Doppler

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Proceedings of the VLDB Endowment

سال: 2022

ISSN: ['2150-8097']

DOI: https://doi.org/10.14778/3554821.3554840