George: Learning to Place Long-Lived Containers in Large Clusters with Operation Constraints

Abstract

Online cloud services are widely deployed as Long-Running Applications (LRAs) hosted in containers. Placing LRA containers turns out to be particularly challenging due to the complex interference between co-located containers and the operation constraints in production clusters such as fault tolerance, disaster avoidance and incremental deployment. Existing schedulers typically provide APIs for operators to manually specify the container scheduling requirements and offer only qualitative scheduling guidelines for container placement. Such schedulers, do not perform well in terms of both performance and scale, while also requiring manual intervention. In this work, we propose George, an end-to-end generalpurpose LRA scheduler by leveraging the state-of-the-art Reinforcement Learning (RL) techniques to intelligently schedule LRA containers. We present an optimal container placement formulation for the first time with the objective of maximizing container placement performance subject to a set of operation constraints. One fundamental challenge in scheduling is to categorically satisfy different operation constraints in practice; specifically, to guarantee hard constraints and ensure soft constraints violations within a pre-defined threshold. We design a novel projection-based proximal policy optimization (PPPO) algorithm in combination with an Integer Linear optimization technique to intelligently schedule LRA containers under operation constraints. In order to reduce the training time, we apply transfer learning technique by taking advantage of the similarity in different LRA scheduling events. We prove theoretically that our proposed algorithm is effective, stable, and safe. We implement George as a plug-in service in Docker Swarm. Our in-house cluster demonstrates that George can maximize the LRA performance while enforcing the hard constraints and the soft constraints with a pre-defined threshold. The experiments show that George improves LRA performance and scale tremendously by requiring less than 1 hour scheduling time in a large cluster with 2K containers and 700 machines, 16x faster than existing schedulers. Compared with state-of-the-art alternatives, George also achieves 26% higher container performance with up to 70% lower constraint violation.