A Blockchain-Based Distributed Storage Network to Manage Growing Data Storage Needs

2021 ◽  
Author(s):  
Vijay A. Kanade
Keyword(s):  
Data Storage ◽  
Distributed Storage ◽  
Storage Network

scholarly journals An Architecture for Distributed Electronic Documents Storage in Decentralized Blockchain B2B Applications

Computers ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 142
Author(s):  
Obadah Hammoud ◽  
Ivan Tarkhanov ◽  
Artyom Kosmarski
Keyword(s):  
Distributed Systems ◽  
Data Storage ◽  
Distributed Storage ◽  
Distributed Data ◽  
Erasure Coding ◽  
Distributed Data Storage ◽  
Electronic Documents ◽  
File Storage ◽  
Load Balancer ◽  
The Cost

This paper investigates the problem of distributed storage of electronic documents (both metadata and files) in decentralized blockchain-based b2b systems (DApps). The need to reduce the cost of implementing such systems and the insufficient elaboration of the issue of storing big data in DLT are considered. An approach for building such systems is proposed, which allows optimizing the size of the required storage (by using Erasure coding) and simultaneously providing secure data storage in geographically distributed systems of a company, or within a consortium of companies. The novelty of this solution is that we are the first who combine enterprise DLT with distributed file storage, in which the availability of files is controlled. The results of our experiment demonstrate that the speed of the described DApp is comparable to known b2c torrent projects, and subsequently justify the choice of Hyperledger Fabric and Ethereum Enterprise for its use. Obtained test results show that public blockchain networks are not suitable for creating such a b2b system. The proposed system solves the main challenges of distributed data storage by grouping data into clusters and managing them with a load balancer, while preventing data tempering using a blockchain network. The considered DApps storage methodology easily scales horizontally in terms of distributed file storage and can be deployed on cloud computing technologies, while minimizing the required storage space. We compare this approach with known methods of file storage in distributed systems, including central storage, torrents, IPFS, and Storj. The reliability of this approach is calculated and the result is compared to traditional solutions based on full backup.

Monitoring of a Grid Storage Virtualization Service

2013 ◽  
Vol 5 (1) ◽  
pp. 53-69
Author(s):  
Jacques Jorda ◽  
Aurélien Ortiz ◽  
Abdelaziz M’zoughi ◽  
Salam Traboulsi
Keyword(s):  
Monitoring System ◽  
Data Storage ◽  
Large Scale ◽  
Distributed Storage ◽  
Storage System ◽  
Data Access ◽  
Data Placement ◽  
Workload Prediction ◽  
Storage Virtualization

Grid computing is commonly used for large scale application requiring huge computation capabilities. In such distributed architectures, the data storage on the distributed storage resources must be handled by a dedicated storage system to ensure the required quality of service. In order to simplify the data placement on nodes and to increase the performance of applications, a storage virtualization layer can be used. This layer can be a single parallel filesystem (like GPFS) or a more complex middleware. The latter is preferred as it allows the data placement on the nodes to be tuned to increase both the reliability and the performance of data access. Thus, in such a middleware, a dedicated monitoring system must be used to ensure optimal performance. In this paper, the authors briefly introduce the Visage middleware – a middleware for storage virtualization. They present the most broadly used grid monitoring systems, and explain why they are not adequate for virtualized storage monitoring. The authors then present the architecture of their monitoring system dedicated to storage virtualization. We introduce the workload prediction model used to define the best node for data placement, and show on a simple experiment its accuracy.

A Review: Map Reduce Framework for Cloud Computing

2018 ◽  
Vol 7 (4.6) ◽  
pp. 13
Author(s):  
Mekala Sandhya ◽  
Ashish Ladda ◽  
Dr. Uma N Dulhare ◽  
. . ◽  
. .
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Distributed Computing ◽  
Data Storage ◽  
High Performance ◽  
Large Scale ◽  
Distributed Storage ◽  
Large Data ◽  
Mass Data ◽  
Internet Information

In this generation of Internet, information and data are growing continuously. Even though various Internet services and applications. The amount of information is increasing rapidly. Hundred billions even trillions of web indexes exist. Such large data brings people a mass of information and more difficulty discovering useful knowledge in these huge amounts of data at the same time. Cloud computing can provide infrastructure for large data. Cloud computing has two significant characteristics of distributed computing i.e. scalability, high availability. The scalability can seamlessly extend to large-scale clusters. Availability says that cloud computing can bear node errors. Node failures will not affect the program to run correctly. Cloud computing with data mining does significant data processing through high-performance machine. Mass data storage and distributed computing provide a new method for mass data mining and become an effective solution to the distributed storage and efficient computing in data mining. 

Optimal Elliptic Curve Cryptography-Based Effective Approach for Secure Data Storage in Clouds

2020 ◽  
Vol 11 (4) ◽  
pp. 65-81
Author(s):  
Anju Malik ◽  
Mayank Aggarwal ◽  
Bharti Sharma ◽  
Akansha Singh ◽  
Krishna Kant Singh
Keyword(s):  
Elliptic Curve ◽  
Data Storage ◽  
Elliptic Curve Cryptography ◽  
Search Algorithm ◽  
Distributed Storage ◽  
Data Retrieval ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensitive Data ◽  
Cloud Server

With the rapid development of cloud advancement, a data security challenge has emerged. In this paper, a technique based on elliptical cryptography and cuckoo search algorithm is proposed. With this technique, data owners securely store their data files in the cloud server. Initially the user sends a file storage request to store a file in a cloud server provider (CSP). The input file is checked whether it is sensitive or non-sensitive by the user. If the file is sensitive, then it would be split and stored in different virtual machines (VMs), and if the file is non-sensitive, then it would be assigned in a single VM. This approach was used for the first time as per the survey. To add further security, the sensitive data retrieval needs an encryption process that is supported by the proposed algorithm. If the data owner stores the sensitive data to cloud server, the data owner's document is encrypted by the double encryption technique. Here RSA and optimal elliptic curve cryptography (OECC) algorithm is used to encrypt the document with high security. The authors have used cuckoo search algorithm to identify the optimal key in ECC. This paper has proposed a novel cryptography approach for delivering mass distributed storage by which user's original data cannot be directly reached by cloud operators. Hence, this research has proved that the proposed work will give better securable data storage solving the security issues.

Research on Distributed Storage Technology Based on Mass Data

2014 ◽  
Vol 687-691 ◽  
pp. 2710-2713
Author(s):  
Jing Yang
Keyword(s):  
Data Storage ◽  
Distributed Storage ◽  
Rapid Development ◽  
Distributed Data ◽  
Distributed Environment ◽  
Environment Analysis ◽  
Distributed Data Storage ◽  
Storage Technology ◽  
Mass Data ◽  
Storage Performance

With the rapid development of computer technology and network technology, mass data store distributed and management pattern already received accepted extensively. Thus it can be seen malpractice obviously, data storage structure, storage environment are different and other problems such as data handing. The paper go into how improve data storage performance in the distributed environment, analysis the data storage technology at present and data storage performance in the distributed environment, summarize the claim of distributed storage database design, provide the theory in vacation distributed data storage performance standardization.

scholarly journals Distributed Storage Strategy and Visual Analysis for Economic Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiangli Chang ◽  
Hailang Cui
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Visual Analysis ◽  
Distributed Storage ◽  
Storage System ◽  
Service Level Agreement ◽  
Wiener Filter ◽  
Service Level ◽  
Prototype System ◽  
Data Types

With the increasing popularity of a large number of Internet-based services and a large number of services hosted on cloud platforms, a more powerful back-end storage system is needed to support these services. At present, it is very difficult or impossible to implement a distributed storage to meet all the above assumptions. Therefore, the focus of research is to limit different characteristics to design different distributed storage solutions to meet different usage scenarios. Economic big data should have the basic requirements of high storage efficiency and fast retrieval speed. The large number of small files and the diversity of file types make the storage and retrieval of economic big data face severe challenges. This paper is oriented to the application requirements of cross-modal analysis of economic big data. According to the source and characteristics of economic big data, the data types are analyzed and the database storage architecture and data storage structure of economic big data are designed. Taking into account the spatial, temporal, and semantic characteristics of economic big data, this paper proposes a unified coding method based on the spatiotemporal data multilevel division strategy combined with Geohash and Hilbert and spatiotemporal semantic constraints. A prototype system was constructed based on Mongo DB, and the performance of the multilevel partition algorithm proposed in this paper was verified by the prototype system based on the realization of data storage management functions. The Wiener distributed memory based on the principle of Wiener filter is used to store the workload of each workload distributed storage window in a distributed manner. For distributed storage workloads, this article adopts specific types of workloads. According to its periodicity, the workload is divided into distributed storage windows of specific duration. At the beginning of each distributed storage window, distributed storage is distributed to the next distributed storage window. Experiments and tests have verified the distributed storage strategy proposed in this article, which proves that the Wiener distributed storage solution can save platform resources and configuration costs while ensuring Service Level Agreement (SLA).

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