Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)

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Azure-SQL AutoSizer: Privacy-Aware Performance-Cost SKU Mapping for SQL Migrations

Authors
Harika Naidu Beesabathuni1, *
1Project Manager, MSR Technology Group LLC, Chatham, United States of America
*Corresponding author. Email: harikanaidu.b@gmail.com
Corresponding Author
Harika Naidu Beesabathuni
Available Online 16 June 2026.
DOI
10.2991/978-94-6239-693-7_101How to use a DOI?
Keywords
Azure SQL migration; SKU recommendation; privacy-aware performance modelling; cloud resource optimization
Abstract

This paper describes the design of Azure-SQL AutoSizer, a SKU recommendation engine for automatically selecting Azure SQL PaaS targets (i.e. Azure SQL Database and Azure SQL Managed In- stance) to which on-premises SQL workloads can be migrated. Unlike existing tools that require intrusive access to customer data or queries, AutoSizer works only on low-level performance counters such as CPU, memory, IOPS and latency and is GDPR compliant. We employ a price- performance throttling model to create personalised SKU ranking and integrate Azure customer telemetry that profiles workload negotiability on various resource dimensions. Since October 2021, AutoSizer has been implemented in Azure Data Migration Assistant (DMA) and has shown high accuracy – with 89.4% of SQL DB and 96.7% of SQL MI expert-defined SKUs – while flagging significant cost-saving opportuni- ties for over-provisioned Azure SQL customers. The system can assist with hundreds of migration assessments a day, maintaining transparency and performance adaptability.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
16 June 2026
ISBN
978-94-6239-693-7
ISSN
2589-4919
DOI
10.2991/978-94-6239-693-7_101How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Harika Naidu Beesabathuni
PY  - 2026
DA  - 2026/06/16
TI  - Azure-SQL AutoSizer: Privacy-Aware Performance-Cost SKU Mapping for SQL Migrations
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 1043
EP  - 1055
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_101
DO  - 10.2991/978-94-6239-693-7_101
ID  - Beesabathuni2026
ER  -