Bandwidth Reservation for Tenants in Reconfigurable Optical OFDM Datacenter Networks
The optical datacenter networks need periodical reconfiguration in response to traffic change. Before the networks reconfigure, the tenant requests are given and should be served within fixed transfer time and spectrum capacity. In this paper, the planning problem of serving the requests in optical...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
|
| Series: | IEEE Photonics Journal |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8418848/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849417127140786176 |
|---|---|
| author | Aijun Liu Yongmei Sun Yuefeng Ji |
| author_facet | Aijun Liu Yongmei Sun Yuefeng Ji |
| author_sort | Aijun Liu |
| collection | DOAJ |
| description | The optical datacenter networks need periodical reconfiguration in response to traffic change. Before the networks reconfigure, the tenant requests are given and should be served within fixed transfer time and spectrum capacity. In this paper, the planning problem of serving the requests in optical orthogonal frequency division multiplexing datacenter networks is investigated. We introduce the knapsack-based spectrum and time allocation (KSTA) problem. The objective of this paper is to maximize the network throughput. We formulate the KSTA problem as an integer linear programming (ILP) model. However, ILP cannot find the optimal solution for large input requests within shorter time. To solve the problem, three fast heuristic algorithms, i.e., the most spectrum first, the most time first, and the most data volume first, are proposed to achieve suboptimal solutions. Furthermore, the simulated annealing (SA) algorithm is employed to yield a better suboptimal solution. The simulation results indicate that ILP provides an optimal solution for small input requests, whereas the three heuristic algorithms and SA can yield suboptimal solutions for large input requests. The results also show that the suboptimal solution to SA is better than those provided by the three heuristic algorithms. |
| format | Article |
| id | doaj-art-55f4e5c2d3724e5fa25f715fa74a9eee |
| institution | Kabale University |
| issn | 1943-0655 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-55f4e5c2d3724e5fa25f715fa74a9eee2025-08-20T03:32:57ZengIEEEIEEE Photonics Journal1943-06552018-01-0110511610.1109/JPHOT.2018.28592758418848Bandwidth Reservation for Tenants in Reconfigurable Optical OFDM Datacenter NetworksAijun Liu0https://orcid.org/0000-0003-4941-7360Yongmei Sun1https://orcid.org/0000-0002-0235-2507Yuefeng Ji2https://orcid.org/0000-0002-6618-272XState Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, ChinaThe optical datacenter networks need periodical reconfiguration in response to traffic change. Before the networks reconfigure, the tenant requests are given and should be served within fixed transfer time and spectrum capacity. In this paper, the planning problem of serving the requests in optical orthogonal frequency division multiplexing datacenter networks is investigated. We introduce the knapsack-based spectrum and time allocation (KSTA) problem. The objective of this paper is to maximize the network throughput. We formulate the KSTA problem as an integer linear programming (ILP) model. However, ILP cannot find the optimal solution for large input requests within shorter time. To solve the problem, three fast heuristic algorithms, i.e., the most spectrum first, the most time first, and the most data volume first, are proposed to achieve suboptimal solutions. Furthermore, the simulated annealing (SA) algorithm is employed to yield a better suboptimal solution. The simulation results indicate that ILP provides an optimal solution for small input requests, whereas the three heuristic algorithms and SA can yield suboptimal solutions for large input requests. The results also show that the suboptimal solution to SA is better than those provided by the three heuristic algorithms.https://ieeexplore.ieee.org/document/8418848/Optical datacenter networksreconfigurationknapsack-based spectrum and time allocationorthogonal frequency division multiplexing. |
| spellingShingle | Aijun Liu Yongmei Sun Yuefeng Ji Bandwidth Reservation for Tenants in Reconfigurable Optical OFDM Datacenter Networks IEEE Photonics Journal Optical datacenter networks reconfiguration knapsack-based spectrum and time allocation orthogonal frequency division multiplexing. |
| title | Bandwidth Reservation for Tenants in Reconfigurable Optical OFDM Datacenter Networks |
| title_full | Bandwidth Reservation for Tenants in Reconfigurable Optical OFDM Datacenter Networks |
| title_fullStr | Bandwidth Reservation for Tenants in Reconfigurable Optical OFDM Datacenter Networks |
| title_full_unstemmed | Bandwidth Reservation for Tenants in Reconfigurable Optical OFDM Datacenter Networks |
| title_short | Bandwidth Reservation for Tenants in Reconfigurable Optical OFDM Datacenter Networks |
| title_sort | bandwidth reservation for tenants in reconfigurable optical ofdm datacenter networks |
| topic | Optical datacenter networks reconfiguration knapsack-based spectrum and time allocation orthogonal frequency division multiplexing. |
| url | https://ieeexplore.ieee.org/document/8418848/ |
| work_keys_str_mv | AT aijunliu bandwidthreservationfortenantsinreconfigurableopticalofdmdatacenternetworks AT yongmeisun bandwidthreservationfortenantsinreconfigurableopticalofdmdatacenternetworks AT yuefengji bandwidthreservationfortenantsinreconfigurableopticalofdmdatacenternetworks |