@inproceedings{449f46efa9844314932eb38ee7331f1d,
title = "Multidimensional Resource and Load Collaborative Scheduling Algorithm Based on Reinforcement Learning for Cloud Data Centers",
abstract = "Task scheduling for multi-dimensional resources is one of the most fundamental problems in cloud data centers (DC). Among existing resolutions, the Q-learning model has been considered an excellent tool for fast task scheduling in DC environments. In this paper, we propose a load-balancing model for multi-dimensional resource scheduling in a cloud DC and a Q-learning based task scheduling algorithm (TSQL) that aims to reduce task makespan time and improve resource utilization. Simulation results show that, compared with existing algorithms, our algorithm optimizes 46.92\%, 33.67\% in makespan and resource utilization.",
keywords = "Cloud computing, load balancing, reinforcement learning, task scheduling",
author = "Hui Guo and Fu Wang and Qi Zhang and Jingjing Gao and Dong Guo and Qinghua Tian and Feng Tian and Xiaoli Yin",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 21st International Conference on Optical Communications and Networks, ICOCN 2023 ; Conference date: 31-07-2023 Through 03-08-2023",
year = "2023",
doi = "10.1109/ICOCN59242.2023.10236394",
language = "English",
series = "2023 21st International Conference on Optical Communications and Networks, ICOCN 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 21st International Conference on Optical Communications and Networks, ICOCN 2023",
address = "United States",
}