Auto-Scaling Containerized Applications in Geo-Distributed Clouds

Abstract

As a lightweight and flexible infrastructure solution, containers have increasingly been used for application deployment on a global scale. By rapidly scaling containers at different locations, the deployed applications can handle dynamic workloads from the worldwide user community. Existing studies usually focus on the (dynamic) container scaling within a single data center or the (static) container deployment across geo-distributed data centers. This article studies an increasingly important container scaling problem for application deployment in geo-distributed clouds. Reinforcement learning (RL) has been widely used in container scaling due to its high adaptability and robustness. To handle high-dimensional state spaces in geo-distributed clouds, we propose a deep RL algorithm, named DeepScale , to auto-scale containerized applications. DeepScale innovatively utilizes multi-step predicted future workloads to train a holistic scaling policy. It features several newly designed algorithmic components, including a domain-tailored state constructor and a heuristic-based action executor. These new algorithmic components are essential to meet the requirements of low deployment costs and achieve desirable application performance. We conduct extensive simulation studies using real-world datasets. The results show that DeepScale can significantly outperform an industry-leading scaling strategy and two state-of-the-art baselines in terms of both cost-effectiveness and constraint satisfaction.