Open Access

ARTICLE

A Middleware for Polyglot Persistence and Data Portability of Big Data PaaS Cloud Applications

Kiranbir Kaur^{1, *}, Sandeep Sharma¹, Karanjeet Singh Kahlon²

1 Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, 143001, India.
2 Department of Computer Science and Applications, Guru Nanak Dev University, Amritsar, 143001, India.

* Corresponding Author: Kiranbir Kaur. Email: .

Computers, Materials & Continua 2020, 65(2), 1625-1647. https://doi.org/10.32604/cmc.2020.011535

Received 14 May 2020; Accepted 11 June 2020; Issue published 20 August 2020

Abstract

Vendor lock-in can occur at any layer of the cloud stack-Infrastructure, Platform, and Software-as-a-service. This paper covers the vendor lock-in issue at Platform as a Service (PaaS) level where applications can be created, deployed, and managed without worrying about the underlying infrastructure. These applications and their persisted data on one PaaS provider are not easy to port to another provider. To overcome this issue, we propose a middleware to abstract and make the database services as cloud-agnostic. The middleware supports several SQL and NoSQL data stores that can be hosted and ported among disparate PaaS providers. It facilitates the developers with data portability and data migration among relational and NoSQL-based cloud databases. NoSQL databases are fundamental to endure Big Data applications as they support the handling of an enormous volume of highly variable data while assuring fault tolerance, availability, and scalability. The implementation of the middleware depicts that using it alleviates the efforts of rewriting the application code while changing the backend database system. A working protocol of a migration tool has been developed using this middleware to facilitate the migration of the database (move existing data from a database on one cloud to a new database even on a different cloud). Although the middleware adds some overhead compared to the native code for the cloud services being used, the experimental evaluation on Twitter (a Big Data application) data set, proves this overhead is negligible.

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Citations