Atlas: Hybrid Cloud Migration Advisor for Interactive Microservices
Ka-Ho Chow, Umesh Deshpande, Veera Deenadhayalan, Sangeetha Seshadri,, Ling Liu

TL;DR
Atlas is a data-driven hybrid cloud migration advisor for microservices that optimizes API performance, availability, and cost, providing continuous recommendations and outperforming existing solutions.
Contribution
It introduces a novel approach that learns API component utilization and network footprints to recommend high-quality, customizable migration plans in hybrid cloud environments.
Findings
Achieves 21% better API latency performance
Reduces costs by 11% compared to existing solutions
Provides proactive, continuous migration recommendations
Abstract
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…
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Taxonomy
TopicsSoftware System Performance and Reliability · Cloud Computing and Resource Management · IoT and Edge/Fog Computing
