dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917581006307328 |
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| author | Aftab, Asad Rehman, Semeen |
| author_facet | Aftab, Asad Rehman, Semeen |
| contents | Multi-access-Mobile Edge Computing (MEC) is a promising solution for computationally demanding rigorous applications, that can meet 6G network service requirements. However, edge servers incur high computation costs during task processing. In this paper, we proposed a technique to minimize the total computation and communication overhead for optimal resource utilization with joint computational offloading that enables a green environment. Our optimization problem is NP-hard; thus, we proposed a decentralized Reinforcement Learning (dRL) approach where we eliminate the problem of dimensionality and over-estimation of the value functions. Compared to baseline schemes our technique achieves a 37.03% reduction in total system costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00874 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network Aftab, Asad Rehman, Semeen Networking and Internet Architecture Artificial Intelligence Signal Processing Multi-access-Mobile Edge Computing (MEC) is a promising solution for computationally demanding rigorous applications, that can meet 6G network service requirements. However, edge servers incur high computation costs during task processing. In this paper, we proposed a technique to minimize the total computation and communication overhead for optimal resource utilization with joint computational offloading that enables a green environment. Our optimization problem is NP-hard; thus, we proposed a decentralized Reinforcement Learning (dRL) approach where we eliminate the problem of dimensionality and over-estimation of the value functions. Compared to baseline schemes our technique achieves a 37.03% reduction in total system costs. |
| title | dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network |
| topic | Networking and Internet Architecture Artificial Intelligence Signal Processing |
| url | https://arxiv.org/abs/2402.00874 |