Hybrid cloud provides an attractive solution to microservices for better resource elasticity. A subset of application components can be offloaded from the on-premises cluster to the cloud, where they can readily access additional resources. However, the selection of this subset is challenging because of the large number of possible combinations. A poor choice degrades the application performance, disrupts the critical services, and increases the cost to the extent of making the use of hybrid cloud unviable. This paper presents Atlas, a hybrid cloud migration advisor. Atlas uses a data-driven approach to learn how each user-facing API utilizes different components and their network footprints to drive the migration decision. It learns to accelerate the discovery of high-quality migration plans from millions and offers recommendations with customizable trade-offs among three quality indicators: end-to-end latency of user-facing APIs representing application performance, service availability, and cloud hosting costs. Atlas continuously monitors the application even after the migration for proactive recommendations. Our evaluation shows that Atlas can achieve 21% better API performance (latency) and 11% cheaper cost with less service disruption than widely used solutions.

翻译：摘要：混合云为微服务提供了更具吸引力的资源弹性解决方案。部分应用组件可从本地集群卸载至云端，以便更便捷地获取额外资源。然而，由于可能的组合数量庞大，子集的选择极具挑战性。不当的选择会降低应用性能、干扰关键服务，并增加成本，甚至使混合云的应用变得不可行。本文提出了Atlas——一种混合云迁移顾问。Atlas采用数据驱动方法，学习每个面向用户的API如何利用不同组件及其网络特征，从而驱动迁移决策。它能够从数百万种方案中加速发现高质量迁移计划，并提供可定制的折中建议，权衡三个质量指标：代表应用性能的用户端到端API延迟、服务可用性及云托管成本。即使迁移完成后，Atlas仍持续监控应用，以提供主动建议。评估表明，与广泛使用的解决方案相比，Atlas可将API性能（延迟）提升21%，成本降低11%，同时减少服务中断。