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Journal articles on the topic 'Parallel file systems'

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1

Paterno, Marc, Jim Kowalkowski, and Saba Sehrish. "Parallel Event Selection on HPC Systems." EPJ Web of Conferences 214 (2019): 04059. http://dx.doi.org/10.1051/epjconf/201921404059.

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In their recent measurement of the neutrino oscillation parameters, NOvA uses a sample of approximately 25 million reconstructed spills to search for electron-neutrino appearance events. These events are stored in an n-tuple format, in 250 thousand ROOT files. File sizes range from a few hundred KiB to a few MiB; the full dataset is approximately 1.4 TiB. These millions of events are reduced to a few tens of events by the application of strict event selection criteria, and then summarized by a handful of numbers each, which are used in the extraction of the neutrino oscillation parameters. The
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Kotz, David, and Nils Nieuwejaar. "Flexibility and performance of parallel file systems." ACM SIGOPS Operating Systems Review 30, no. 2 (1996): 63–73. http://dx.doi.org/10.1145/232302.232314.

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3

Hellwagner, H. "Design Considerations for Scalable Parallel File Systems." Computer Journal 36, no. 8 (1993): 741–55. http://dx.doi.org/10.1093/comjnl/36.8.741.

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Huo, Qiu Yan, and Yu Zhang. "Semi-Preemptible Range Lock in Parallel Network File System (pNFS)." Advanced Materials Research 546-547 (July 2012): 1250–55. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1250.

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Distributed file systems use file lock mechanism to ensure consistency when the shared data are accessed by multiple nodes. In this paper, using the feature of distributed systems that the same file would be accessed frequently and the advantage of high concurrency of range lock, the semi-preemptible range lock for pNFS is proposed. Clients locally cache the finer-grained locks for ranges of files they hold. Clients retain or cache range locks even without the file instances. When an access lock is cached, a client answers some requests without a server message, improving performance by exploi
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Reddy, Basireddy Ithihas. "Comparative Analysis of Distributed File Systems." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 20–26. http://dx.doi.org/10.22214/ijraset.2021.37261.

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It has been observed that there has been a great interest in computing experiments which has been useful on shared nothing computers and commodity machines. We need multiple systems running in parallel working closely together towards the same goal. Frequently it has been experienced and observed that the distributed execution engine named MapReduce handles the primary input-output workload for such clusters. There are numerous distributed file systems around viz. NTFS,ReFS,FAT,FAT32 in windows and linux, we studied them and implemented a few distributed file systems. It has been studied that
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Long, Saiqin, Yuelong Zhao, Wei Chen, and Yuanbin Tang. "A prediction-based dynamic file assignment strategy for parallel file systems." Parallel Computing 41 (January 2015): 1–13. http://dx.doi.org/10.1016/j.parco.2014.10.004.

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7

Calderón, A., F. García-Carballeira, L. M. Sánchez, J. D. García, and J. Fernandez. "Fault tolerant file models for parallel file systems: introducing distribution patterns for every file." Journal of Supercomputing 47, no. 3 (2008): 312–34. http://dx.doi.org/10.1007/s11227-008-0199-8.

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8

Kotz, D., and C. S. Ellis. "Caching and Writeback Policies in Parallel File Systems." Journal of Parallel and Distributed Computing 17, no. 1-2 (1993): 140–45. http://dx.doi.org/10.1006/jpdc.1993.1012.

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9

Corbett, P. F., D. G. Feitelson, J. P. Prost, et al. "Parallel file systems for the IBM SP computers." IBM Systems Journal 34, no. 2 (1995): 222–48. http://dx.doi.org/10.1147/sj.342.0222.

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10

Keerthivasan, R., K. S. Srivastavan Iyer, Vyshnav A.K, and M. Vishal. "Visual Cryptography Based Authentication For Parallel Network File Systems." International Journal of Computer Sciences and Engineering 5, no. 10 (2017): 320–23. http://dx.doi.org/10.26438/ijcse/v5i10.320323.

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11

Carretero, J., F. Pérez, P. de Miguel, F. García, and L. Alonso. "Performance increase mechanisms for parallel and distributed file systems." Parallel Computing 23, no. 4-5 (1997): 525–42. http://dx.doi.org/10.1016/s0167-8191(97)00012-4.

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12

Lim, Hoon Wei, and Guomin Yang. "Authenticated Key Exchange Protocols for Parallel Network File Systems." IEEE Transactions on Parallel and Distributed Systems 27, no. 1 (2016): 92–105. http://dx.doi.org/10.1109/tpds.2015.2388447.

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13

Sathya, S., M. Ranjith Kumar, and K. Madheswaran. "Parallel network file systems using authenticated key exchange protocols." Journal of Applied and Advanced Research 2, no. 3 (2017): 161. http://dx.doi.org/10.21839/jaar.2017.v2i3.89.

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The keyestablishment for secure many-to-many communications is very important nowadays. The problem is inspired by the proliferation of large-scale distributed file systems supporting parallel access to multiple storage devices. In this, a variety of authenticated key exchange protocols that are designed to address the issues. This shows that these protocols are capable of reducing the workload of the metadata server and concurrently supporting forward secrecy and escrow-freeness. All this requires only a small fraction of increased computation overhead at the client. This proposed three authe
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14

Ching, Avery, Alok Choudhary, Wei-Keng Liao, Robert Ross, and William Gropp. "Evaluating structured I/O methods for parallel file systems." International Journal of High Performance Computing and Networking 2, no. 2/3/4 (2004): 133. http://dx.doi.org/10.1504/ijhpcn.2004.008898.

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15

Kougkas, Anthony, Hassan Eslami, Xian-He Sun, Rajeev Thakur, and William Gropp. "Rethinking key–value store for parallel I/O optimization." International Journal of High Performance Computing Applications 31, no. 4 (2016): 335–56. http://dx.doi.org/10.1177/1094342016677084.

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Key–value stores are being widely used as the storage system for large-scale internet services and cloud storage systems. However, they are rarely used in HPC systems, where parallel file systems are the dominant storage solution. In this study, we examine the architecture differences and performance characteristics of parallel file systems and key–value stores. We propose using key–value stores to optimize overall Input/Output (I/O) performance, especially for workloads that parallel file systems cannot handle well, such as the cases with intense data synchronization or heavy metadata operati
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Kella, Kush K., and Aasia Khanum. "APCFS: Autonomous and Parallel Compressed File System." International Journal of Parallel Programming 39, no. 4 (2010): 522–32. http://dx.doi.org/10.1007/s10766-010-0154-1.

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17

LIAO, JIANWEI. "SELF-TUNING OPTIMIZATION ON STORAGE SERVERS IN PARALLEL FILE SYSTEMS." Journal of Circuits, Systems and Computers 23, no. 04 (2014): 1450052. http://dx.doi.org/10.1142/s0218126614500522.

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This paper proposes a framework, which requires keeping tracks of both logical I/O access operations and their corresponding physical access on the storage servers in a parallel file system, to build a self-tuning storage system. Thus, the built self-tuning storage system is able to support dynamical data migrating and data pre-fetching transparently from the view point of clients to boost I/O performance. To this end, we first devised an approach to find out the pairs of logical I/O operations and their associated physical I/O operations. We then employed working set modeling to form I/O acce
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18

Rao, G. Narasinga, and Srinivasan Nagaraj. "Client Level Framework for Parallel Downloading of Large File Systems." International Journal of Computer Applications 3, no. 2 (2010): 32–38. http://dx.doi.org/10.5120/707-991.

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19

Boito, Francieli Zanon, Rodrigo Virote Kassick, Philippe O. A. Navaux, and Yves Denneulin. "Automatic I/O scheduling algorithm selection for parallel file systems." Concurrency and Computation: Practice and Experience 28, no. 8 (2015): 2457–72. http://dx.doi.org/10.1002/cpe.3606.

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20

Li, Yong, Dan Feng, Zhan Shi, and Ying Zheng. "A probability-based load balancing algorithm for parallel file systems." Journal of the Chinese Institute of Engineers 38, no. 6 (2015): 811–20. http://dx.doi.org/10.1080/02533839.2015.1027739.

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21

Zhang, Quan, Dan Feng, Fang Wang, and Sen Wu. "Mlock: building delegable metadata service for the parallel file systems." Science China Information Sciences 58, no. 3 (2014): 1–14. http://dx.doi.org/10.1007/s11432-014-5194-5.

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22

He, Qin Lu, Zhan Huai Li, Le Xiao Wang, Hui Feng Wang, and Jian Sun. "Performance Measurement Technique of Cloud Storage System." Advanced Materials Research 760-762 (September 2013): 1197–201. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1197.

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Researches on technologies about testing aggregate bandwidth of file systems in cloud storage systems. Through the memory file system, network file system, parallel file system theory analysis, according to the cloud storage system polymerization bandwidth and concept, developed to cloud storage environment file system polymerization bandwidth test software called FSPoly. In this paper, use FSpoly to luster file system testing, find reasonable test methods, and then evaluations latest development in cloud storage system file system performance by using FSPoly.
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23

Liao, Jianwei, Guoqiang Xiao, and Xiaoning Peng. "Log-Less Metadata Management on Metadata Server for Parallel File Systems." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/813521.

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This paper presents a novel metadata management mechanism on the metadata server (MDS) for parallel and distributed file systems. In this technique, the client file system backs up the sent metadata requests, which have been handled by the metadata server, so that the MDS does not need to log metadata changes to nonvolatile storage for achieving highly available metadata service, as well as better performance improvement in metadata processing. As the client file system backs up certain sent metadata requests in its memory, the overhead for handling these backup requests is much smaller than t
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24

Shenbaga Bharatha Priya, A., J. Ganesh, and Mareeswari M. Devi. "Dynamic Load Rebalancing Algorithm for Private Cloud." Applied Mechanics and Materials 573 (June 2014): 556–59. http://dx.doi.org/10.4028/www.scientific.net/amm.573.556.

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Infrastructure-As-A-Service (IAAS) provides an environmental setup under any type of cloud. In Distributed file system (DFS), nodes are simultaneously serve computing and storage functions; that is parallel Data Processing and storage in cloud. Here, file is considered as a data or load. That file is partitioned into a number of File chunks (FC) allocated in distinct nodes so that Map Reduce tasks can be performed in parallel over the nodes. Files and Nodes can be dynamically created, deleted, and added. This results in load imbalance in a distributed file system; that is, the file chunks are
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25

Margaris, Athanasios I. "Log File Formats for Parallel Applications: A Review." International Journal of Parallel Programming 37, no. 2 (2009): 195–222. http://dx.doi.org/10.1007/s10766-009-0093-x.

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26

Piernas-Canovas, Juan, and Jarek Nieplocha. "Implementation and evaluation of active storage in modern parallel file systems." Parallel Computing 36, no. 1 (2010): 26–47. http://dx.doi.org/10.1016/j.parco.2009.11.002.

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27

Lafta, Inam Abdullah, Ruaa Ali Khamees, and Rana Alauldeen Abdalrahman. "Key Exchange Protocols for Parallel Network File Systems Using Optimized Cryptography." IOP Conference Series: Materials Science and Engineering 881 (August 11, 2020): 012108. http://dx.doi.org/10.1088/1757-899x/881/1/012108.

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28

He, Xubin, Li Ou, Christian Engelmann, Xin Chen, and Stephen L. Scott. "Symmetric active/active metadata service for high availability parallel file systems." Journal of Parallel and Distributed Computing 69, no. 12 (2009): 961–73. http://dx.doi.org/10.1016/j.jpdc.2009.08.004.

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29

Arunachalam, Meenakshi, and Alok Choudhary. "A prefetching prototype for the parallel file systems on the Paragon." ACM SIGMETRICS Performance Evaluation Review 23, no. 1 (1995): 321–22. http://dx.doi.org/10.1145/223586.223631.

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30

Huang, Dan, Dezhi Han, Jun Wang, et al. "Achieving Load Balance for Parallel Data Access on Distributed File Systems." IEEE Transactions on Computers 67, no. 3 (2018): 388–402. http://dx.doi.org/10.1109/tc.2017.2749229.

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31

Song, Huaiming, Yanlong Yin, Yong Chen, and Xian-He Sun. "Cost-intelligent application-specific data layout optimization for parallel file systems." Cluster Computing 16, no. 2 (2012): 285–98. http://dx.doi.org/10.1007/s10586-012-0200-4.

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32

Liu, Yonggang, Jing Qin, and Renato Figueiredo. "The dispatch time aligning I/O scheduling for parallel file systems." Cluster Computing 18, no. 3 (2015): 1025–39. http://dx.doi.org/10.1007/s10586-015-0457-5.

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33

Li, Xiuqiao, Limin Xiao, Meikang Qiu, Bin Dong, and Li Ruan. "Enabling dynamic file I/O path selection at runtime for parallel file system." Journal of Supercomputing 68, no. 2 (2013): 996–1021. http://dx.doi.org/10.1007/s11227-013-1077-6.

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34

Fisher, Meghan A., Pádraig Ó. Conbhuí, Cathal Ó. Brion, et al. "ExSeisDat: A set of parallel I/O and workflow libraries for petroleum seismology." Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles 73 (2018): 74. http://dx.doi.org/10.2516/ogst/2018048.

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Seismic data-sets are extremely large and are broken into data files, ranging in size from 100s of GiBs to 10s of TiBs and larger. The parallel I/O for these files is complex due to the amount of data along with varied and multiple access patterns within individual files. Properties of legacy file formats, such as the de-facto standard SEG-Y, also contribute to the decrease in developer productivity while working with these files. SEG-Y files embed their own internal layout which could lead to conflict with traditional, file-system-level layout optimization schemes. Additionally, as seismic fi
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35

Miller, L. L., S. R. Inglett, and A. R. Hurson. "PASS—A multiuser parallel file system based on microcomputers." Journal of Systems and Software 19, no. 1 (1992): 75–83. http://dx.doi.org/10.1016/0164-1212(92)90021-b.

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36

Wu, Qi Meng, Ke Xie, Ming Fa Zhu, Li Min Xiao, and Li Ruan. "DMFSsim: A Distributed Metadata File System Simulator." Applied Mechanics and Materials 241-244 (December 2012): 1556–61. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1556.

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Parallel file systems deploy multiple metadata servers to distribute heavy metadata workload from clients. With the increasing number of metadata servers, metadata-intensive operations are facing some problems related with collaboration among them, compromising the performance gain. Consequently, a file system simulator is very helpful to try out some optimization ideas to solve these problems. In this paper, we propose DMFSsim to simulate the metadata-intensive operations on large-scale distributed metadata file systems. DMFSsim can flexibly replay traces of multiple metadata operations, supp
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Bu, Lingrui, Hui Zhang, Haiyan Xing, and Lijun Wu. "Research on parallel data processing of data mining platform in the background of cloud computing." Journal of Intelligent Systems 30, no. 1 (2021): 479–86. http://dx.doi.org/10.1515/jisys-2020-0113.

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Abstract The efficient processing of large-scale data has very important practical value. In this study, a data mining platform based on Hadoop distributed file system was designed, and then K-means algorithm was improved with the idea of max-min distance. On Hadoop distributed file system platform, the parallelization was realized by MapReduce. Finally, the data processing effect of the algorithm was analyzed with Iris data set. The results showed that the parallel algorithm divided more correct samples than the traditional algorithm; in the single-machine environment, the parallel algorithm
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He, Shuibing, Yang Wang, Xian-He Sun, and Chengzhong Xu. "HARL: Optimizing Parallel File Systems with Heterogeneity-Aware Region-Level Data Layout." IEEE Transactions on Computers 66, no. 6 (2017): 1048–60. http://dx.doi.org/10.1109/tc.2016.2637905.

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39

Yu, Yang, Yongqing Zhu, Willie Ng, Juniarto Samsudin, and Zhixiang Li. "A File Assignment Strategy Towards Minimized Response Time for Parallel Storage Systems." IEEE Transactions on Magnetics 49, no. 6 (2013): 2459–65. http://dx.doi.org/10.1109/tmag.2013.2252157.

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40

Kim, Dong-Oh, Hong-Yeon Kim, Young-Kyun Kim, and Jeong-Joon Kim. "Efficient techniques of parallel recovery for erasure-coding-based distributed file systems." Computing 101, no. 12 (2019): 1861–84. http://dx.doi.org/10.1007/s00607-019-00714-7.

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41

Ding, Wei, Yuanrui Zhang, Mahmut Kandemir, and Seung Woo Son. "Compiler-Directed File Layout Optimization for Hierarchical Storage Systems." Scientific Programming 21, no. 3-4 (2013): 65–78. http://dx.doi.org/10.1155/2013/167581.

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File layout of array data is a critical factor that effects the behavior of storage caches, and has so far taken not much attention in the context of hierarchical storage systems. The main contribution of this paper is a compiler-driven file layout optimization scheme for hierarchical storage caches. This approach, fully automated within an optimizing compiler, analyzes a multi-threaded application code and determines a file layout for each disk-resident array referenced by the code, such that the performance of the target storage cache hierarchy is maximized. We tested our approach using 16 I
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42

Page, G. J. "Rapid, parallel CFD grid generation using octrees." Aeronautical Journal 117, no. 1188 (2013): 133–46. http://dx.doi.org/10.1017/s0001924000007910.

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Abstract As Large Eddy Simulation is increasingly applied to flows containing complex geometry, grid generation becomes difficult and time consuming when using software originally developed for RANS flow solvers. The traditional ‘pipeline’ approach of grid generation → solve → visualise entails the time consuming transfer of large files and conversion of file formats. This work demonstrates a grid generation methodology developed specifically to be integrated with parallel LES. The current approach is to use a Cartesian grid with adaptive refinement based upon geometry intersection, surface de
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43

LIU, NING, JING FU, CHRISTOPHER D. CAROTHERS, ONKAR SAHNI, KENNETH E. JANSEN, and MARK S. SHEPHARD. "MASSIVELY PARALLEL I/O FOR PARTITIONED SOLVER SYSTEMS." Parallel Processing Letters 20, no. 04 (2010): 377–95. http://dx.doi.org/10.1142/s0129626410000302.

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This paper investigates I/O approaches for massively parallel partitioned solver systems. Typically, such systems have synchronized "loops" and write data in a well defined block I/O format consisting of a header and data portion. Our target use for such a parallel I/O subsystem is checkpoint-restart where writing is by far the most common operation and reading typically only happens during either initialization or during a restart operation because of a system failure. We compare four parallel I/O strategies: POSIX File Per Processor (1PFPP), "Poor-Man's" Parallel I/O (PMPIO), a synchronized
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44

Mac, Su-Cheong, Ce-Kuen Shieh, and Jyh-Biau Chang. "Design and analysis of a parallel file system for distributed shared memory systems." Journal of Systems Architecture 45, no. 8 (1999): 603–17. http://dx.doi.org/10.1016/s1383-7621(98)00003-4.

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Shieh, Ce-Kuen, Su-Cheong Mac, and Jyh-Chang Ueng. "Improving the performance of distributed shared memory systems via parallel file input/output." Journal of Systems and Software 44, no. 1 (1998): 3–15. http://dx.doi.org/10.1016/s0164-1212(98)10039-0.

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46

Tavakoli, Neda, Dong Dai, and Yong Chen. "Client-side straggler-aware I/O scheduler for object-based parallel file systems." Parallel Computing 82 (February 2019): 3–18. http://dx.doi.org/10.1016/j.parco.2018.07.001.

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47

Lin-Wen Lee, P. Scheuermann, and R. Vingralek. "File assignment in parallel I/O systems with minimal variance of service time." IEEE Transactions on Computers 49, no. 2 (2000): 127–40. http://dx.doi.org/10.1109/12.833109.

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48

Kim, Jeong-Joon. "Erasure-Coding-Based Storage and Recovery for Distributed Exascale Storage Systems." Applied Sciences 11, no. 8 (2021): 3298. http://dx.doi.org/10.3390/app11083298.

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Various techniques have been used in distributed file systems for data availability and stability. Typically, a method for storing data in a replication technique-based distributed file system is used, but due to the problem of space efficiency, an erasure-coding (EC) technique has been utilized more recently. The EC technique improves the space efficiency problem more than the replication technique does. However, the EC technique has various performance degradation factors, such as encoding and decoding and input and output (I/O) degradation. Thus, this study proposes a buffering and combinin
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Ma, Yung-Cheng, Chung-Ping Chung, and Tien-Fu Chen. "Load and storage balanced posting file partitioning for parallel information retrieval." Journal of Systems and Software 84, no. 5 (2011): 864–84. http://dx.doi.org/10.1016/j.jss.2011.01.028.

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Fukuda, Munehiro, and Jumpei Miyauchi. "An implementation of parallel file distribution in an agent hierarchy." Journal of Supercomputing 47, no. 3 (2008): 255–85. http://dx.doi.org/10.1007/s11227-008-0194-0.

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