A Spatio-temporal Data Model for Road Network in Data Center Based on Incremental Updating in Vehicle Navigation System

The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.

Data Center Traffic Scheduling Strategy for Minimization Congestion and Quality of Service Guaranteeing

According to Cisco’s Internet Report 2020 white paper,there will be 29.3 billion connected devices worldwide by 2023,up from 18.4 billion in 2018.5G connections will generate nearly three times more traffic than 4G connections.While bringing a boom to the network,it also presents unprecedented challenges in terms of flow forwarding decisions.The path assignment mechanism used in traditional traffic schedulingmethods tends to cause local network congestion caused by the concentration of elephant flows,resulting in unbalanced network load and degraded quality of service.Using the centralized control of software-defined networks,this study proposes a data center traffic scheduling strategy for minimization congestion and quality of service guaranteeing(MCQG).The ideal transmission path is selected for data flows while considering the network congestion rate and quality of service.Different traffic scheduling strategies are used according to the characteristics of different service types in data centers.Reroute scheduling for elephant flows that tend to cause local congestion.The path evaluation function is formed by the maximum link utilization on the path,the number of elephant flows and the time delay,and the fast merit-seeking capability of the sparrow search algorithm is used to find the path with the lowest actual link overhead as the rerouting path for the elephant flows.It is used to reduce the possibility of local network congestion occurrence.Equal cost multi-path(ECMP)protocols with faster response time are used to schedulemouse flows with shorter duration.Used to guarantee the quality of service of the network.To achieve isolated transmission of various types of data streams.The experimental results show that the proposed strategy has higher throughput,better network load balancing,and better robustness compared to ECMP under different traffic models.In addition,because it can fully utilize the resources in the network,MCQG also outperforms another traffic scheduling strategy that does rerouting for elephant flows(namely Hedera).Compared withECMPandHedera,MCQGimproves average throughput by 11.73%and 4.29%,and normalized total throughput by 6.74%and 2.64%,respectively;MCQG improves link utilization by 23.25%and 15.07%;in addition,the average round-trip delay and packet loss rate fluctuate significantly less than the two compared strategies.

Research on Track Fastener Service Status Detection Based on Improved Yolov4 Model

As an important part of railway lines, the healthy service status of track fasteners was very important to ensure the safety of trains. The application of deep learning algorithms was becoming an important method to realize its state detection. However, there was often a deficiency that the detection accuracy and calculation speed of model were difficult to balance, when the traditional deep learning model is used to detect the service state of track fasteners. Targeting this issue, an improved Yolov4 model for detecting the service status of track fasteners was proposed. Firstly, the Mixup data augmentation technology was introduced into Yolov4 model to enhance the generalization ability of model. Secondly, the MobileNet-V2 lightweight network was employed in lieu of the CSPDarknet53 network as the backbone, thereby reducing the number of algorithm parameters and improving the model’s computational efficiency. Finally, the SE attention mechanism was incorporated to boost the importance of rail fastener identification by emphasizing relevant image features, ensuring that the network’s focus was primarily on the fasteners being inspected. The algorithm achieved both high precision and high speed operation of the rail fastener service state detection, while realizing the lightweight of model. The experimental results revealed that, the MAP value of the rail fastener service state detection algorithm based on the improved Yolov4 model reaches 83.2%, which is 2.83% higher than that of the traditional Yolov4 model, and the calculation speed was improved by 67.39%. Compared with the traditional Yolov4 model, the proposed method achieved the collaborative optimization of detection accuracy and calculation speed.

Workflow-Based Platform Design and Implementation for Numerical Weather Prediction Models and Meteorological Data Service

In this paper, we present a set of best practices for workflow design and implementation for numerical weather prediction models and meteorological data service, which have been in operation in China Meteorological Administration (CMA) for years and have been proven effective in reliably managing the complexities of large-scale meteorological related workflows. Based on the previous work on the platforms, we argue that a minimum set of guidelines including workflow scheme, module design, implementation standards and maintenance consideration during the whole establishment of the platform are highly recommended, serving to reduce the need for future maintenance and adjustment. A significant gain in performance can be achieved through the workflow-based projects. We believe that a good workflow system plays an important role in the weather forecast service, providing a useful tool for monitoring the whole process, fixing the errors, repairing a workflow, or redesigning an equivalent workflow pattern with new components.

Modeling TCP Incast Issue in Data Center Networks and an Adaptive Application-Layer Solution

In data centers, the transmission control protocol(TCP) incast causes catastrophic goodput degradation to applications with a many-to-one traffic pattern. In this paper, we intend to tame incast at the receiver-side application. Towards this goal, we first develop an analytical model that formulates the incast probability as a function of connection variables and network environment settings. We combine the model with the optimization theory and derive some insights into minimizing the incast probability through tuning connection variables related to applications. Then,enlightened by the analytical results, we propose an adaptive application-layer solution to the TCP incast.The solution equally allocates advertised windows to concurrent connections, and dynamically adapts the number of concurrent connections to the varying conditions. Simulation results show that our solution consistently eludes incast and achieves high goodput in various scenarios including the ones with multiple bottleneck links and background TCP traffic.

Subject Oriented Autonomic Cloud Data Center Networks Model

This paper investigates autonomic cloud data center networks, which is the solution with the increasingly complex computing environment, in terms of the management and cost issues to meet users’ growing demand. The virtualized cloud networking is to provide a plethora of rich online applications, including self-configuration, self-healing, self-optimization and self-protection. In addition, we draw on the intelligent subject and multi-agent system, concerning system model, strategy, autonomic cloud computing, involving independent computing system development and implementation. Then, combining the architecture with the autonomous unit, we propose the MCDN (Model of Autonomic Cloud Data Center Networks). This model can define intelligent state, elaborate the composition structure, and complete life cycle. Finally, our proposed public infrastructure can be provided with the autonomous unit in the supported interaction model.

Data model for digital library service registry

According to the existing multidimensional description idea for services,the authors designed a data model for a digital library service registry based on the IESR data model.This DLSR data model consists of five core entities:service,business,agent,collection and method.It offers an efficient way to release and discover digital library services.At the end of this paper,a mapping from the DLSR data model to the UDDI data model is proposed,which provides a technical solution to implement a DLSR system based on UDDI registry center.Both the data model and the mapping solution have been applied to the development of the OFSR system and proved to be effective.

Cloud Data Center Selection Using a Modified Differential Evolution

The interest in selecting an appropriate cloud data center is exponentially increasing due to the popularity and continuous growth of the cloud computing sector.Cloud data center selection challenges are compounded by ever-increasing users’requests and the number of data centers required to execute these requests.Cloud service broker policy defines cloud data center’s selection,which is a case of an NP-hard problem that needs a precise solution for an efficient and superior solution.Differential evolution algorithm is a metaheuristic algorithm characterized by its speed and robustness,and it is well suited for selecting an appropriate cloud data center.This paper presents a modified differential evolution algorithm-based cloud service broker policy for the most appropriate data center selection in the cloud computing environment.The differential evolution algorithm is modified using the proposed new mutation technique ensuring enhanced performance and providing an appropriate selection of data centers.The proposed policy’s superiority in selecting the most suitable data center is evaluated using the CloudAnalyst simulator.The results are compared with the state-of-arts cloud service broker policies.

FP-STE: A Novel Node Failure Prediction Method Based on Spatio-Temporal Feature Extraction in Data Centers

The development of cloud computing and virtualization technology has brought great challenges to the reliability of data center services.Data centers typically contain a large number of compute and storage nodes which may fail and affect the quality of service.Failure prediction is an important means of ensuring service availability.Predicting node failure in cloud-based data centers is challenging because the failure symptoms reflected have complex characteristics,and the distribution imbalance between the failure sample and the normal sample is widespread,resulting in inaccurate failure prediction.Targeting these challenges,this paper proposes a novel failure prediction method FP-STE(Failure Prediction based on Spatio-temporal Feature Extraction).Firstly,an improved recurrent neural network HW-GRU(Improved GRU based on HighWay network)and a convolutional neural network CNN are used to extract the temporal features and spatial features of multivariate data respectively to increase the discrimination of different types of failure symptoms which improves the accuracy of prediction.Then the intermediate results of the two models are added as features into SCSXGBoost to predict the possibility and the precise type of node failure in the future.SCS-XGBoost is an ensemble learning model that is improved by the integrated strategy of oversampling and cost-sensitive learning.Experimental results based on real data sets confirm the effectiveness and superiority of FP-STE.

Mechanistic modeling of copper corrosions in data center environments

Air-side economizers are increasingly used to take advantage of“free-cooling”in data centers with the intent of reducing the carbon footprint of buildings.However,they can introduce outdoor pollutants to indoor environment of data centers and cause corrosion damage to the information technology equipment.To evaluate the reliability of information technology equipment under various thermal and air-pollution conditions,a mechanistic model based on multi-ion transport and chemical reactions was developed.The model was used to predict Cu corrosion caused by Cl_(2)-containing pollutant mixtures.It also accounted for the effects of temperature(25℃and 28℃),relative humidity(50%,75%,and 95%),and synergism.It also identified higher air temperature as a corrosion barrier and higher relative humidity as a corrosion accelerator,which agreed well with the experimental results.The average root mean square error of the prediction was 13.7Å.The model can be used to evaluate the thermal guideline for data centers design and operation when Cl_(2)is present based on pre-established acceptable risk of corrosion in data centers’environment.

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

With the promotion of“dual carbon”strategy,data center(DC)access to high-penetration renewable energy sources(RESs)has become a trend in the industry.However,the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids.In this paper,a multi-timescale optimal scheduling model is established for interconnected data centers(IDCs)based on model predictive control(MPC),including day-ahead optimization,intraday rolling optimization,and intraday real-time correction.The day-ahead optimization stage aims at the lowest operating cost,the rolling optimization stage aims at the lowest intraday economic cost,and the real-time correction aims at the lowest power fluctuation,eliminating the impact of prediction errors through coordinated multi-timescale optimization.The simulation results show that the economic loss is reduced by 19.6%,and the power fluctuation is decreased by 15.23%.

Data Fusion about Serviceability Reliability Prediction for the Long-Span Bridge Girder Based on MBDLM and Gaussian Copula Technique

This article presented a new data fusion approach for reasonably predicting dynamic serviceability reliability of the long-span bridge girder.Firstly,multivariate Bayesian dynamic linear model(MBDLM)considering dynamic correlation among the multiple variables is provided to predict dynamic extreme deflections;secondly,with the proposed MBDLM,the dynamic correlation coefficients between any two performance functions can be predicted;finally,based on MBDLM and Gaussian copula technique,a new data fusion method is given to predict the serviceability reliability of the long-span bridge girder,and the monitoring extreme deflection data from an actual bridge is provided to illustrated the feasibility and application of the proposed method.

A Web-based Geographic Hypermedia System:Data Model,System Design and Prototype Applications

Geographic Hypermedia(GH)is a rich and interactive map document with geo-tagged graphics,sound and video ele-ments.A Geographic Hypermedia System(GHS)is designed to manage,query,display and explore GH resources.Recognizing emerging geo-tagged videos and measurable images as valuable geographic data resources,this paper aims to design a web-based GHS using web mapping,geoprocessing,video streaming and XMLHTTP services.The concept,data model,system design and implementation of this GHS are discussed in detail.Geo-tagged videos are modeled as temporal,spatial and metadata entities such as video clip,video path and frame-based descriptions.Similarly,geo-tagged stereo video and derived data are modeled as interre-lated entities:original video,rectified video,stereo video,video path,frame-based description and measurable image(rectified and disparity image with baseline,interior and exterior parameters).The entity data are organized into video files,GIS layers with linear referencing and XML documents for web publishing.These data can be integrated in HTML pages or used as Rich Internet Appli-cations(RIA)using standard web technologies such as the AJAX,ASP.NET and RIA frameworks.An SOA-based GHS is designed using four types of web services:ArcGIS Server 9.3 web mapping and geoprocessing services,Flash FMS 3.0 video streaming ser-vices and GeoRSS XMLHTTP services.GHS applications in road facility management and campus hypermapping indicate that the GH data models and technical solutions introduced in this paper are useful and flexible enough for wider deployment as a GHS.

The Development of World Data Center for Seismology,Beijing

World Data Center(WDC)for Seismology,Beijing has developed for 20 years in China until this year.The sustained and stable data sharing service system has already taken shape.This article gives an overview of the construction and development of WDC for Seismology,Beijing.It outlines the history,facilities and technical specifications of the center.It also illustrates the data service,the website,and gives a brief description of the perspective.

基于Panel Data模型的生产者服务业区域发展影响因素

利用面板数据误差修正模型考察了制造业规模、投资、就业、区域经济水平、城市化水平等因素对我国东部、中部和西部的生产者服务业发展的长期和短期影响.结果发现,除了区域经济水平在长期和短期都对本区域生产者服务业具有显著的促进作用以外,其他影响因素在不同区域的影响方向和大小有所不同.从长期来看,东部地区制造业规模对生产者服务业具有挤出效应,而投资和就业能显著地促进行业发展;中部地区投资、就业和城市化水平都对生产者服务业产生了负向影响;西部地区除了制造业规模影响为负值之外,其他因素都能促进行业发展.从短期来看,东部除了投资、中部和西部除了城市化之外,其他因素基本上对行业发展具有正效应.

The Sensitivity of Model Results to Specification of Network-Based Level of Service Attributes: An Application of a Mixed Logit Model to Trave Mode Choice

The need for travel demand models is growing worldwide. Obtaining reasonably accurate level of service (LOS) attributes of different travel modes such as travel time and cost representing the performance of transportation system is not a trivial task, especially in growing cities of developing countries. This study investigates the sensitivity of results of a travel mode choice model to different specifications of network-based LOS attributes using a mixed logit model. The study also looks at the possibilities of correcting some of the inaccuracies in network-based LOS attributes. Further, the study also explores the effects of different specifications of LOS data on implied values of time and aggregation forecasting. The findings indicate that the implied values of time are very sensitive to specification of data and model implying that utmost care must be taken if the purpose of the model is to estimate values of time. Models estimated on all specifications of LOS-data perform well in prediction, likely suggesting that the extra expense on developing a more detailed and accurate network models so as to derive more precise LOS attributes is unnecessary for impact analyses of some policies.

Local Curvature and Centering Effects in Nonlinear Regression Models

The effects of centering response and explanatory variables as a way of simplifying fitted linear models in the presence of correlation are reviewed and extended to include nonlinear models, common in many biological and economic applications. In a nonlinear model, the use of a local approximation can modify the effect of centering. Even in the presence of uncorrelated explanatory variables, centering may affect linear approximations and related test statistics. An approach to assessing this effect in relation to intrinsic curvature is developed and applied. Mis-specification bias of linear versus nonlinear models also reflects this centering effect.

Energy-Conserving Transmission Network Model Based on Service-Awareness

In this paper, we propose a service-aware network model which is based on the traffic pattern in data center. First of all, we analyze the traffic model in data center networks. Then we use this model to make the net topology integration and classification through the software define network. In order to achieve the purpose of energy consumption optimization, we divide the hosts into same VLAN according to their interaction frequency to reduce the cross VLAN transmission consumption. Simulation results show that we get a great energy improvement in the fat tree net topology.

基于Modelica的冷水机组建模仿真

针对传统冷水机组所建模型组件不可更换、模型可调参数少、模型通用性低的问题,采用Modelica语言和面向对象的建模思想建立了冷水机组的仿真模型。在Dymola软件中先分别建立了主要部件模型,将各个部件统一整合构成了冷水机组模型,模型具备真实机械的基础制冷功能。在参照真实机械某段时间的运行工况调整参数后,得到的运行数据对比真实机械的运行数据具备一定准确性;与传统模型相比,该模型在模块化、参数动态调整区间拥有更加广泛等优势。

“东数西算”背景下多数据中心联合消纳可再生能源途径研究综述

“碳达峰、碳中和”和“东数西算”战略目标背景下,新能源装机占比和数据中心机架数量持续增长,数据中心的负荷调节能力可有效促进新能源消纳。与传统的可中断、可转移负荷不同,数据中心作为一种用电量可观的新型负荷,在时间、空间尺度上均有调节潜力。因此,立足于现有研究成果,该文首先对数据中心能耗模型进行分析,对比了几种常见的建模方法;其次,通过对数据中心时空转移特性的定性分析和对消纳可再生能源潜力定量评估方法的总结,揭示了数据中心的消纳潜力;再次,分别从技术和管理2个层面梳理了数据中心消纳可再生能源的途径;最后,展望了“东数西算”背景下多数据中心联合消纳可再生能源的途径,为可再生能源消纳提供新的思路,助力我国“双碳”目标的实现。