Hybrid Recurrent Neural Network and Decision Tree Scheduling for Energy-Efficient Resource Allocation in Cloud Computing
Efficient resource allocation in cloud computing is critical for optimizing execution time, minimizing delays, and improving system reliability. Traditional heuristic-based scheduling approaches struggle to adapt to dynamic workloads and heterogeneous virtual machines (VMs), leading to suboptimal pe...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
|
| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2025/11/epjconf_cofmer2025_05007.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850152212980826112 |
|---|---|
| author | Sefati Seyed Salar Vulpe Alexandru Popovici Eduard Fratu Octavian |
| author_facet | Sefati Seyed Salar Vulpe Alexandru Popovici Eduard Fratu Octavian |
| author_sort | Sefati Seyed Salar |
| collection | DOAJ |
| description | Efficient resource allocation in cloud computing is critical for optimizing execution time, minimizing delays, and improving system reliability. Traditional heuristic-based scheduling approaches struggle to adapt to dynamic workloads and heterogeneous virtual machines (VMs), leading to suboptimal performance. This paper proposes a hybrid scheduling framework that integrates Recurrent Neural Networks (RNNs) for execution time prediction and Decision Trees (DTs) for VM classification, enhancing resource allocation efficiency. The RNN model uses historical execution data to accurately predict task execution time, while the DT model classifies VMs based on performance characteristics, ensuring optimal task-to- VM assignments. The proposed method dynamically adapts to workload variations, reducing execution delays and improving Quality of Service (QoS) metrics. Experimental evaluations demonstrate that the hybrid RNN-DT approach outperforms traditional scheduling methods and metaheuristic algorithms, such as Genetic Algorithm and Artificial Bee Colony, in terms of execution time reduction, reliability, and delay minimization. |
| format | Article |
| id | doaj-art-5f7103dfbce84200ab13577becad574e |
| institution | OA Journals |
| issn | 2100-014X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj-art-5f7103dfbce84200ab13577becad574e2025-08-20T02:26:02ZengEDP SciencesEPJ Web of Conferences2100-014X2025-01-013260500710.1051/epjconf/202532605007epjconf_cofmer2025_05007Hybrid Recurrent Neural Network and Decision Tree Scheduling for Energy-Efficient Resource Allocation in Cloud ComputingSefati Seyed Salar0Vulpe Alexandru1Popovici Eduard2Fratu Octavian3POLITEHNICA Bucharest, Research Center CAMPUSPOLITEHNICA Bucharest, Research Center CAMPUSPOLITEHNICA Bucharest, Telecommunication DepartmentPOLITEHNICA Bucharest, Research Center CAMPUSEfficient resource allocation in cloud computing is critical for optimizing execution time, minimizing delays, and improving system reliability. Traditional heuristic-based scheduling approaches struggle to adapt to dynamic workloads and heterogeneous virtual machines (VMs), leading to suboptimal performance. This paper proposes a hybrid scheduling framework that integrates Recurrent Neural Networks (RNNs) for execution time prediction and Decision Trees (DTs) for VM classification, enhancing resource allocation efficiency. The RNN model uses historical execution data to accurately predict task execution time, while the DT model classifies VMs based on performance characteristics, ensuring optimal task-to- VM assignments. The proposed method dynamically adapts to workload variations, reducing execution delays and improving Quality of Service (QoS) metrics. Experimental evaluations demonstrate that the hybrid RNN-DT approach outperforms traditional scheduling methods and metaheuristic algorithms, such as Genetic Algorithm and Artificial Bee Colony, in terms of execution time reduction, reliability, and delay minimization.https://www.epj-conferences.org/articles/epjconf/pdf/2025/11/epjconf_cofmer2025_05007.pdf |
| spellingShingle | Sefati Seyed Salar Vulpe Alexandru Popovici Eduard Fratu Octavian Hybrid Recurrent Neural Network and Decision Tree Scheduling for Energy-Efficient Resource Allocation in Cloud Computing EPJ Web of Conferences |
| title | Hybrid Recurrent Neural Network and Decision Tree Scheduling for Energy-Efficient Resource Allocation in Cloud Computing |
| title_full | Hybrid Recurrent Neural Network and Decision Tree Scheduling for Energy-Efficient Resource Allocation in Cloud Computing |
| title_fullStr | Hybrid Recurrent Neural Network and Decision Tree Scheduling for Energy-Efficient Resource Allocation in Cloud Computing |
| title_full_unstemmed | Hybrid Recurrent Neural Network and Decision Tree Scheduling for Energy-Efficient Resource Allocation in Cloud Computing |
| title_short | Hybrid Recurrent Neural Network and Decision Tree Scheduling for Energy-Efficient Resource Allocation in Cloud Computing |
| title_sort | hybrid recurrent neural network and decision tree scheduling for energy efficient resource allocation in cloud computing |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2025/11/epjconf_cofmer2025_05007.pdf |
| work_keys_str_mv | AT sefatiseyedsalar hybridrecurrentneuralnetworkanddecisiontreeschedulingforenergyefficientresourceallocationincloudcomputing AT vulpealexandru hybridrecurrentneuralnetworkanddecisiontreeschedulingforenergyefficientresourceallocationincloudcomputing AT popovicieduard hybridrecurrentneuralnetworkanddecisiontreeschedulingforenergyefficientresourceallocationincloudcomputing AT fratuoctavian hybridrecurrentneuralnetworkanddecisiontreeschedulingforenergyefficientresourceallocationincloudcomputing |