Distributed File System Research Articles

Solving blockchain trilemma using off‐chain storage protocol

AbstractTrilemma in blockchain refers to the infamous problem of simultaneously not delivering the three critical aspects of a ledger: security, scalability, and decentralisation. While security and scalability hinder decentralisation, security is jeopardised if the scalability is escalated. This deficiency of not maintaining a balance among these three crucial factors restricts the broader adoption of blockchain technology and cryptocurrencies in the industries. This paper proposes a solution to the blockchain trilemma by implementing a public ledger using The InterPlanetary File System (IPFS) and a newly introduced strategy called the double‐chain technique. The scalability and decentralisation features are guaranteed by the distributed file system of IPFS and the public nature of the blockchain suggested in this study. Although any consensus can be plugged into our system, the proof‐of‐work consensus is utilised to ensure that the security is not compromised while stabilising scalability and decentralisation.

An Aggregator Framework for Transforming Big Data in Real-Time using PT-INDRNN

The prediction of stock market prices based on the financial text sentiment classification using Machine Learning (ML) and Deep Learning (DL) models is becoming popular among researchers in the era of Big Data (BD). Nevertheless, owing to the lack of extensive analysis, most of the developed ML and DL models failed to achieve better classification results. Thus, for the real-time prediction of the polarity of the stock price, a Probability Tanh-Independently Recurrent Neural Network (PT-IndRNN)-based classification of the sentiment of the financial text data of Twitter is proposed to solve this problem. Primarily, by employing the corresponding API, the real-time financial data and Twitter data are extracted and stored in the MongoDB database using Apache Flume. This stored data with the historical big datasets are taken and pre-processed. Next, by deploying the proposed Hadoop Distributed File System (HDFS) clustering, the pre-processed stock market data and Twitter data in real-time, as well as the historical dataset, are combined separately. After that, the features are extracted from the clustered sentences. Then, by utilizing the Senti Word Net, the sentences chosen using Linear Scaling-Dwarf Mongoose Optimization Algorithm (LS-DMOA) are converted to negative and positive scores. In the end, the sentiment of the financial texts is classified by the PTh-Ind RNN, which is proved by obtaining reliable result values.

Tinba: Incremental partitioning for efficient trajectory analytics

Applications with mobile and sensing devices have already become ubiquitous. In most of these applications, trajectory data is continuously growing to huge volumes. Existing systems for big trajectory data organize trajectories at distributed block storage systems. Systems like DITA that use block storage (e.g., 128 MB each) are more efficient for analytical queries, but they cannot incrementally maintain the partitioned data and do not support delete operations, resulting in difficulties in trajectory analytics. In this paper, we propose an incremental trajectory partitioning framework Tinba that enables distributed block storage systems to efficiently maintain optimized partitions under incremental updates of trajectories. We employ a data flushing technique to bulk ingest trajectory data for random writing in distributed file system (DFS). We recast the incremental partitioning problem as an optimal partitioning problem and prove its NP-hardness. A cost–benefit model is proposed to address the optimal partitioning problem. Moreover, Tinba supports most of the existing similarity measures to quantify the similarity between trajectories. A heuristic is developed to instantiate the Tinba framework. Comprehensive experiments on real-world and synthetic datasets demonstrate the advancements in ingestion performance and partition quality, as opposed to other trajectory partition methods.

Application of MPP database and artificial intelligence system in online evaluation of college students' mental health

Based on MPP database, we have conducted research and investigation on big data processing and analysis, and provided a good solution for big data. However, due to the large data storage requirements in the local file system may not be fully met. Therefore, the storage and management of big data, reliability, availability and scalability are more and more adopted by distributed file system. Therefore, MPP database based on distributed file system has become a research hotspot. In this paper, in the network control, through the control algorithm based on two kinds of artificial intelligence system, through the fuzzy PID controller to reduce the impact of system control performance. The dynamic BP network error correction and predictive PID controller are introduced into the system to reduce the influence. College Students' mental health education is an important part of the current training. Rapid and accurate online evaluation and analysis of College Students' mental health can provide reference for college students and effective help for the follow-up activities and consultation of the school, At the same time, it provides some basis for the balance of psychological scale and the revision of item setting. In this paper, MPP database and artificial intelligence system are introduced into the application of mental health assessment, which can effectively assess the mental health of college students, and thus promote the continuous development of assessment application.

KVRangeDB: Range Queries for a Hash-based Key–Value Device

Key–value (KV) software has proven useful to a wide variety of applications including analytics, time-series databases, and distributed file systems. To satisfy the requirements of diverse workloads, KV stores have been carefully tailored to best match the performance characteristics of underlying solid-state block devices. Emerging KV storage device is a promising technology for both simplifying the KV software stack and improving the performance of persistent storage-based applications. However, while providing fast, predictable put and get operations, existing KV storage devices do not natively support range queries that are critical to all three types of applications described above. In this article, we present KVRangeDB, a software layer that enables processing range queries for existing hash-based KV solid-state disks (KVSSDs). As an effort to adapt to the performance characteristics of emerging KVSSDs, KVRangeDB implements log-structured merge tree key index that reduces compaction I/O, merges keys when possible, and provides separate caches for indexes and values. We evaluated the KVRangeDB under a set of representative workloads, and compared its performance with two existing database solutions: a Rocksdb variant ported to work with the KVSSD, and Wisckey, a key–value database that is carefully tuned for conventional block devices. On filesystem aging workloads, KVRangeDB outperforms Wisckey by 23.7× in terms of throughput and reduce CPU usage and external write amplifications by 14.3× and 9.8×, respectively.

Understand the working of Sqoop and hive in Hadoop

In past decades, the structured and consistent data analysis has seen huge success. It is a challenging task to analyse the multimedia data which is in unstructured format. Here the big data defines the huge volume of data that can be processed in distributed format. The big data can be analysed by using the hadoop tool which contains the Hadoop Distributed File System (HDFS) storage space and inbuilt several components are there. Hadoop manages the distributed data which is placed in the form of cluster analysis of data itself. In this, it shows the working of Sqoop and Hive in hadoop. Sqoop (SQL-to-Hadoop) is one of the Hadoop component that is designed to efficiently imports the huge data from traditional database to HDFS and vice versa. Hive is an open source software for managing large data files that is stored in HDFS. To show the working, here we are taking the application Instagram which is a most popular social media. In this analyze the data that is generated from Instagram that can be mined and utilized by using Sqoop and Hive. By this, prove that sqoop and hive can give results efficiently. This paper gives the details of sqoop and hive working in hadoop.

Disaggregating RocksDB: A Production Experience

As in the general industry, there is a trend in Meta's data centers to migrate data from locally attached SSDs to cloud storage. We extended RocksDB [26], a widely used open-source storage engine designed and built for local SSDs, to leverage disaggregated storage. RocksDB's design, such as its data and log files' access patterns, makes an append-only distributed file system a desirable underlying storage. At Meta, we built disaggregated RocksDB using Tectonic File System [35], which so far had mainly been used for our data warehouse and blob storage stacks. We identified that metadata overhead and tail latencies were Tectonic's major performance gaps and addressed them accordingly. We improved the reliability, performance and other requirements with both general and customized optimizations to the core engine in RocksDB. We also took the time to deeply understand the common challenges presented by applications running on RocksDB and implemented enhancements to address them. This architecture enabled RocksDB to adapt to a more distributed architecture for performance enhancements.

An Optimal Algorithm for Partial Order Multiway Search

Partial order multiway search (POMS) is an important problem that finds use in crowdsourcing, distributed file systems, software testing, etc. In this problem, a game is played between an algorithm A and an oracle, based on a directed acyclic graph G known to both parties. First, the oracle picks a vertex t in G called the target; then, A aims to figure out which vertex is t by probing reachability. In each probe, A selects a set Q of vertices in G whose size is bounded by a pre-agreed value k, and the oracle then reveals, for each vertex q 2 Q, whether q can reach the target in G. The objective of A is to minimize the number of probes. This article presents an algorithm to solve POMS in O(log1+k n + d k log1+d n) probes, where n is the number of vertices in G, and d is the largest out-degree of the vertices in G. The probing complexity is asymptotically optimal.

Guest Editorial: Blockchain and decentralised solutions for social good

Guest Editorial: Blockchain and decentralised solutions for social good

Small Files Problem Resolution via Hierarchical Clustering Algorithm.

The Small Files Problem in Hadoop Distributed File System (HDFS) is an ongoing challenge that has not yet been solved. However, various approaches have been developed to tackle the obstacles this problem creates. Properly managing the size of blocks in a file system is essential as it saves memory and computing time and may reduce bottlenecks. In this article, a new approach using a Hierarchical Clustering Algorithm is suggested for dealing with small files. The proposed method identifies the files by their structure and via a special Dendrogram analysis, and then recommends which files can be merged. As a simulation, the proposed algorithm was applied via 100 CSV files with different structures, containing 2-4 columns with different data types (integer, decimal and text). Also, 20 files that were not CSV files were created to demonstrate that the algorithm only works on CSV files. All data were analyzed via a machine learning hierarchical clustering method, and a Dendrogram was created. According to the merge process that was performed, seven files from the Dendrogram analysis were chosen as appropriate files to be merged. This reduced the memory space in the HDFS. Furthermore, the results showed that using the suggested algorithm led to efficient file management.

Efficient Management and Scheduling of Massive Remote Sensing Image Datasets

The rapid development of remote sensing image sensor technology has led to exponential increases in available image data. The real-time scheduling of gigabyte-level images and the storage and management of massive image datasets are incredibly challenging for current hardware, networking and storage systems. This paper’s three novel strategies (ring caching, multi-threading and tile-prefetching mechanisms) are designed to comprehensively optimize the remote sensing image scheduling process from image retrieval, transmission and visualization perspectives. A novel remote sensing image management and scheduling system (RSIMSS) is designed using these three strategies as its core algorithm, the PostgreSQL database and HDFS distributed file system as its underlying storage system, and the multilayer Hilbert spatial index and image tile pyramid to organize massive remote sensing image datasets. Test results show that the RSIMSS provides efficient and stable image storage performance and allows real-time image scheduling and view roaming.

Video File Allocation for Wear-Leveling in Distributed Storage Systems With Heterogeneous Solid-State-Disks (SSDs)

With the advent of new large-capacity solid-state disks (SSDs) such as quad-level-cells (QLC), SSD arrays can be effectively used in video storage systems that require large-capacity storage space. Typically, SSD manufacturers specify a drive-writes-per-day (DWPD) metric, which is the ratio of bytes written per day to the total capacity in bytes, to ensure an SSD’s specified lifetime; it is important to limit the number of write operations by considering the DWPD for each SSD. We propose a new video file allocation technique to effectively manage the heterogeneous DWPD characteristics of SSDs in distributed storage systems. To express the degree of wear-leveling for heterogeneous SSDs, we first introduce the concept of ADWD, which is the actual number of bytes written per day compared to DWPD. We then propose two algorithms for file placement and migration. The file placement algorithm places files greedily based on the bandwidth-to-space ratio (BSR) of each file and SSD to balance the bandwidth usage and storage of the SSD. The file migration algorithm moves files from overloaded to underloaded SSDs to meet bandwidth limit requirements while minimizing the overall ADWD as a result of migration, and then migrates additional popular files to improve SSD bandwidth utilization. To use these algorithms in actual distributed file systems, we implemented a suite of tools for file placement and migration in the Hadoop distributed file system (HDFS). Experimental results show that the proposed algorithm reduces the mean of ADWD by 35.44% and its standard deviation by 69.78% compared to the benchmark methods on average.

LARGE VECTOR SPATIAL DATA STORAGE AND QUERY PROCESSING USING CLICKHOUSE

Abstract. The exponential growth of geospatial data resulting from the development of earth observation technology has created significant challenges for traditional relational databases. While NoSQL databases based on distributed file systems can handle massive data storage, they often struggle to cope with real-time query. Column-storage databases, on other hand, are highly effective at both storage and query processing for large-scale datasets. In this paper, we propose a spatial version of ClickHouse that leverages R-Tree indexing to enable efficient storage and real-time analysis of massive remote sensing data. ClickHouse is a column-oriented, open-source database management system designed for handling large-scale datasets. By integrating R-Tree indexing, we have created a highly efficient system for storing and querying geospatial data. To evaluate the performance of our system, we compare it with HBase, a popular distributed, NoSQL database system. Our experimental results show that ClickHouse outperforms HBase in handling spatial data queries, with a response time approximately three times faster than HBase. We attribute this performance gain to the highly efficient R-Tree indexing used in ClickHouse, which allows for fast spatial data query.

PbDinEHR: A Novel Privacy by Design Developed Framework Using Distributed Data Storage and Sharing for Secure and Scalable Electronic Health Records Management

Privacy in Electronic Health Records (EHR) has become a significant concern in today’s rapidly changing world, particularly for personal and sensitive user data. The sheer volume and sensitive nature of patient records require healthcare providers to exercise an intense quantity of caution during EHR implementation. In recent years, various healthcare providers have been hit by ransomware and distributed denial of service attacks, halting many emergency services during COVID-19. Personal data breaches are becoming more common day by day, and privacy concerns are often raised when sharing data across a network, mainly due to transparency and security issues. To tackle this problem, various researchers have proposed privacy-preserving solutions for EHR. However, most solutions do not extensively use Privacy by Design (PbD) mechanisms, distributed data storage and sharing when designing their frameworks, which is the emphasis of this study. To design a framework for Privacy by Design in Electronic Health Records (PbDinEHR) that can preserve the privacy of patients during data collection, storage, access and sharing, we have analysed the fundamental principles of privacy by design and privacy design strategies, and the compatibility of our proposed healthcare principles with Privacy Impact Assessment (PIA), Australian Privacy Principles (APPs) and General Data Protection Regulation (GDPR). To demonstrate the proposed framework, ‘PbDinEHR’, we have implemented a Patient Record Management System (PRMS) to create interfaces for patients and healthcare providers. In addition, to provide transparency and security for sharing patients’ medical files with various healthcare providers, we have implemented a distributed file system and two permission blockchain networks using the InterPlanetary File System (IPFS) and Ethereum blockchain. This allows us to expand the proposed privacy by design mechanisms in the future to enable healthcare providers, patients, imaging labs and others to share patient-centric data in a transparent manner. The developed framework has been tested and evaluated to ensure user performance, effectiveness, and security. The complete solution is expected to provide progressive resistance in the face of continuous data breaches in the patient information domain.

Analysis of Distributed File System Replication Using the NDLC Method with Hyper-V Virtual Simulation Machine

The need to access file sharing easily on an organization's computer network was increased. Users didn't have to worry about the number of file server addresses that can be accessed and are made with only one access address to use file sharing. The availability of data on the network to file storage availability in an organization was also essential. Data would be permanently lost, following a reason including hardware failure, or even accidentally deleted. It was important to ensure that there was a copy of the data. Achieving good data availability requires a system strategy built in the organization's data center. This research used Distributed File System Replication (DFSR) based on active directory domain services with Windows Server. The research method used NDLC (Network Diagram Life Cycle) method. This research was conducted through analysis with the Hyper-V virtual simulation machine. The results of the research with this simulation are that the Distributed File System (DFS) makes it easy for users to access file shares on several file server nodes using only one URL address. DFSR makes it easy for users to clone files automatically on multiple nodes file servers at other locations. DFSR, with its Share and Publish features, provide good data availability. If one of the file server nodes experiences an interruption, the file server nodes at another location would be taken over to provide the data. This system makes it easy for administrators to manage file servers

Small files access efficiency in hadoop distributed file system a case study performed on British library text files

Small files access efficiency in hadoop distributed file system a case study performed on British library text files

Cache-Based Matrix Technology for Efficient Write and Recovery in Erasure Coding Distributed File Systems

With the development of various information and communication technologies, the amount of big data has increased, and distributed file systems have emerged to store them stably. The replication technique divides the original data into blocks and writes them on multiple servers for redundancy and fault tolerance. However, there is a symmetrical space efficiency problem that arises from the need to store blocks larger than the original data. When storing data, the Erasure Coding (EC) technique generates parity blocks through encoding calculations and writes them separately on each server for fault tolerance and data recovery purposes. Even if a specific server fails, original data can still be recovered through decoding calculations using the parity blocks stored on the remaining servers. However, matrices generated during encoding and decoding are redundantly generated during data writing and recovery, which leads to unnecessary overhead in distributed file systems. This paper proposes a cache-based matrix technique that uploads the matrices generated during encoding and decoding to cache memory and reuses them, rather than generating new matrices each time encoding or decoding occurs. The design of the cache memory applies the Weighting Size and Cost Replacement Policy (WSCRP) algorithm to efficiently upload and reuse matrices to cache memory using parameters known as weights and costs. Furthermore, the cache memory table can be managed efficiently because the weight–cost model sorts and updates matrices using specific parameters, which reduces replacement cost. The experiment utilized the Hadoop Distributed File System (HDFS) as the distributed file system, and the EC volume was composed of Reed–Solomon code with parameters (6, 3). As a result of the experiment, it was possible to reduce the write, read, and recovery times associated with encoding and decoding. In particular, for up to three node failures, systems using WSCRP were able to reduce recovery time by about 30 s compared to regular HDFS systems.

Stacked multi‐layer security for Hadoop distributed file system using HSCT steganography

SummaryBig data needs to have scalable storage platforms as well as parallel processing environments. Hadoop is preferable because it is an open‐source software which works in a distributed, parallel processing environment and no need of any special hardware. In recent developments of Hadoop, security was introduced as optional. A symmetric block cipher advanced encryption standard (AES) is used for encryption. A 128 MB block of data needs 8 million encryptions. A better way of hiding the data from unauthorized access is preferable. Steganography, a more contemporary security technology than cryptography, is the art and science of hiding subconscious information in hidden item while exposing opponents their existence. The cover is an image, and the secret information may be a photo, music, video, or text. A double layer hexcode substituted–color space transformed (HSCT) steganography with 3 band splitting of data ensures confidentiality of the data and one time pad key generated with client's ID provides authentication also. Considering the volume of data, steganography concept is much faster than AES or any other cipher. Hiding the data inside the random images will indulge in fast reconstruction of data when needed for processing using either MapReduce or tools like spark.

Tiny datablock in saving Hadoop distributed file system wasted memory

<p>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 datablock-HDFS (TD-HDFS) to increase the usability of HDFS memory and increase the file hosting capabilities by reducing the datablock size to the minimum capacity, and then merging all the related datablocks into one master datablock. This master datablock consists of tiny virtual datablocks that contain the related small files together; will exploit the full memory of the master datablock. The result of this study is a running HDFS with a minimum amount of wasted memory with the same read/write data performance. 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How does solid‐state drives cluster perform for distributed file systems: An empirical study

SummaryAs the capacity of Solid‐State Drives (SSDs) is constantly being optimised and boosted with gradually reduced cost, the SSD cluster is now widely deployed as part of the hybrid storage system in various scenarios such as cloud computing and big data processing. However, despite its rapid developments, the performance of the SSD cluster remains largely under‐investigated, leaving its sub‐optimal applications in reality. To address this issue, in this paper we conduct extensive empirical studies for a comprehensive understanding of the SSD cluster in diverse settings. To this end, we configure a real SSD cluster and gather the generated trace data based on some often‐used benchmarks, then adopt analytical methods to analyse the performance of the SSD cluster with different configurations. In particular, regression models are built to provide better performance predictability under broader configurations, and the correlations between influential factors and performance metrics with respect to different numbers of nodes are investigated, which reveal the high scalability of the SSD cluster. Additionally, the cluster's network bandwidth is inspected to explain the performance bottleneck. Finally, the knowledge gained is summarised to benefit the SSD cluster deployment in practice.