Elastic Scaling of Resources for Energy-Efficient Container Cloud Using Reinforcement Learning

In this paper, we aim to save the total energy consumption of servers through elastic scaling of CPU resources in container cloud. To be practical, we propose an online scheduling method, which consists of three parts: container placement, vertical scaling and migration. 1) For container placement, we design an algorithm based on dynamic threshold, resource balancing and delayed running. When there are PMs (Physical Machines) turned on, the CPU threshold increases so that the containers can be placed onto fewest possible PMs. To make full use of multi-dimensional resources of PM, we put forward a resource balancing strategy. Since the number of CPU cores can be scaled dynamically in containers’ run time, the start time of containers can be delayed without violating deadlines. 2) For vertical scaling, a collaborative multi-agent reinforcement learning (MARL) algorithm is proposed to adjust the container’s CPU, so that the containers on the same PM can finish simultaneously if possible. Then, the PM can be turned off to save energy. 3) To further reduce total energy consumption, we consider migrating the containers from underloaded PMs and overloaded PMs. Experiment results show the superior performance of our method to that of the state-of-the-art.