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

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Abstract

Selecting the optimal cloud target to migrate SQL estates from an on-premise data platform to the cloud remains a challenge. Current solutions are not only time-consuming and error prone, requiring significant input from database administrators or field engineers, but also fail to provide clear recommendations. We present Doppler, a scalable recommendation engine that provides right-sized Azure SQL PaaS recommendations without requiring access to sensitive customer data and queries. Dopper provides a consistent framework for mapping on-premise workloads to appropriate PaaS cloud targets by introducing a novel price-performance methodology that allows customers to get a personalized rank of relevant cloud targets. This rank is obtained solely from the input of low-level resource statistics. Doppler also supplements this rank with internal knowledge of Azure customer behavior to help guide new migration customers towards one optimal cloud 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 migration assessment requests daily.