research-article

Energy-efficient polyglot persistence database live migration among heterogeneous clouds

Authors:

Karan Aggarwal,

Sandeep SharmaAuthors Info & Claims

The Journal of Supercomputing, Volume 79, Issue 1

Pages 265 - 294

https://doi.org/10.1007/s11227-022-04662-6

Published: 07 July 2022 Publication History

Abstract

Cloud computing is seen as a more promising technology than any other traditional information technology computing paradigm in today’s world. It essentially functions as an on-demand resource provisioning platform that requires no active user participation. The resource provisioning strategies necessitate proper load distribution management across the cloud network, without which the cloud would experience biased workload performance. Today virtualization is the cornerstone of cloud computing, allowing data dissemination and administration via deploying virtual machines. Modern applications contain data that need to be stored into a scheme called polyglot persistence (combining SQL and NoSQL data-stores). However, these services are tailored to specific storage requirements, necessitating aggregating them from several heterogeneous clouds or migrating data from one cloud to another. Data migration can be done offline where the database is independent of the application, or otherwise, the application has to be down for the migration period. This paper developed a middleware in .NET Core facilitating the live migration of persistent polyglot data in heterogeneous clouds. This paper presents the proof of concept for live migration of the database layer of an application hosted on any supported clouds to any implemented cloud’s data-store. Our suggested technique performs better in migration time, energy usage, and throughput aspects as compared with the offline migration scenario. In our experimentation, we found that while migrating data in offline mode from SQL to mongo and vice versa there is a marginal increase of 29% and 11%, respectively, in latency time. This increase is acceptable and tolerable while considering the live data migration scenario.

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Cited By

Rostami SBroumandnia AKhademzadeh A(2024)An energy-efficient task scheduling method for heterogeneous cloud computing systems using capuchin search and inverted ant colony optimization algorithmThe Journal of Supercomputing10.1007/s11227-023-05725-y80:6(7812-7848)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s11227-023-05725-y
Sapate SPathak GTembhurne O(2023)Performance Evaluation of Data Migration Tools: A Comparative Study Based on Data Type, Size and Network BandwidthProceedings of the 5th International Conference on Information Management & Machine Intelligence10.1145/3647444.3647917(1-6)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1145/3647444.3647917

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Information & Contributors

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Published In

cover image The Journal of Supercomputing

The Journal of Supercomputing Volume 79, Issue 1

Jan 2023

1160 pages

ISSN:0920-8542

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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 07 July 2022

Accepted: 17 June 2022

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Cited By

Rostami SBroumandnia AKhademzadeh A(2024)An energy-efficient task scheduling method for heterogeneous cloud computing systems using capuchin search and inverted ant colony optimization algorithmThe Journal of Supercomputing10.1007/s11227-023-05725-y80:6(7812-7848)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s11227-023-05725-y
Sapate SPathak GTembhurne O(2023)Performance Evaluation of Data Migration Tools: A Comparative Study Based on Data Type, Size and Network BandwidthProceedings of the 5th International Conference on Information Management & Machine Intelligence10.1145/3647444.3647917(1-6)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1145/3647444.3647917

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