Affiliation:
1. School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, China
2. Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum-Beijing, Beijing 102249, China
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.
Funder
Project of Cultivation for Young Top-Notch Talents of Beijing Municipal Institutions
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献