An efficient deep reinforcement learning based task scheduler in cloud-fog environment

被引:0
|
作者
Choppara, Prashanth [1 ]
Mangalampalli, Sudheer [2 ]
机构
[1] VIT AP Univ, Amaravati 522237, Andhra Pradesh, India
[2] Manipal Acad Higher Educ, Manipal Inst Technol Bengaluru, Dept Comp Sci & Engn, Manipal, India
来源
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2025年 / 28卷 / 01期
关键词
Fog computing; Reinforcement learning; Makespan; Energy consumption; Fault tolerance; OPTIMIZATION;
D O I
10.1007/s10586-024-04712-z
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Efficient task scheduling in cloud and fog computing environments remains a significant challenge due to the diverse nature and critical processing requirements of tasks originating from heterogeneous devices. Traditional scheduling methods often struggle with high latency and inadequate processing times, especially in applications demanding strict computational efficiency. To address these challenges, this paper proposes an advanced fog-cloud integration approach utilizing a deep reinforcement learning-based task scheduler, DRLMOTS (Deep Reinforcement Learning based Multi Objective Task Scheduler in Cloud Fog Environment). This novel scheduler intelligently evaluates task characteristics, such as length and processing capacity, to dynamically allocate computation to either fog nodes or cloud resources. The methodology leverages a Deep Q-Learning Network model and includes extensive simulations using both randomized workloads and real-world Google Jobs Workloads. Comparative analysis demonstrates that DRLMOTS significantly outperforms existing baseline algorithms such as CNN, LSTM, and GGCN, achieving a substantial reduction in makespan by up to 26.80%, 18.84, and 13.83% and decreasing energy consumption by up to 39.60%, 30.29%, and 27.11%. Additionally, the proposed scheduler enhances fault tolerance, showcasing improvements of up to 221.89%, 17.05%, and 11.05% over conventional methods. These results validate the efficiency and robustness of DRLMOTS in optimizing task scheduling in fog-cloud environments.
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页数:26
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