Efficient Deep Learning Job Allocation in Cloud Systems by Predicting Resource Consumptions including GPU and CPU
One objective of GPU scheduling in cloud systems is to minimize the completion times of given deep learning models. This is important for deep learning in cloud environments because deep learning workloads require a lot of time to finish, and misallocation of these workloads can create a huge increa...
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University North
2025-01-01
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| Online Access: | https://hrcak.srce.hr/file/480464 |
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| author | Abuda Chad Ferrino Tae Young Choe |
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| description | One objective of GPU scheduling in cloud systems is to minimize the completion times of given deep learning models. This is important for deep learning in cloud environments because deep learning workloads require a lot of time to finish, and misallocation of these workloads can create a huge increase in job completion time. Difficulties of GPU scheduling come from a diverse type of parameters including model architectures and GPU types. Some of these model architectures are CPU-intensive rather than GPU-intensive which creates a different hardware requirement when training different models. The previous GPU scheduling research had used a small set of parameters, which did not include CPU parameters, which made it difficult to reduce the job completion time (JCT). This paper introduces an improved GPU scheduling approach that reduces job completion time by predicting execution time and various resource consumption parameters including GPU Utilization%, GPU Memory Utilization%, GPU Memory, and CPU Utilization%. The experimental results show that the proposed model improves JCT by up to 40.9% on GPU Allocation based on Computing Efficiency compared to Driple. |
| format | Article |
| id | doaj-art-0c2e900834784fd0a1ce9c9fdaeafc41 |
| institution | DOAJ |
| issn | 1846-6168 1848-5588 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | University North |
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| series | Tehnički Glasnik |
| spelling | doaj-art-0c2e900834784fd0a1ce9c9fdaeafc412025-08-20T02:39:38ZengUniversity NorthTehnički Glasnik1846-61681848-55882025-01-0119346147210.31803/tg-20240112104444Efficient Deep Learning Job Allocation in Cloud Systems by Predicting Resource Consumptions including GPU and CPUAbuda Chad Ferrino0Tae Young Choe1Department of Computer AI Convergence Engineering, Kumoh National Institute of Technology, 61 Daehak-ro, Gumi-si, Gyeongsangbuk-do, 39177, Republic of KoreaDepartment of Computer AI Convergence Engineering, Kumoh National Institute of Technology, 61 Daehak-ro, Gumi-si, Gyeongsangbuk-do, 39177, Republic of KoreaOne objective of GPU scheduling in cloud systems is to minimize the completion times of given deep learning models. This is important for deep learning in cloud environments because deep learning workloads require a lot of time to finish, and misallocation of these workloads can create a huge increase in job completion time. Difficulties of GPU scheduling come from a diverse type of parameters including model architectures and GPU types. Some of these model architectures are CPU-intensive rather than GPU-intensive which creates a different hardware requirement when training different models. The previous GPU scheduling research had used a small set of parameters, which did not include CPU parameters, which made it difficult to reduce the job completion time (JCT). This paper introduces an improved GPU scheduling approach that reduces job completion time by predicting execution time and various resource consumption parameters including GPU Utilization%, GPU Memory Utilization%, GPU Memory, and CPU Utilization%. The experimental results show that the proposed model improves JCT by up to 40.9% on GPU Allocation based on Computing Efficiency compared to Driple.https://hrcak.srce.hr/file/480464cloud computingconvolutional neural networkdeep learningGPU job schedulingperformance estimation |
| spellingShingle | Abuda Chad Ferrino Tae Young Choe Efficient Deep Learning Job Allocation in Cloud Systems by Predicting Resource Consumptions including GPU and CPU Tehnički Glasnik cloud computing convolutional neural network deep learning GPU job scheduling performance estimation |
| title | Efficient Deep Learning Job Allocation in Cloud Systems by Predicting Resource Consumptions including GPU and CPU |
| title_full | Efficient Deep Learning Job Allocation in Cloud Systems by Predicting Resource Consumptions including GPU and CPU |
| title_fullStr | Efficient Deep Learning Job Allocation in Cloud Systems by Predicting Resource Consumptions including GPU and CPU |
| title_full_unstemmed | Efficient Deep Learning Job Allocation in Cloud Systems by Predicting Resource Consumptions including GPU and CPU |
| title_short | Efficient Deep Learning Job Allocation in Cloud Systems by Predicting Resource Consumptions including GPU and CPU |
| title_sort | efficient deep learning job allocation in cloud systems by predicting resource consumptions including gpu and cpu |
| topic | cloud computing convolutional neural network deep learning GPU job scheduling performance estimation |
| url | https://hrcak.srce.hr/file/480464 |
| work_keys_str_mv | AT abudachadferrino efficientdeeplearningjoballocationincloudsystemsbypredictingresourceconsumptionsincludinggpuandcpu AT taeyoungchoe efficientdeeplearningjoballocationincloudsystemsbypredictingresourceconsumptionsincludinggpuandcpu |