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Journal of Optical Communications and Networking
Vol. 13,
Issue 11,
pp. 253-265
(2021)
•https://doi.org/10.1364/JOCN.431225

Reconfiguring multicast sessions in elastic optical networks adaptively with graph-aware deep reinforcement learning

Xiaojian Tian, Baojia Li, Rentao Gu, and Zuqing Zhu

Author Information

Author Affiliations

Xiaojian Tian,¹Baojia Li,¹Rentao Gu,²and Zuqing Zhu^1,^*

¹School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, China

²School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

^*Corresponding author:zqzhu@ieee.org

ORCID

ZuqingZhu

https://orcid.org/0000-0002-4251-788X

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Xiaojian Tian, Baojia Li, Rentao Gu, and Zuqing Zhu, "Reconfiguring multicast sessions in elastic optical networks adaptively with graph-aware deep reinforcement learning," J. Opt. Commun. Netw.13, 253-265 (2021)
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About this Article
History
- Original Manuscript: May 11, 2021
- Revised Manuscript: June 26, 2021
- Manuscript Accepted: July 7, 2021
- Published: July 29, 2021

Abstract

With the fast deployment of datacenters (DCs), bandwidth-intensive multicast services are becoming more and more popular in metro and wide-area networks, to support dynamic applications such as DC synchronization and backup. Hence, this work studies the problem of how to formulate and reconfigure multicast sessions in an elastic optical network (EON) dynamically. We propose a deep reinforcement learning (DRL) model based on graph neural networks to solve the sub-problem of multicast session selection in a more universal and adaptive manner. The DRL model abstracts topology information of the EON and the current provisioning scheme of a multicast session as graph-structured data, and analyzes the data to intelligently determine whether the session should be selected for reconfiguration. We evaluate our proposal with extensive simulations that consider different EON topologies, and the results confirm its effectiveness and universality. Specifically, the results show that it can balance the trade-off between the number of reconfiguration operations and blocking performance much better than existing algorithms, and the DRL model trained in one EON topology can easily adapt to solve the problem of dynamic multicast session reconfiguration in other topologies, without being redesigned or retrained.

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Journal of Optical Communications and Networking

Andrew Lord, Editor-in-Chief

Table 2.

Average Running Time per Multicast Reconfiguration (Seconds)

Topology	NSFNET	USB	EUB	RT
DRL-P	0.1943	0.2494	0.2383	0.2908
QTS-P	0.2964	0.3941	0.4040	0.3986
DTS-P	0.0693	0.0900	0.1016	0.0867
DRL-F	0.2104	0.2823	0.3022	0.3110
QTS-F	0.3620	0.4648	0.4927	0.4448
DTS-F	0.1995	0.2723	0.2882	0.2687

Tables (4)

Algorithm 1.

Dynamic Provisioning of Multicast Sessions

1.while the EON is operationaldo

2.for each newly arrived session

{MR}_{i} (s, D, b, t)

3. try to set up a logic light-tree for it with OL-M-SFMOR

4.if the light-tree cannot be establishedthen

5. mark

{MR}_{i}

as blocked

6.for each existing session

{MR}_{j} (s, D, b, t)

7.if

t

has been expiredthen

8. remove

{MR}_{j}

and free its resources

9.if

D

has changedthen

10. update its logic light-tree with OL-M-SFMOR

11.if it is time to reconfigure multicast sessionsthen

12. select existing multicast sessions to reconfigure

13. reconfigure the selected multicast sessions

Algorithm 2.

Training Process of a Thread

T = 0

2.while

T < T_{max}

3.if it is the time to reconfigure multicast sessionsthen

4.for each in-service multicast session

{MR}_{i}

5. get state

S_{i}

{MR}_{i}

6. put

S_{i}

into the A-GNN to get an action

a_{i}

7. apply

a_{i}

to the EON environment

8. calculate reward

r_{i}

9. push

{S_{i}, a_{i}, r_{i}}

to experience buffer

10.if experience buffer is fullthen

11. reset the gradients as zero

12. calculate the loss with A-GNN and C-GNN using the training samples in experience buffer

13. get the gradients with the loss

14. send the gradients to the global GNN

15. update the parameters of A-GNN and C-GNN according to the feedback from the global GNN

16. empty the experience buffer

17.

T = T + 1

Table 1.

Average Running Time of Offline Training (Seconds)

Topology	NSFNET	USB	EUB	RT
DRL-P	25,167.35	34,698.01	34,944.14	32,342.45
DRL-F	31,541.22	40,423.58	45,508.86	36,843.80

Table 2.

Average Running Time per Multicast Reconfiguration (Seconds)

Topology	NSFNET	USB	EUB	RT
DRL-P	0.1943	0.2494	0.2383	0.2908
QTS-P	0.2964	0.3941	0.4040	0.3986
DTS-P	0.0693	0.0900	0.1016	0.0867
DRL-F	0.2104	0.2823	0.3022	0.3110
QTS-F	0.3620	0.4648	0.4927	0.4448
DTS-F	0.1995	0.2723	0.2882	0.2687

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Reconfiguring multicast sessions in elastic optical networks adaptively with graph-aware deep reinforcement learning

Author Affiliations

ORCID

Abstract

Cited By

Figures (14)

Tables (4)

Equations (7)

Journal of Optical Communications and Networking

Algorithm 1.

Dynamic Provisioning of Multicast Sessions

Algorithm 2.

Training Process of a Thread

Table 1.

Average Running Time of Offline Training (Seconds)

Table 2.

Average Running Time per Multicast Reconfiguration (Seconds)

Tables (4)

Algorithm 1.

Algorithm 2.

Table 1.

Table 2.

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