Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost. We employ a Data Science (DS) driven solution to predict user load and leverage these predictions to optimize resource allocation. To this end, we built the Seagull infrastructure that processes per-server telemetry, validates the data, trains and deploys ML models. The models are used to predict customer load per server (24h into the future), and optimize service operations. Seagull continually re-evaluates accuracy of predictions, fallback to previously known good models and triggers alerts as appropriate. We deployed this infrastructure in production for PostgreSQL and MySQL servers across all Azure regions, and applied it to the problem of scheduling server backups during low-load time. This minimizes interference with user-induced load and improves customer experience.

中文翻译：

---

Seagull：用于负载预测和优化资源分配的基础架构

Microsoft Azure 致力于为客户提供高质量的服务，尤其是在客户活动频繁期间，同时控制成本。我们采用数据科学 (DS) 驱动的解决方案来预测用户负载并利用这些预测来优化资源分配。为此，我们构建了 Seagull 基础设施，用于处理每台服务器的遥测数据、验证数据、训练和部署 ML 模型。这些模型用于预测每台服务器的客户负载（未来 24 小时），并优化服务运营。Seagull 不断重新评估预测的准确性，回退到以前已知的良好模型并在适当时触发警报。我们在所有 Azure 区域的 PostgreSQL 和 MySQL 服务器的生产中部署了这个基础设施，并将其应用于在低负载时间安排服务器备份的问题。这最大限度地减少了对用户引起的负载的干扰并改善了客户体验。