Optimizing Container Placement in Data Centers by Deep Reinforcement Learning
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| Publicado en: | Applied Sciences vol. 15, no. 10 (2025), p. 5720 |
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| Autor principal: | |
| Otros Autores: | |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3211859467 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2076-3417 | ||
| 024 | 7 | |a 10.3390/app15105720 |2 doi | |
| 035 | |a 3211859467 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231338 |2 nlm | ||
| 100 | 1 | |a Kim, Hyeonjeong | |
| 245 | 1 | |a Optimizing Container Placement in Data Centers by Deep Reinforcement Learning | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a As our society becomes increasingly digitized, the demand for computing power provided by data centers continues to grow; consequently, operating costs are increasing exponentially. Data centers supply virtualized servers to customers, primarily in the form of lightweight containers. Since the number of containers to be allocated is fixed, they should be optimally placed on physical servers to minimize the number of required servers and reduce costs. However, current data center operations do not prioritize reducing the number of physical servers through optimized container placement. Instead, containers are distributed across existing servers primarily to maintain stability. Therefore, costs associated with servers, auxiliary facilities, and electricity consumption have increased. To address this issue, we propose an optimization method that ensures economic efficiency without compromising system stability. Specifically, we utilize deep reinforcement learning (DRL), which has been widely applied in various fields, to optimize container placement. Our approach outperforms traditional heuristic algorithms and offers the additional advantage of handling fixed-size inputs, enabling flexible operation regardless of the number of containers. Using DRL in container placement has further reduced the number of servers and operating costs while enhancing overall system flexibility. | |
| 653 | |a Computer centers | ||
| 653 | |a Software | ||
| 653 | |a Packing problem | ||
| 653 | |a Deep learning | ||
| 653 | |a Costs | ||
| 653 | |a Cloud computing | ||
| 653 | |a Heuristic | ||
| 653 | |a Servers | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Energy consumption | ||
| 653 | |a Efficiency | ||
| 653 | |a Algorithms | ||
| 700 | 1 | |a Lee, Cheolhoon | |
| 773 | 0 | |t Applied Sciences |g vol. 15, no. 10 (2025), p. 5720 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211859467/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211859467/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211859467/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |