Reinforcement Learning Based Task Scheduling for Environmentally Sustainable Federated Cloud Computing

Zhibao Wang, Shuaijun Chen, Lu Bai, Juntao Gao, Jinhua Tao, RR Bond, Maurice Mulvenna

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The significant energy consumption within data centers is an essential contributor to global energy consumption and carbon emissions. Therefore, reducing energy consumption and carbon emissions in data centers plays a crucial role in sustainable development. Traditional cloud computing has reached a bottleneck, primarily due to high energy consumption. The emerging federated cloud approach can reduce the energy consumption and carbon emissions of cloud data centers by leveraging the geographical differences of multiple cloud data centers in a federated cloud. In this paper, we propose Eco-friendly Reinforcement Learning in Federated Cloud (ERLFC), a framework that uses reinforcement learning for task scheduling in a federated cloud environment. ERLFC aims to intelligently consider the state of each data center and effectively harness the variations in energy and carbon emission ratios across geographically distributed cloud data centers in the federated cloud. We build ERLFC using Actor-Critic algorithm, which select the appropriate data center to assign a task based on various factors such as energy consumption, cooling method, waiting time of the task, energy type, emission ratio, and total energy consumption of the current cloud data center and the details of the next task. To demonstrate the effectiveness of ERLFC, we conducted simulations based on real-world task execution data, and the results show that ERLFC can effectively reduce energy consumption and emissions during task execution. In comparison to Round Robin, Random, SO, and GJO algorithms, ERLFC achieves respective reductions of 1.09, 1.08, 1.21, and 1.26 times in terms of energy saving and emission reduction.
Original languageEnglish
Article number174
JournalJournal of Cloud Computing: Advances, Systems and Applications
Issue number1
Early online date7 Dec 2023
Publication statusPublished online - 7 Dec 2023

Bibliographical note

Funding Information:
This work was supported in part by TUOHAI special project 2020 from Bohai Rim Energy Research Institute of Northeast Petroleum University under Grant HBHZX202002, project of Excellent and Middle-aged Scientific Research Innovation Team of Northeast Petroleum University under Grant KYCXTD201903, Heilongjiang Province Higher Education Teaching Reform Project under Grant SJGY20200125 and National Key Research and Development Program of China under Grant 2022YFC330160204.

Publisher Copyright:
© 2023, The Author(s).


  • Carbon emissions
  • Cloud computing
  • Energy efficiency
  • Federated cloud
  • Reinforcement learning
  • Task scheduling


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