발간년도 : [2022]
| 논문정보 |
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| 논문명(한글) |
[Vol.17, No.3] Designing Low-Cost and High-Availability Networks with Reinforcement Learning |
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| 논문투고자 |
Choong-Hee Cho |
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| 논문내용 |
Telecommunication disasters have been regarded as national disasters that cause social chaos and economic losses in various fields such as medical care, emergency relief, public safety, finance, and commerce. Such disasters can be prevented by considering network availability during network design. However, telecommunication companies build networks with more focus on reducing construction costs than providing sufficient network availability. Network construction costs can be minimized if the network of other telecommunication companies are utilized without requiring the construction of 쟮ew protection paths to avoid traffic failure. Although most telecommunication companies have built their communication networks in geographically similar locations, they do not share networks despite the fact that shared networks could lead to improved network availability and reduced construction costs. Therefore, in this study, we introduced a method to minimize the network construction costs while improving the overall network availability. We defined an optimization problem to determine the optimal set of cable lines that can interconnect the networks and used a deep reinforcement learning algorithm to select the cable lines. The deep reinforcement learning algorithm learns to select the optimal cable line by repeating episodes of cable selection until target availability is satisfied. The result was compared with that of a general greedy algorithm to evaluate the reduction in construction costs. |
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