Grain Refinement of Y in As-Cast Mg-Y Alloys

Zhang, Qing; Li, Quan An; Chen, Jun; Zhang, Xingyuan

doi:10.4028/www.scientific.net/amr.750-752.603

Cited by 44 publications

(51 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhang et al [19] proposed merging related small files according to WebGIS application, which improved the storage effeciency and HDFS metadata management, however the results are limited by the scene.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Hadoop for Small File Management

Elmahouti

Achandair

Khoulji

et al. 2017

TMLAI

View full text Add to dashboard Cite

HDFS is one of the most used distributed file systems, that offer a high availability and scalability on low cost hardware. HDFS is delivered as the storage component of Hadoop framework. Coupled with map reduce, which is the processing component, HDFS and MapReduce become the de facto platform for managing big data nowadays. However, HDFS was designed to handle specifically a huge number of large files, while when it comes to a large number of small files, Hadoop deployments may be not efficient. In this paper, we proposed a new strategy to manage small files. Our approach consists of two principal phases. The first phase is about consolidating more than only one client's small files input, and store the inputs continuously in the first allocated block, in a SequenceFile format, and so on into the next blocks. That way we avoid multiple block allocations for different streams, to reduce calls for available blocks and to reduce the metadata memory on the NameNode. This is because groups of small files packaged in a SequenceFile on the same block will require one entry instead of one for each small file. The second phase consists of analyzing attributes of stored small files to distribute them in such a way that the most called files will be referenced by an additional index as a MapFile format to reduce the read throughput during random access.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimizing Hadoop for Small File Management

Elmahouti

Achandair

Khoulji

et al. 2017

TMLAI

View full text Add to dashboard Cite

show abstract

“…In this case power factor control could also be implemented at a lower cost. [2] IV. DOUBLY FED INDUCTION GENERATOR Figure 1 Doubly fed Induction Generator…”

mentioning

confidence: 99%

Modelling of Doubly Fed Induction Generator in Wind Power Generation

2015

IJAERD

View full text Add to dashboard Cite

Abstract-This paper deals with the modelling and simulation of a double-fed induction generator (DFIG) based wind-power unit which is connected to the grid .A detailed model of a DFIG in the S tator flux reference frame, along with its controllers, is developed in this paper. This paper proposes a comprehensive set of differential equations for grid connection of DFIGs that takes into account the dynamics of the grid side line filter and transmission line. Time domain simulations are performed in Matlab/S imulink. Based on the model and simulation results, system stability is investigated for different wind S peed. This paper presents the steady-state model of a variable-speed wind power machine based on a 1.5MW doubly-fed induction generator (DFIG).

show abstract

“…To improve the metadata management, G. Mackey et al [16] merged small files into a single larger file, using the HAR through a MapReduce task. The small files are referenced with an added index layer (Master index, Index) delivered with the archive, to retain the separation and to keep the original structure of files.…”

Section: Related Workmentioning

confidence: 99%

Improving Small File Management in Hadoop

Achandair

Elmahouti

Khoulji³

et al. 2017

TMLAI

View full text Add to dashboard Cite

Hadoop, considered nowadays as the defacto platform for managing big data, has revolutionized the way customers manage their data. As an opensource implementation of mapreduce, it was designed to offer a high scalability and availability across clusters of thousands of machines. Through it two principals' components, which is HDFS for a distributed storage and MapReduce as the distributed processing engine, companies and research studies are taking a big benefit from its capabilities.However, Hadoop was designed to handle large size files, so when it comes to a large number of small files, the performance can be heavily degraded. The small file problem has been well defined by researchers and Hadoop community, but most of the proposed approaches only deal with the pressure caused on the NameNode memory. Certainly, grouping small files in different possible formats, that are most of time supported by the actual Hadoop distribution, reduce the metadata entries and solve the memory limitation, but that remain only a part of the equation. Actually, the real impact that organizations need to solve when dealing with lot of small files, is the cluster performance when those files are processed in Hadoop clusters. In this paper, we proposed a new strategy to use efficiently some one of the common solution that group files in a MapFile format.The core idea, is to organize small files files based on specific attributes in MapFile output files, and use prefetching and caching mechanisms during read access. This would lead to less calls of metadata from the NameNode, and better I/O performance during MapReduce jobs. The experimental results show that this approach can help to obtain better access time when the cluster contain massive number of small files.

show abstract

Grain Refinement of Y in As-Cast Mg-Y Alloys

Cited by 44 publications

References 8 publications

Optimizing Hadoop for Small File Management

Optimizing Hadoop for Small File Management

Modelling of Doubly Fed Induction Generator in Wind Power Generation

Improving Small File Management in Hadoop

Contact Info

Product

Resources

About