Enabling Data Sharding through Blockchain Technology

Sagar Korekar1, Ankit Nadgire2, Mahesh Bhosale3, Sandip Chougule4

1Department of Information Technology, VPKBIET, Baramati.

2Department of Information Technology, VPKBIET, Baramati.

3Department of Information Technology, VPKBIET, Baramati.

4Department of Information Technology, VPKBIET, Baramati.

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Abstract - The sharding scheme is highly useful and an effective mechanism to improve the storage and improve the volume of the database. Majority of the data being stored across the world is stored in conventional techniques in servers and workstations. As the amount of data keeps increasing every single day, these systems need to be able to scale up and allow for more data storage within the existing infrastructure. This is a big challenge as the conventional approaches are not as effective in achieving the scaling of the storage infrastructure with certain limitations that are inherent towards a centralized implementation of the databases. Therefore, the utilization of the decentralized sharding mechanism can help in achieving effective scalability and improved volume of the storage. The conventional approaches for the purpose of achieving the scaling of the existing database architecture are not sufficient for the current influx of data. Data nowadays has increased in value and volume that demands for an improvement in the infrastructure to be able to store more data efficiently and with improved security. The sharding strategy is one of the leading strategies that have been effective in the overcoming the scalability problem that plagues the traditional databases. Therefore, to provide a solution for this a data sharding approach has been defined in this research article that utilizes Linear Clustering and sharding through the Blockchain Framework along with Bilinear Pairing for Data Integrity report generation. The approach has been evaluated effectively through experimentation to achieve satisfactory results.

Key Words:  Blockchain, Linear Clustering, Bilinear Pairing, Data Sharding, etc.