TY - JOUR
T1 - Doppler
T2 - 48th International Conference on Very Large Data Bases, VLDB 2022
AU - Cahoon Jcahoon@Microsoft.Com, Joyce
AU - Liu, Sean
AU - Wang Wenjwang@Microsoft.Com, Wenjing
AU - Truong, Raymond
AU - Ciortea, Alexandra
AU - Narasimhan, Sreraman
AU - Zhu, Yiwen
AU - Singh, Neetu
AU - Lin, Katherine
AU - Wan, Chengcheng
AU - Krishnan, Subru
N1 - Publisher Copyright:
© 2022, VLDB Endowment. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85137990092
U2 - 10.14778/3554821.3554840
DO - 10.14778/3554821.3554840
M3 - 会议文章
AN - SCOPUS:85137990092
SN - 2150-8097
VL - 15
SP - 3509
EP - 3521
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
IS - 12
Y2 - 5 September 2022 through 9 September 2022
ER -