Exponentially Weighted Algorithm for Online Network Resource Allocation with Long-Term Constraints
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916235114971136 |
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| author | Sid-Ali, Ahmed Lambadaris, Ioannis Zhao, Yiqiang Q. Shaikhet, Gennady Asgharnia, Amirhossein |
| author_facet | Sid-Ali, Ahmed Lambadaris, Ioannis Zhao, Yiqiang Q. Shaikhet, Gennady Asgharnia, Amirhossein |
| contents | This paper studies an online optimal resource reservation problem in communication networks with job transfers where the goal is to minimize the reservation cost while maintaining the blocking cost under a certain budget limit. To tackle this problem, we propose a novel algorithm based on a randomized exponentially weighted method that encompasses long-term constraints. We then analyze the performance of our algorithm by establishing an upper bound for the associated regret and the cumulative constraint violations. Finally, we present numerical experiments where we compare the performance of our algorithm with those of reinforcement learning where we show that our algorithm surpasses it. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02373 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exponentially Weighted Algorithm for Online Network Resource Allocation with Long-Term Constraints Sid-Ali, Ahmed Lambadaris, Ioannis Zhao, Yiqiang Q. Shaikhet, Gennady Asgharnia, Amirhossein Optimization and Control Machine Learning This paper studies an online optimal resource reservation problem in communication networks with job transfers where the goal is to minimize the reservation cost while maintaining the blocking cost under a certain budget limit. To tackle this problem, we propose a novel algorithm based on a randomized exponentially weighted method that encompasses long-term constraints. We then analyze the performance of our algorithm by establishing an upper bound for the associated regret and the cumulative constraint violations. Finally, we present numerical experiments where we compare the performance of our algorithm with those of reinforcement learning where we show that our algorithm surpasses it. |
| title | Exponentially Weighted Algorithm for Online Network Resource Allocation with Long-Term Constraints |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2405.02373 |