scholarly journals A Cloud-Computing-Based Data Placement Strategy in High-Speed Railway

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Hanning Wang ◽  
Weixiang Xu ◽  
Futian Wang ◽  
Chaolong Jia
Keyword(s):  
Cloud Computing ◽  
Data Sharing ◽  
High Speed ◽  
Large Scale ◽  
Interval Mapping ◽  
Data Placement ◽  
Programming Algorithm ◽  
High Speed Railway ◽  
Typical Application ◽  
Mapping Algorithm

As an important component of China’s transportation data sharing system, high-speed railway data sharing is a typical application of data-intensive computing. Currently, most high-speed railway data is shared in cloud computing environment. Thus, there is an urgent need for an effective cloud-computing-based data placement strategy in high-speed railway. In this paper, a new data placement strategy named hierarchical structure data placement strategy is proposed. The proposed method combines the semidefinite programming algorithm with the dynamic interval mapping algorithm. The semi-definite programming algorithm is suitable for the placement of files with various replications, ensuring that different replications of a file are placed on different storage devices, while the dynamic interval mapping algorithm ensures better self-adaptability of the data storage system. A hierarchical data placement strategy is proposed for large-scale networks. In this paper, a new theoretical analysis is provided, which is put in comparison with several other previous data placement approaches, showing the efficacy of the new analysis in several experiments.

A High-Speed Railway Data Placement Strategy Based on Cloud Computing

2011 ◽  
Vol 135-136 ◽  
pp. 43-49
Author(s):  
Han Ning Wang ◽  
Wei Xiang Xu ◽  
Chao Long Jia
Keyword(s):  
Cloud Computing ◽  
High Speed ◽  
Data Access ◽  
Interval Mapping ◽  
Data Placement ◽  
Programming Algorithm ◽  
High Speed Railway ◽  
Mapping Algorithm ◽  
Data Intensive ◽  
Study Results

The application of high-speed railway data, which is an important component of China's transportation science data sharing, has embodied the typical characteristics of data-intensive computing. A reasonable and effective data placement strategy is needed to deploy and execute data-intensive applications in the cloud computing environment. Study results of current data placement approaches have been analyzed and compared in this paper. Combining the semi-definite programming algorithm with the dynamic interval mapping algorithm, a hierarchical structure data placement strategy is proposed. The semi-definite programming algorithm is suitable for the placement of files with various replications, ensuring that different replications of a file are placed on different storage devices. And the dynamic interval mapping algorithm could guarantee better self-adaptability of the data storage system. It has been proved both by theoretical analysis and experiment demonstration that a hierarchical data placement strategy could guarantee the self-adaptability, data reliability and high-speed data access for large-scale networks.

scholarly journals Monitoring and Modeling Railway Structures on High-Speed Lines with Asphalt Concrete Underlay: A Study on the Bretagne–Pays de la Loire Line

2020 ◽  
Vol 2674 (12) ◽  
pp. 600-607
Author(s):  
Diana Khairallah ◽  
Olivier Chupin ◽  
Juliette Blanc ◽  
Pierre Hornych ◽  
Jean-Michel Piau ◽  
...  
Keyword(s):  
Asphalt Concrete ◽  
High Speed ◽  
Large Scale ◽  
Strain Gages ◽  
Railway Transportation ◽  
Dynamic Stresses ◽  
High Speed Railway ◽  
High Frequencies ◽  
High Acceleration ◽  
High Speed Trains

The design and durability of high-speed railway lines is a major challenge in the field of railway transportation. In France, 40 years of feedback on the field behavior of ballasted tracks led to improvements in the design rules. However, the settlement and wear of ballast, caused by dynamic stresses at high frequencies, remains a major problem on high-speed tracks leading to high maintenance costs. Studies have shown that this settlement is linked to the high acceleration produced in the ballast layer by high-speed trains traveling on the track, disrupting the granular assembly. The “Bretagne–Pays de la Loire” high-speed line (BPL HSL), with its varied subgrade conditions, represents the first large-scale application of asphalt concrete (GB) as the ballast sublayer. This line includes 77 km of conventional track with a granular sublayer of unbound granular material (UGM) and 105 km of track with an asphalt concrete sublayer under the ballast. During construction, instruments such as accelerometers, anchored deflection sensors, and strain gages, among others, were installed on four sections of the track. This paper examines the instrumentation as well as the acquisition system installed on the track. The data processing is explained first, followed by a presentation of the ViscoRail software, developed for modeling railway tracks. The bituminous section’s behavior and response is modeled using a multilayer dynamic response model, implemented in the ViscoRail software. A good match between experimental and calculated results is highlighted.

Dynamic Scheduling Model and Algorithm for Dispatching Trains in High-Speed Railway Passenger Station

2010 ◽  
Author(s):  
Yixiang Yue ◽  
Leishan Zhou
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Job Shop ◽  
Dynamic Scheduling ◽  
Time Window ◽  
Real Life ◽  
Greedy Heuristic ◽  
Railway Station ◽  
High Speed Railway ◽  
Railway Passenger

Regarding the railway station tracks and train running routes as machines, all trains in this railway station as jobs, dispatching trains in high-speed railway passenger stations can be considered as a special type of Job-Shop Problem (JSP). In this paper, we proposed a multi-machines, multi-jobs JSP model with special constraints for Operation Plan Scheduling Problem (OPSP) in high-speed railway passenger stations, and presented a fast heuristic algorithm based on greedy heuristic. This algorithm first divided all operations into several layers according to the yards attributes and the operation’s urgency level. Then every operation was allotted a feasible time window, each operation was assigned to a specified “machine” sequenced or backward sequenced within the time slot, layer by layer according to its priority. As we recorded and modified the time slots dynamically, the searching space was decreased dramatically. And we take the South Beijing High-speed Railway Station as example and give extensive numerical experiment. Computational results based on real-life instance show that the algorithm has significant merits for large scale problems; can both reduce tardiness and shorten cycle times. The empirical evidence also proved that this algorithm is industrial practicable.

scholarly journals Optimization Problem of Pricing and Seat Allocation Based on Bilevel Multifollower Programming in High-Speed Railway

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lianbo Deng ◽  
Jing Xu ◽  
Ningxin Zeng ◽  
Xinlei Hu
Keyword(s):  
Optimization Model ◽  
Dynamic Pricing ◽  
High Speed ◽  
Large Scale ◽  
Solution Method ◽  
Programming Model ◽  
Divide And Conquer ◽  
Small Scale ◽  
High Speed Railway ◽  
Seat Allocation

This paper studies the multistage pricing and seat allocation problems for multiple train services in a high-speed railway (HSR) with multiple origins and destinations (ODs). Taking the maximum total revenue of all trains as the objective function, a joint optimization model of multistage pricing and seat allocation is established. The actual operation constraints, including train seat capacity constraints, price time constraints in each period, and price space constraints among products, are fully considered. We reformulate the optimization model as a bilevel multifollower programming model in which the upper-level model solves the seat allocation problem for all trains serving multiple ODs in the whole booking horizon and the lower optimizes the pricing decisions for each train serving each OD in different decision periods. The upper and lower are a large-scale static seat allocation programming and many small-scale multistage dynamic pricing programming which can be solved independently, respectively. The solving difficulty can be significantly reduced by decomposing. Then, we design an effective solution method based on divide-and-conquer strategy. A real instance of the China’s Wuhan-Guangzhou high-speed railway is employed to validate the advantages of the proposed model and the solution method.

scholarly journals Cloud bursting galaxy: federated identity and access management

2019 ◽  
Vol 36 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Vahid Jalili ◽  
Enis Afgan ◽  
James Taylor ◽  
Jeremy Goecks
Keyword(s):  
Cloud Computing ◽  
High Speed ◽  
Large Scale ◽  
Best Practice ◽  
Data Transfer ◽  
Data Access ◽  
Web Security ◽  
Biomedical Data ◽  
Authentication And Authorization ◽  
Computing Platforms

Abstract Motivation Large biomedical datasets, such as those from genomics and imaging, are increasingly being stored on commercial and institutional cloud computing platforms. This is because cloud-scale computing resources, from robust backup to high-speed data transfer to scalable compute and storage, are needed to make these large datasets usable. However, one challenge for large-scale biomedical data on the cloud is providing secure access, especially when datasets are distributed across platforms. While there are open Web protocols for secure authentication and authorization, these protocols are not in wide use in bioinformatics and are difficult to use for even technologically sophisticated users. Results We have developed a generic and extensible approach for securely accessing biomedical datasets distributed across cloud computing platforms. Our approach combines OpenID Connect and OAuth2, best-practice Web protocols for authentication and authorization, together with Galaxy (https://galaxyproject.org), a web-based computational workbench used by thousands of scientists across the world. With our enhanced version of Galaxy, users can access and analyze data distributed across multiple cloud computing providers without any special knowledge of access/authorization protocols. Our approach does not require users to share permanent credentials (e.g. username, password, API key), instead relying on automatically generated temporary tokens that refresh as needed. Our approach is generalizable to most identity providers and cloud computing platforms. To the best of our knowledge, Galaxy is the only computational workbench where users can access biomedical datasets across multiple cloud computing platforms using best-practice Web security approaches and thereby minimize risks of unauthorized data access and credential use. Availability and implementation Freely available for academic and commercial use under the open-source Academic Free License (https://opensource.org/licenses/AFL-3.0) from the following Github repositories: https://github.com/galaxyproject/galaxy and https://github.com/galaxyproject/cloudauthz.

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