AI-enhanced modelling of queueing and scheduling systems in cloud computing
Abstract Cloud computing environments encounter significant challenges in resource management through queueing and scheduling systems, as traditional methods struggle with dynamic workload optimization. This research introduces an innovative AI-enhanced framework combining deep workload prediction a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-03-01
|
| Series: | Discover Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-025-06755-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850063789393707008 |
|---|---|
| author | Himani Chaudhary Geetanjali Sharma D. K. Nishad Saifullah Khalid |
| author_facet | Himani Chaudhary Geetanjali Sharma D. K. Nishad Saifullah Khalid |
| author_sort | Himani Chaudhary |
| collection | DOAJ |
| description | Abstract Cloud computing environments encounter significant challenges in resource management through queueing and scheduling systems, as traditional methods struggle with dynamic workload optimization. This research introduces an innovative AI-enhanced framework combining deep workload prediction and reinforcement learning for dynamic scheduling. The framework features a dual-layer neural network architecture with a hybrid decision engine that merges conventional queueing metrics with learned policies. Experimental results in a simulated cloud environment showcase remarkable improvements: a 30% decrease in average waiting time, 25% optimization in queue length, and 91% peak resource utilization compared to traditional approaches. The AI-enhanced model demonstrates 20–35% higher throughput rates across various workload intensities, with the reinforcement learning scheduler maintaining steady performance under high loads. Statistical confidence levels exceed 95%, validating the approach's effectiveness. The research provides practical solutions for cloud service providers, enabling implementation of efficient, adaptive resource management systems that reduce operational costs while enhancing service quality. The modular architecture ensures scalability and seamless integration with existing cloud infrastructure, making it particularly valuable for large-scale production environments. |
| format | Article |
| id | doaj-art-242ba8b7bc0b4e029b395646cb452463 |
| institution | DOAJ |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-242ba8b7bc0b4e029b395646cb4524632025-08-20T02:49:30ZengSpringerDiscover Applied Sciences3004-92612025-03-017412810.1007/s42452-025-06755-2AI-enhanced modelling of queueing and scheduling systems in cloud computingHimani Chaudhary0Geetanjali Sharma1D. K. Nishad2Saifullah Khalid3Department of Mathematics and Statistics, Banasthali VidyapithDepartment of Mathematics and Statistics, Banasthali VidyapithDepartment of Electrical Engineering, Dr. Shakuntala Misra National Rehabilitation UniversityIBM Multi Activities Co. Ltd.Abstract Cloud computing environments encounter significant challenges in resource management through queueing and scheduling systems, as traditional methods struggle with dynamic workload optimization. This research introduces an innovative AI-enhanced framework combining deep workload prediction and reinforcement learning for dynamic scheduling. The framework features a dual-layer neural network architecture with a hybrid decision engine that merges conventional queueing metrics with learned policies. Experimental results in a simulated cloud environment showcase remarkable improvements: a 30% decrease in average waiting time, 25% optimization in queue length, and 91% peak resource utilization compared to traditional approaches. The AI-enhanced model demonstrates 20–35% higher throughput rates across various workload intensities, with the reinforcement learning scheduler maintaining steady performance under high loads. Statistical confidence levels exceed 95%, validating the approach's effectiveness. The research provides practical solutions for cloud service providers, enabling implementation of efficient, adaptive resource management systems that reduce operational costs while enhancing service quality. The modular architecture ensures scalability and seamless integration with existing cloud infrastructure, making it particularly valuable for large-scale production environments.https://doi.org/10.1007/s42452-025-06755-2AI-enhanced resource managementCloud computing optimizationReinforcement learning schedulingMulti-queue load balancing |
| spellingShingle | Himani Chaudhary Geetanjali Sharma D. K. Nishad Saifullah Khalid AI-enhanced modelling of queueing and scheduling systems in cloud computing Discover Applied Sciences AI-enhanced resource management Cloud computing optimization Reinforcement learning scheduling Multi-queue load balancing |
| title | AI-enhanced modelling of queueing and scheduling systems in cloud computing |
| title_full | AI-enhanced modelling of queueing and scheduling systems in cloud computing |
| title_fullStr | AI-enhanced modelling of queueing and scheduling systems in cloud computing |
| title_full_unstemmed | AI-enhanced modelling of queueing and scheduling systems in cloud computing |
| title_short | AI-enhanced modelling of queueing and scheduling systems in cloud computing |
| title_sort | ai enhanced modelling of queueing and scheduling systems in cloud computing |
| topic | AI-enhanced resource management Cloud computing optimization Reinforcement learning scheduling Multi-queue load balancing |
| url | https://doi.org/10.1007/s42452-025-06755-2 |
| work_keys_str_mv | AT himanichaudhary aienhancedmodellingofqueueingandschedulingsystemsincloudcomputing AT geetanjalisharma aienhancedmodellingofqueueingandschedulingsystemsincloudcomputing AT dknishad aienhancedmodellingofqueueingandschedulingsystemsincloudcomputing AT saifullahkhalid aienhancedmodellingofqueueingandschedulingsystemsincloudcomputing |