Relevant bibliographies by topics / HDFS (Hadoop Distributed File System)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'HDFS (Hadoop Distributed File System)'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "HDFS (Hadoop Distributed File System)"

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Gupta, Manish Kumar, and Rajendra Kumar Dwivedi. "Blockchain Enabled Hadoop Distributed File System Framework for Secure and Reliable Traceability." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 12 (December 29, 2023): e31478. http://dx.doi.org/10.14201/adcaij.31478.

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Hadoop Distributed File System (HDFS) is a distributed file system that allows large amounts of data to be stored and processed across multiple servers in a Hadoop cluster. HDFS also provides high throughput for data access. HDFS enables the management of vast amounts of data using commodity hardware. However, security vulnerabilities in HDFS can be manipulated for malicious purposes. This emphasizes the significance of establishing strong security measures to facilitate file sharing within Hadoop and implementing a reliable mechanism for verifying the legitimacy of shared files. The objective
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Mohammad, Bahjat Al-Masadeh, Sanusi Azmi Mohd, and Sakinah Syed Ahmad Sharifah. "Tiny datablock in saving Hadoop distributed file system wasted memory." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1757–72. https://doi.org/10.11591/ijece.v13i2.pp1757-1772.

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Hadoop distributed file system (HDFS) is the file system whereby Hadoop is use it to store all the upcoming data inside it. Since it been declared, HDFS is consuming a huge memory amount in order to serve a normal dataset. Nonetheless, the current file saving mechanism in HDFS save only one file in one datablock. Thus, a file with just 5 Mb in size will take up the whole datablock capacity causing the rest of the memory unavailable for other upcoming files, and this is considered a huge waste of memory in serving a normal size dataset. This paper proposed a method called tiny datablockHDFS (TD
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Kapil, Gayatri, Alka Agrawal, Abdulaziz Attaallah, Abdullah Algarni, Rajeev Kumar, and Raees Ahmad Khan. "Attribute based honey encryption algorithm for securing big data: Hadoop distributed file system perspective." PeerJ Computer Science 6 (February 17, 2020): e259. http://dx.doi.org/10.7717/peerj-cs.259.

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Hadoop has become a promising platform to reliably process and store big data. It provides flexible and low cost services to huge data through Hadoop Distributed File System (HDFS) storage. Unfortunately, absence of any inherent security mechanism in Hadoop increases the possibility of malicious attacks on the data processed or stored through Hadoop. In this scenario, securing the data stored in HDFS becomes a challenging task. Hence, researchers and practitioners have intensified their efforts in working on mechanisms that would protect user’s information collated in HDFS. This has led to the
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Awasthi, Yogesh. "Enhancing approach for information security in Hadoop." Ukrainian Journal of Educational Studies and Information Technology 8, no. 1 (2020): 39–49. http://dx.doi.org/10.32919/uesit.2020.01.04.

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Hadoop, is one of the ongoing patterns in innovation which is utilized as a structure for the distributed storage, is an open-source appropriated figuring structure actualized in Java and comprises two modules that are MapReduce and Hadoop Distributed File System (HDFS). The MapReduce is intended for handling with enormous informational collections, it enables the clients to utilize a huge number of item machines in parallel adequately, by just characterizing guides and lessening capacities, the client can prepare huge amounts of information, HDFS is for putting away information on appropriate
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K., Srikanth* P. Venkateswarlu Ashok Suragala. "A FUNDAMENTAL CONCEPT OF MAPREDUCE WITH MASSIVE FILES DATASET IN BIG DATA USING HADOOP PSEUDO-DISTRIBUTION MODE." Global Journal of Engineering Science and Research Management 4, no. 5 (2017): 58–62. https://doi.org/10.5281/zenodo.801301.

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Hadoop Distributed File System (HDFS) and MapReduce programming model is used for storage and retrieval of the big data. Big data can be any structured collection which results incapability of conventional data management methods. The Tera Bytes size file can be easily stored on the HDFS and can be analyzed with MapReduce. This paper provides introduction to Hadoop HDFS and MapReduce for storing large number of files and retrieve information from these files. In this paper we present our experimental work done on Hadoop by applying a number of files as input to the system and then analyzing th
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Malik, Vandana. "Hadoop Distributed File System (HDFS) with Its Architecture." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6031–34. https://doi.org/10.22214/ijraset.2025.71584.

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The exponential growth of big data has catalyzed the development of robust, scalable, and fault-tolerant storage systems. The Hadoop Distributed File System (HDFS) stands as a key pillar in the Hadoop ecosystem, providing a distributed, resilient storage infrastructure for managing petabytes of data. This paper investigates the core architecture of HDFS, including its design principles, components, and operational workflow. It also analyzes practical implementations, advantages, limitations, and future trends. Through case studies and real-world applications, the paper illustrates how HDFS sup
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Sudirman, Ahmad, Irawan, and Zawiyah Saharuna. "PENGEMBANGAN SISTEM BIG DATA: RANCANG BANGUN INFRASTRUKTUR DENGAN FRAMEWORK HADOOP." Journal of Informatics and Computer Engineering Research 1, no. 1 (2024): 25–32. https://doi.org/10.31963/jicer.v1i1.4919.

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Hadoop is a distributed data storage platform that provides a parallel processing framework. HDFS (Hadoop Distributed File System) and Map Reduce are two very important parts of Hadoop, HDFS is a distributed storage system built on java, while Map Reduce is a program for managing large data in parallel and distributed. The research focused on testing the data transfer speed on HDFS, testing using 3 types of data, namely video, ISO Image, and text with a total of 512 MB, 1 GB, and 2 GB of data. Testing will be carried out by entering data into HDFS using the Hadoop command and changing the size
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Awasthi, Yogesh, and Ashish Sharma. "Enhancing Approach for Information Security in Hadoop." JITCE (Journal of Information Technology and Computer Engineering) 4, no. 01 (2020): 5–9. http://dx.doi.org/10.25077/jitce.4.01.5-9.2020.

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Using the Hadoop package, a proposed secure cloud computing system was designed. Hadoop would use the area to establish and enhance the security for saving and managing user data. Apache had also produced a software tool termed Hadoop to overcome this big data problem which often uses MapReduce architecture to process vast amounts of data. Hadoop has no strategy to assure the security and privacy of the files stored in Hadoop distributed File system (HDFS). As an encryption scheme of the files stored in HDFS, an asymmetric key cryptosystem is advocated. Thus before saving data in HDFS, the pro
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Han, Yong Qi, Yun Zhang, and Shui Yu. "Research of Cloud Storage Based on Hadoop Distributed File System." Applied Mechanics and Materials 513-517 (February 2014): 2472–75. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2472.

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This paper discusses the application of cloud computing technology to store large amount of data in agricultural remote training video and other multimedia data, using four computers to build a Hadoop cloud platform, focus on the Hadoop Distributed File System (HDFS) principle and file storage, to achieve massive agricultural multimedia data storage.
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Zine-Dine, Fayçal, Sara Alcabnani, Ahmed Azouaoui, and Jamal El Kafi. "Enhance big data security based on HDFS using the hybrid approach." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 1256. https://doi.org/10.11591/ijeecs.v38.i2.pp1256-1264.

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Hadoop has emerged as a prominent open-source framework for the storage, management, and processing of extensive big data through its distributed file system, known as Hadoop distributed file system (HDFS). This widespread adoption can be attributed to its capacity to provide reliable, scalable, and cost-effective solutions for managing large datasets across diverse sectors, including finance, healthcare, and social media. Nevertheless, as the significance and scale of big data applications continue to expand, the challenge of ensuring the security and safeguarding of sensitive data within Had
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Dissertations / Theses on the topic "HDFS (Hadoop Distributed File System)"

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Musatoiu, Mihai. "An approach to choosing the right distributed file system : Microsoft DFS vs. Hadoop DFS." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-844.

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Context. An important goal of most IT groups is to manage server resources in such a way that their users are provided with fast, reliable and secure access to files. The modern needs of organizations imply that resources are often distributed geographically, asking for new design solutions for the file systems to remain highly available and efficient. This is where distributed file systems (DFSs) come into the picture. A distributed file system (DFS), as opposed to a "classical", local, file system, is accessible across some kind of network and allows clients to access files remotely as if th
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Bhat, Adithya. "RDMA-based Plugin Design and Profiler for Apache and Enterprise Hadoop Distributed File system." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440188090.

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Polato, Ivanilton. "Energy savings and performance improvements with SSDs in the Hadoop Distributed File System." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-31102016-155908/.

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Energy issues gathered strong attention over the past decade, reaching IT data processing infrastructures. Now, they need to cope with such responsibility, adjusting existing platforms to reach acceptable performance while promoting energy consumption reduction. As the de facto platform for Big Data, Apache Hadoop has evolved significantly over the last years, with more than 60 releases bringing new features. By implementing the MapReduce programming paradigm and leveraging HDFS, its distributed file system, Hadoop has become a reliable and fault tolerant middleware for parallel and distribute
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Careres, Gutierrez Franco Jesus. "Towards an S3-based, DataNode-lessimplementation of HDFS." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291125.

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The relevance of data processing and analysis today cannot be overstated. The convergence of several technological advancements has fostered the proliferation of systems and infrastructure that together support the generation, transmission, and storage of nearly 15,000 exabytes of digital, analyzabledata. The Hadoop Distributed File System (HDFS) is an open source system designed to leverage the storage capacity of thousands of servers, and is the file system component of an entire ecosystem of tools to transform and analyze massive data sets. While HDFS is used by organizations of all sizes,
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Caceres, Gutierrez Franco Jesus. "Towards an S3-based, DataNode-less implementation of HDFS." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291125.

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The relevance of data processing and analysis today cannot be overstated. The convergence of several technological advancements has fostered the proliferation of systems and infrastructure that together support the generation, transmission, and storage of nearly 15,000 exabytes of digital, analyzabledata. The Hadoop Distributed File System (HDFS) is an open source system designed to leverage the storage capacity of thousands of servers, and is the file system component of an entire ecosystem of tools to transform and analyze massive data sets. While HDFS is used by organizations of all sizes,
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Benkő, Krisztián. "Zpracování velkých dat z rozsáhlých IoT sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403820.

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The goal of this diploma thesis is to design and develop a system for collecting, processing and storing data from large IoT networks. The developed system introduces a complex solution able to process data from various IoT networks using Apache Hadoop ecosystem. The data are real-time processed and stored in a NoSQL database, but the data are also stored  in the file system for a potential later processing. The system is optimized and tested using data from IQRF network. The data stored in the NoSQL database are visualized and the system periodically generates derived predictions. Users are c
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Lorenzetto, Luca <1988&gt. "Evaluating performance of Hadoop Distributed File System." Master's Degree Thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/4773.

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In recent years, a huge quantity of data produced by multiple sources has appeared. Dealing with this data has arisen the so called "big data problem", which can be faced only with new computing paradigms and platforms. Many vendors compete in this field, but at this day the de-facto standard platform for big-data is the opensource framework Apache Hadoop . Inspired by Google's private cluster platform, some indipendent developers created Hadoop and, following the structure published by Google's engineering team, a complete set of components for big data elaboration has been developed. One of
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Pradeep, Aakash. "P2PHDFS: AN IMPLEMENTATION OF STATISTIC MULTIPLEXED COMPUTING ARCHITECTURE IN HADOOP FILE SYSTEM." Master's thesis, Temple University Libraries, 2012. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/214757.

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Computer and Information Science<br>M.S.<br>The Peer to Peer Hadoop Distributed File System (P2PHDFS) is designed to store and process extremely large-scale data sets reliably. This is a first attempt implementation of the Statistic Multiplexed Computing Architecture concept proposed by Dr. Shi for the existing Hadoop File System (HDFS) to eliminate all single point failures. Unlike HDFS, in P2PHDFS every node is designed to be equal and behaves as a file system server as well as slave, which enable it to attain higher performance and higher reliability at the same time as the infrastructure u
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Cheng, Lu. "Concentric layout, a new scientific data layout for matrix data set in Hadoop file system." Master's thesis, University of Central Florida, 2010. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4545.

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The data generated by scientific simulation, sensor, monitor or optical telescope has increased with dramatic speed. In order to analyze the raw data speed and space efficiently, data pre-process operation is needed to achieve better performance in data analysis phase. Current research shows an increasing tread of adopting MapReduce framework for large scale data processing. However, the data access patterns which generally applied to scientific data set are not supported by current MapReduce framework directly. The gap between the requirement from analytics application and the property of Map
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Sodhi, Bir Apaar Singh. "DATA MINING: TRACKING SUSPICIOUS LOGGING ACTIVITY USING HADOOP." CSUSB ScholarWorks, 2016. https://scholarworks.lib.csusb.edu/etd/271.

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In this modern rather interconnected era, an organization’s top priority is to protect itself from major security breaches occurring frequently within a communicational environment. But, it seems, as if they quite fail in doing so. Every week there are new headlines relating to information being forged, funds being stolen and corrupt usage of credit card and so on. Personal computers are turned into “zombie machines” by hackers to steal confidential and financial information from sources without disclosing hacker’s true identity. These identity thieves rob private data and ruin the very purpos
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Books on the topic "HDFS (Hadoop Distributed File System)"

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Mishra, Kartikeya. Guide to Big Data Hadoop Distributed File System: A Book for Beginners/intermediate. Independently Published, 2020.

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Hadoop. O'Reilly Media, Incorporated, 2009.

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Hadoop. O'Reilly Media, Incorporated, 2012.

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Turkington, Garry. Hadoop Beginner's Guide. Packt Publishing, Limited, 2013.

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Hadoop Beginner's Guide. Packt Publishing, 2013.

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Hadoop: The Definitive Guide. 3rd ed. O'Reilly Media, Inc, 2012.

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Hadoop: The Definitive Guide. O'Reilly, 2009.

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Hadoop: The Definitive Guide. O'Reilly Media, Incorporated, 2009.

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Hadoop: The definitive guide. O'Reilly, 2015.

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Hadoop Real-World Solutions Cookbook. Packt Publishing, Limited, 2013.

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Book chapters on the topic "HDFS (Hadoop Distributed File System)"

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Siva Reddy, M., and S. Christy. "Data dispersion and encryption of stored data using Advanced Encryption Standard (AES) compared with Hadoop Distributed File System (HDFS) for cloud secure storage mechanism." In Hybrid and Advanced Technologies. CRC Press, 2025. https://doi.org/10.1201/9781003559115-70.

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Xu, JunWu, and JunLing Liang. "Research on Distributed File System with Hadoop." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35211-9_19.

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Bass, Len, Rick Kazman, and Ipek Ozkaya. "Developing Architectural Documentation for the Hadoop Distributed File System." In IFIP Advances in Information and Communication Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24418-6_4.

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Bari, Wasim, Ahmed Shiraz Memon, and Bernd Schuller. "Enhancing UNICORE Storage Management Using Hadoop Distributed File System." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14122-5_39.

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Lakshmi Siva Rama Krishna, T., T. Ragunathan, and Sudheer Kumar Battula. "Customized Web User Interface for Hadoop Distributed File System." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2523-2_55.

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Rajput, Dilip, Ankur Goyal, and Abhishek Tripathi. "Priority-Based Replication Management for Hadoop Distributed File System." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9113-3_40.

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Mehta, Sakshi, and Rishi Prakash Shukla. "The architecture and design of the hadoop distributed file system." In Artificial Intelligence and Information Technologies. CRC Press, 2024. http://dx.doi.org/10.1201/9781003510833-5.

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Goyal, Mani, Navneet Garg, Neelam Oberoi, and Vaishali Mehta. "Resource Management in Hadoop Clusters at the Storing Level of Hadoop Distributed File System." In Multi-Strategy Learning Environment. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1488-9_39.

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Chattaraj, Durbadal, Sumit Bhagat, and Monalisa Sarma. "Storage Service Reliability and Availability Predictions of Hadoop Distributed File System." In Reliability, Safety and Hazard Assessment for Risk-Based Technologies. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9008-1_52.

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Samanta, Pravin Kumar, Soumyadev Mukherjee, and Nirmal Kumar Rout. "Susceptibility Analysis of Novel Corona Virus Using Hadoop Distributed File System." In Advances in Smart Communication Technology and Information Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9433-5_33.

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Conference papers on the topic "HDFS (Hadoop Distributed File System)"

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Rathnam, Martin Joel, S. LakshmiNarayanan, B. Sunantha Sri, V. Manickavasagan, M. Arulprakasam, and S. M. Swetha. "Chaotic Leader Particle swarm optimization (CL-PSO) for efficient data storage in a cloud-based Hadoop distributed file system (HDFS)." In 2024 4th International Conference on Intelligent Technologies (CONIT). IEEE, 2024. http://dx.doi.org/10.1109/conit61985.2024.10627079.

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Wang, Miao, Yanming Zhang, and Hualei Wang. "SF-SNF: A Small-file Sniffer System for Hadoop Clusters." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00226.

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Mishra, Anupam, and Nitika Kapoor. "Encryption Techniques for Hadoop Distributed File System (HDFS)." In 2023 5th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2023. http://dx.doi.org/10.1109/icac3n60023.2023.10541424.

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Agrawal, Bikash, Raymond Hansen, Chunming Rong, and Tomasz Wiktorski. "SD-HDFS: Secure Deletion in Hadoop Distributed File System." In 2016 IEEE International Congress on Big Data (BigData Congress). IEEE, 2016. http://dx.doi.org/10.1109/bigdatacongress.2016.30.

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Liao, Cong, Anna Squicciarini, and Dan Lin. "LAST-HDFS: Location-Aware Storage Technique for Hadoop Distributed File System." In 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). IEEE, 2016. http://dx.doi.org/10.1109/cloud.2016.0093.

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Fazul, Rhauani W., and Patricia Pitthan Barcelos. "Política Customizada de Balanceamento de Réplicas para o HDFS Balancer do Apache Hadoop." In XX Workshop de Testes e Tolerância a Falhas. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/wtf.2019.7717.

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Data replication is a fundamental mechanism of the Hadoop Distributed File System (HDFS). However, the way data is spread across the cluster directly affects the replication balancing. The HDFS Balancer is a Hadoop integrated tool which can balance the storage load on each machine by moving data between nodes, although its operation does not address the specific needs of applications while performing block rearrangement. This paper proposes a customized balancing policy for HDFS Balancer based on a system of priorities, which can be adapted and configured according to usage demands. The priori
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Adnan, Adnan, Zulkifli Tahir, and Muhammad Arfah Asis. "Performance Evaluation of Single Board Computer for Hadoop Distributed File System (HDFS)." In 2019 International Conference on Information and Communications Technology (ICOIACT). IEEE, 2019. http://dx.doi.org/10.1109/icoiact46704.2019.8938434.

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Fazul, Rhauani Weber Aita, Odorico Machado Mendizabal, and Patrícia Pitthan Barcelos. "Dynamic Architecture for Data Replica Balancing in HDFS: Stability, Efficiency, and Data Locality Evaluations." In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação, 2024. http://dx.doi.org/10.5753/sbrc.2024.1308.

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Hadoop Distributed File System (HDFS) is known for its specialized strategies and policies tailored to enhance replica placement. This capability is critical for ensuring efficient and reliable access to data replicas, particularly as HDFS operates best when data are evenly distributed within the cluster. In this study, we conduct a thorough analysis of the replica balancing process in HDFS, focusing on two critical performance metrics: stability and efficiency. We evaluated these balancing aspects by contrasting them with conventional HDFS solutions and employing a novel dynamic architecture
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Li, Feifei, Wei Zhou, Hao Liu, and Lin Deng. "Research on the Development Strategy of Suzhou's Sustainable Development under Hadoop's HDFS (Hadoop Distributed File System)." In DEBAI 2024: International Conference on Digital Economy, Blockchain and Artificial Intelligence. ACM, 2024. https://doi.org/10.1145/3700058.3700126.

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Qu, Kaiyang, Luoming Meng, and Yang Yang. "A dynamic replica strategy based on Markov model for hadoop distributed file system (HDFS)." In 2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2016. http://dx.doi.org/10.1109/ccis.2016.7790280.

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