Elastic Deployment of Software Containers in Geo-Distributed Computing Environments

Abstract

Software containers are ever more adopted to manage and execute distributed applications. Indeed, they enable to quickly scale the amount of computing resources by means of horizontal and vertical elasticity. Most of the existing works consider the deployment of containers in centralized data centers. However, to exploit the diffused presence of edge/fog computing resources, we need new solutions that deploy containers while also considering their placement on decentralized resources. In this paper, we present a two-step approach that manages the run-time adaptation of container-based applications deployed over geo-distributed virtual machines. In the first step, our approach exploits Reinforcement Learning (RL) solutions to control the horizontal and vertical elasticity of the containers. In the second step, it addresses the container placement by solving a suitable integer linear programming problem or using a network-aware heuristic. A wide set of simulation results shows the benefits and flexibility of the proposed approach, which can satisfy stringent application requirements expressed in terms of response time percentiles.