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

Abstract

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.

Data Placement in Era of Cloud Computing: a Survey, Taxonomy and Open Research Issues

“…• Effective distribution of data onto a storage device: There is a large variety of storge devices available with different parameters like storage capacity,transmission speed etc. So making the data evenly and considerably distributed among these storage devices is also an important task [4]. • Data placement of data intensive jobs is major challenge in cloud environment.…”

“…The prime process in a cloud computing environment is during the implementation and execution of data intensive applications for meeting the demand of large volumes of data and storing the data at appropriate data center .This case arises the need of data placement strategy [4]. Data placement includes all activities related to movement such as replication,staging,transfer, space de-allocation and space allocation, unregistering and registering meta data, retrieving and locating the data.…”

Intelligent Block Placement Strategy in Heterogeneous Hadoop Clusters

“…A number of researches have been conducted to improve the performance of MapReduce through designing optimal data placement for Hadoop cluster [7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

“…When there are new data or new nodes join into the cluster, the algorithm has to redistribute the whole data which introduce more network overhead and application response time. Hanning et al [15] improve the performance of MapReduce by allocating data according to the disk space of each node. But it does not consider the data locality and data movement overhead.…”