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.
翻译:选择将 SQL 地产从地盘迁移到云层的最佳云度目标仍然是一项挑战。当前的解决方案不仅耗时和容易出错,需要大量用户投入,而且未能提供适当的建议。我们介绍了可缩放的建议引擎Doppler,这是一个提供适当规模的 Azure SQL 平台-as-Service (PaaS) 建议的可缩放型建议引擎,无需查阅敏感的客户数据和查询。Doppler 引入了一种新的价格绩效方法,使客户能够仅根据诸如延时和记忆使用等低水平资源统计数据获得相关云标的个性化级。Doppler补充了Azure客户行为的内部知识,帮助新移民客户实现一个最佳目标。未来和现有客户在9个月期间的实验结果表明,Doppiller可以确定最佳目标并适应客户工作量的变化。 Doppler还在过度供应的云层客户中找到了节省成本的机会,同时不损害能力或其他要求。Doppler 已经在Azure数据迁移助理 v5.5中整合并发布了这一级别,该助理每天收到数百项评估请求。