Resource learning aware algorithm for route prediction and resources allocation in elastic optical networks with network analytics
We have proposed two algorithms in this paper for dynamic traffic in elastic optical networks (EON). Algorithm1 considers the spatial access blocking probability metric (SABPM) during dynamic operation and route traffic to a path with lower SABPM. Therefore, it considers a path with more contiguous...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024014312 |
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| author | Akhtar Nawaz Khan Hassan Yousif Ahmed Medien Zeghid |
| author_facet | Akhtar Nawaz Khan Hassan Yousif Ahmed Medien Zeghid |
| author_sort | Akhtar Nawaz Khan |
| collection | DOAJ |
| description | We have proposed two algorithms in this paper for dynamic traffic in elastic optical networks (EON). Algorithm1 considers the spatial access blocking probability metric (SABPM) during dynamic operation and route traffic to a path with lower SABPM. Therefore, it considers a path with more contiguous blocks to support heterogeneous bandwidths (BW) and modulation schemes. Algorithm1 keeps a balance of fragmentation on available routes. Algorithm2 makes routing decision for dynamic traffic based on the maximum idle slots as well as minimum SABPM. Therefore, it selects a less fragmented path with more number of available contiguous blocks to support heterogeneous BW. Algorithm2 balances the path utilization and avoids over utilization of congested route. We have evaluated the performance of both algorithms in terms of spatial external fragmentation, contiguity ratio, contiguous aligned spectrum ratio, SABPM, and BW blocking probability (BwBP). Python with seaborn, pandas, and other libraries are used for network analytics. Results show that resource utilization improves with the proposed algorithms. It has been shown that algorithm2 and algorithm1 achieve 27% and 18% traffic gains respectively over the alternate routing. |
| format | Article |
| id | doaj-art-44dec55e68804b22b159006cd7ae91d7 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-44dec55e68804b22b159006cd7ae91d72025-08-20T02:52:09ZengElsevierResults in Engineering2590-12302024-12-012410317610.1016/j.rineng.2024.103176Resource learning aware algorithm for route prediction and resources allocation in elastic optical networks with network analyticsAkhtar Nawaz Khan0Hassan Yousif Ahmed1Medien Zeghid2Department of Electrical Engineering, University of Engineering & Technology, Peshawar, PakistanDepartment of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Wadi Alddawasir 11991, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia; Corresponding author.We have proposed two algorithms in this paper for dynamic traffic in elastic optical networks (EON). Algorithm1 considers the spatial access blocking probability metric (SABPM) during dynamic operation and route traffic to a path with lower SABPM. Therefore, it considers a path with more contiguous blocks to support heterogeneous bandwidths (BW) and modulation schemes. Algorithm1 keeps a balance of fragmentation on available routes. Algorithm2 makes routing decision for dynamic traffic based on the maximum idle slots as well as minimum SABPM. Therefore, it selects a less fragmented path with more number of available contiguous blocks to support heterogeneous BW. Algorithm2 balances the path utilization and avoids over utilization of congested route. We have evaluated the performance of both algorithms in terms of spatial external fragmentation, contiguity ratio, contiguous aligned spectrum ratio, SABPM, and BW blocking probability (BwBP). Python with seaborn, pandas, and other libraries are used for network analytics. Results show that resource utilization improves with the proposed algorithms. It has been shown that algorithm2 and algorithm1 achieve 27% and 18% traffic gains respectively over the alternate routing.http://www.sciencedirect.com/science/article/pii/S2590123024014312Elastic optical networksContiguity constraintContinuity constraintBandwidth blocking probability, fragmentation, and, network analytics |
| spellingShingle | Akhtar Nawaz Khan Hassan Yousif Ahmed Medien Zeghid Resource learning aware algorithm for route prediction and resources allocation in elastic optical networks with network analytics Results in Engineering Elastic optical networks Contiguity constraint Continuity constraint Bandwidth blocking probability, fragmentation, and, network analytics |
| title | Resource learning aware algorithm for route prediction and resources allocation in elastic optical networks with network analytics |
| title_full | Resource learning aware algorithm for route prediction and resources allocation in elastic optical networks with network analytics |
| title_fullStr | Resource learning aware algorithm for route prediction and resources allocation in elastic optical networks with network analytics |
| title_full_unstemmed | Resource learning aware algorithm for route prediction and resources allocation in elastic optical networks with network analytics |
| title_short | Resource learning aware algorithm for route prediction and resources allocation in elastic optical networks with network analytics |
| title_sort | resource learning aware algorithm for route prediction and resources allocation in elastic optical networks with network analytics |
| topic | Elastic optical networks Contiguity constraint Continuity constraint Bandwidth blocking probability, fragmentation, and, network analytics |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024014312 |
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