Reinforcement Learning-Based Intelligent Task Scheduling for Large-Scale IoT Systems

Abstract

The new-generation of Internet of Things (NG-IoT) brings a wide range of challenging problems. At the same time, cloud computing technology is an important foundation for the development of the IoT. In this article, we focus on the task scheduling problem in IoT systems in cloud computing environment. Our goal is to minimize the task runtime. It is well known that the problem of the task scheduling has been a challenging problem. In the last decade, despite being theoretically hard problem, researchers design lots of state-of-the-art algorithms for solving this problem. In our work, we propose a novel efficient reinforcement learning (RL) algorithm to solve the task scheduling problem in IoT systems (EATS), which combines combinatorial optimization to make our proposed algorithm have stable lower bounds. We process a batch of tasks at a time, make decisions on task selection through reinforcement learning, and solve them further through combinatorial optimization methods. The results of the experiments show that our proposed algorithm has outstanding performance in different environments.