Online Microservice Orchestration for IoT via Multiobjective Deep Reinforcement Learning

Abstract

By providing loosely coupled, lightweight, and independent services, the microservice architecture is promising for large-scale and complex service provision requirements in the Internet of Things (IoT). However, it requires more fine-grained resource management and orchestration for service provision. Most of the existing microservice orchestration solutions are based on those designed for the traditional cloud. They can only provide coarse-grained resource allocation using possibly conflicting weighted objectives. In this article, we present a fine-grained microservice orchestration approach to provide services online for dynamic requests of IoT applications. By using a fine-grained resource model of energy cost and service end-to-end response time of orchestrated microservices, we formulate the microservice orchestration problem as a multiobjective Markov decision process. We then propose a multiobjective optimization solution based on deep reinforcement learning (DRL) to simultaneously reduce energy consumption and response time. Through extensive experiments, our proposed algorithm presents significant performance results than the state of the art. To the best of our knowledge, this is the first work that addresses microservice orchestration using DRL for multiple conflicting objectives.