Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud

  • Joyce Cahoon Jcahoon@Microsoft.Com
  • , Sean Liu
  • , Wenjing Wang Wenjwang@Microsoft.Com
  • , Raymond Truong
  • , Alexandra Ciortea
  • , Sreraman Narasimhan
  • , Yiwen Zhu
  • , Neetu Singh
  • , Katherine Lin
  • , Chengcheng Wan
  • , Subru Krishnan

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations

Abstract

Selecting the optimal cloud target to migrate SQL estates from on-premises to the cloud remains a challenge. Current solutions are not only time-consuming and error-prone, requiring significant user input, but also fail to provide appropriate recommendations. We present Doppler, a scalable recommendation engine that provides right-sized Azure SQL Platform-as-a-Service (PaaS) recommendations without requiring access to sensitive customer data and queries. Doppler introduces a novel price-performance methodology that allows customers to get a personalized rank of relevant cloud targets solely based on low-level resource statistics, such as latency and memory usage. Doppler supplements this rank with internal knowledge of Azure customer behavior to help guide new migration customers towards one optimal target. Experimental results over a 9-month period from prospective and existing customers indicate that Doppler can identify optimal targets and adapt to changes in customer workloads. It has also found cost-saving opportunities among over-provisioned cloud customers, without compromising on capacity or other requirements. Doppler has been integrated and released in the Azure Data Migration Assistant v5.5, which receives hundreds of assessment requests daily.

Original languageEnglish
Pages (from-to)3509-3521
Number of pages13
JournalProceedings of the VLDB Endowment
Volume15
Issue number12
DOIs
StatePublished - 2022
Externally publishedYes
Event48th International Conference on Very Large Data Bases, VLDB 2022 - Sydney, Australia
Duration: 5 Sep 20229 Sep 2022

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