.png){kind=icon}
Return
LLM-based cost-aware task scheduling for cloud computing systems
.png){kind=icon}
OA
AI
DOI：10.1186/s13677-025-00822-0.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Cloud task scheduling faces significant challenges due to resource heterogeneity, conflicting optimization objectives, and dynamic workload fluctuations. Traditional heuristic algorithms often necessitate comprehensive knowledge of environmental parameters, significantly constraining their efficacy in dynamic cloud computing environments. While Deep Reinforcement Learning (DRL) methods have shown promise in intelligent scheduling via continuous environment interaction, they suffer from limited generalization to diverse cloud scenarios and lack decision interpretability. To address these shortcomings, this paper proposes LarS, a scheduling framework that employs Large Language Models (LLMs) as high-level decision agents for cloud task scheduling. In LarS, DRL agents trained in carefully chosen representative cloud environments generate a high-quality dataset of scheduling decisions, which is used to fine-tune an LLM. By jointly optimizing average response time, task success rate, and average rental cost, LarS achieves strong generalization across heterogeneous cloud deployments. Experimental results demonstrate that LarS surpasses current approaches in average response time, success rate, and average cost, and maintains strong generalization performance under varied experimental settings.
Keywords:
Cloud computing
Task scheduling
Deep reinforcement learning
Large language models
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
0
Papers:
103
Citations:
0
Organization
Cited Papers
ShareSaveMulti objective trust aware task scheduling algorithm in cloud computing using whale optimizationOAAI
OAAIShareSaveShareSaveLarge Language Model-Guided SARSA Algorithm for Dynamic Task Scheduling in Cloud Computing.png)
Mathematics
MathematicsIF0
PREAI
ShareSaveBatch Jobs Load Balancing Scheduling in Cloud Computing Using Distributional Reinforcement Learning
PREAI
ShareSaveWork Scheduling in Cloud Network Based on Deep Q-LSTM Models for Efficient Resource Utilization
PREAI
ShareSaveShareSave
View more