Parallel Simulation Multi-Sample Task Scheduling Approach Based on Deep Reinforcement Learning in Cloud Computing Environment
Complex scenario analysis and evaluation simulations often involve multiple sets of simulation applications with different combinations of parameters, thus resulting in high computing power consumption, which is one of the factors that limits the efficiency of multi-sample parallel simulations. Clou...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/14/2249 |
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| Summary: | Complex scenario analysis and evaluation simulations often involve multiple sets of simulation applications with different combinations of parameters, thus resulting in high computing power consumption, which is one of the factors that limits the efficiency of multi-sample parallel simulations. Cloud computing provides considerable amounts of cheap and convenient computing resources, thus providing efficient support for multi-sample simulation tasks. However, traditional simulation scheduling methods do not consider the collaborative parallel scheduling of multiple samples and multiple entities under multi-objective constraints. Deep reinforcement learning methods can continuously learn and adjust their strategies through interactions with the environment, demonstrating strong adaptability in response to dynamically changing task requirements. Therefore, herein, a parallel simulation multi-sample task scheduling method based on deep reinforcement learning in a cloud computing environment is proposed. The method collects cluster load information and simulation application information as state inputs in the cloud environment, designs a multi-objective reward function to balance the cost and execution efficiency, and uses deep Q-networks (DQNs) to train agents for intelligent scheduling of multi-sample parallel simulation tasks. In a real cloud environment, the proposed method demonstrates runtime reductions of 4–11% and execution cost savings of 11–22% compared to the Round-Robin algorithm, Best Fit algorithm, and genetic algorithm. |
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| ISSN: | 2227-7390 |