对接过程为例,验证了仿真系统的可行性。

基于数字孪生的液压回路虚拟实验系统

基于智能制造过程中数字孪生技术的应用及新工科人才培养数字孪生实践锻炼需求,探索了沉浸式液压回路虚拟实验数字孪生技术在工程教育中的应用。开发了基于数字学生的液压回路数字孪生系统,通过创建物理实体的精确虚拟孪生模型,并实时同步数据,为学习者提供了一个安全、可控且高度互动的学习环境。系统架构整合了物理实体、数据连接映射与孪生模型,实现了节流调速回路的实时数据采集、存储与三维可视化。通过该实验系统进行实验教学,不仅提升了教学效率,还增强了学生对新工科数字孪生技术的理解,并为学习者提供了全新的学习视角和工具。在提高对液压回路原理理解的同时,锻炼了学生解决实际工程问题的能力,为工程教育的创新与进步提供了新的思路和方法。

基于VR看房及网络大数据的住宅活荷载建模

活荷载取值是建筑结构可靠性设计的基础,样本采集又是荷载建模的关键。针对传统的抽样采集、入户称重的研究方式存在的入户难、称重难、效率低和样本少等问题,提出了基于网络房产中介平台开放的全景虚拟(virtual reality,VR)看房方式及网络大数据研究住宅持久性活荷载的方式。首先,利用VR看房获得房间尺寸、室内物品类型及数量等数据,再通过网络爬虫从互联网大数据资源中获得物品重量,实现非称重方式的活荷载调查。采用该方法调查了9座城市的4676个住宅房间样本(总面积近75000m2),通过数据统计分析与荷载组合得到了住宅活荷载标准值,并与现行规范建议值进行了对比。结果表明,任意时点持久性活荷载标准差显著增大,不同城市活荷载变动时间均值不同且整体呈现下降趋势。VR系统结合网络大数据可以实现高效、便捷的住宅活荷载调查,形成一种全新的活荷载研究方式。

交汇、挑战与应对:大数据技术对人类学民族志的影响

大数据技术的快速发展为研究者提供了更广泛、更丰富的数据资源,其规模性、实时性、多样性和高度可追溯性也为人类学研究带来了全新的视角和方法。本文探讨了大数据对人类学民族志方法的影响,即大数据技术与民族志在本体论、认识论和方法论上均有交汇,使民族志兼具定性与定量的双重特质,大数据技术促进了增强型民族志、网络民族志和线下民族志的发展与创新,拓宽了人类学民族志方法运用的空间。大数据也给传统民族志方法带来挑战,使传统民族志面临技术依赖和数据过载的困扰,过度依赖大数据容易导致对数据背后的人文因素的忽视,大数据“关键问题”策略挑战民族志的客观性与准确性,且大数据难以将人类经验与主观感受完全转换成数据。此外,民族志对大数据技术的运用还面临伦理和隐私保护的问题,如何避免被研究者信息泄露并保护其知情权也是民族志方法中的重点问题。针对这些问题,笔者认为在民族志中需要关注“厚数据”和“小数据”,注重对数据背后的社会和象征意义的深度解释,并严肃对待伦理问题。

基于数字孪生的数据中心机房设备耗电可视化监控系统设计

为提升数据中心机房设备耗电监控效果及可视化监控系统的实时性,提出基于数字孪生的数据中心机房设备耗电可视化监控系统设计。首先,在数字孪生技术的基础上完成系统架构及功能的设计;其次,构建源数据矩阵,计算矩阵的欧氏距离,并通过K-means聚类算法实现设备耗电的聚类处理;最后,利用数字孪生技术将映射真实物理车间到虚拟车间,引入孪生实体概念表达机房部件,融合数据中心机房设备故障数据,构建网络神经模型训练样本数据,生成新的故障诊断模型,实现基于数字孪生的数据中心机房设备耗电可视化监控。结果表明,所提方法的数据中心机房设备耗电监控系统的可视化效果好、设备耗电监控结果好,可为数据管理提供可靠的技术支撑。