Journal of Optical Communications and Networking
Vol. 15,
Issue 8,
pp. 553-568
(2023)
•https://doi.org/10.1364/JOCN.486838

Robust traffic grooming and infrastructure placement in OTN-over-DWDM networks

Dimitrios Michael Manias, Joe Naoum-Sawaya, Abdallah Shami, Abbas Javadtalab, Mahdi Hemmati, and Yuren You

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Dimitrios Michael Manias, Joe Naoum-Sawaya, Abdallah Shami, Abbas Javadtalab, Mahdi Hemmati, and Yuren You, "Robust traffic grooming and infrastructure placement in OTN-over-DWDM networks," J. Opt. Commun. Netw. 15, 553-568 (2023)

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Abstract

The advent of next-generation networks has revolutionized modern networking practices through its improved service capability as well as its numerous emerging use cases. Coupled with the increasing number of connected devices, 5G and beyond (5G$+$) network traffic is expected to be increasingly diverse and high in volume. To address the large amount of data exchanged between the 5G$+$ core and external data networks, optical transport networks (OTNs) with dense wavelength-division multiplexing (DWDM) will be leveraged. In order to prepare for this increase in traffic, network operators (NOs) must develop and expand their existing backbone networks, requiring significant levels of capital expenditures. To this end, the traffic grooming and infrastructure placement problem is critical to supporting NO decisions. The work presented in this paper considers the traffic grooming and infrastructure placement problem for OTN-over-DWDM networks. The dynamicity and diversity of 5G$+$ network traffic are addressed through the use of robust optimization, allowing for increasing levels of solution conservativeness to protect against various levels of demand uncertainty. Furthermore, a robust traffic grooming and infrastructure placement heuristic (RGIP-H) solution capable of addressing the scalability concerns of the optimization problem formulation is presented. The results presented in this work demonstrate how the tuning of the robust parameters affects the cost of the objective function. Additionally, the ability of the robust solution to protect the solution under demand uncertainty is highlighted when the robust and deterministic solutions are compared during parameter deviation trials. Finally, the performance of the RGIP-H is compared to the optimization models when applied to larger network sizes.

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Dimitrios Michael Manias	https://orcid.org/0000-0003-4390-3093
Mahdi Hemmati	https://orcid.org/0000-0002-4226-5221

Symbol	Description
$G$	Granularity
$w_{e}$	Number of wavelengths available on link $e$
$B_{d}$	Bandwidth requirement for demand $d$
$α$	Optical channel weight coefficient
$β$	Optical switch weight coefficient
$ϵ$	Very small positive non-zero number
$y_{i}$	Binary variable denoting if an OTN switch exists at node $i$
$C_{ij}$	Capacity of optical channel with source $i$ and destination $j$ in terms of number of slots
$x_{ij}^{d}$	Fractional flow for demand $d$ across optical channel with source $i$ and destination $j$
$θ_{ij}$	Number of optical channels with source $i$ and destination $j$
$D$	Set of demands
$O$	Set of optical channels
$Φ_{i}$	Set of nodes forming optical channels with node $i$ as the source
$Ψ_{i}$	Set of nodes forming optical channels with node $i$ as the destination
$E$	Set of edges in physical network
$N$	Set of nodes in physical network
$Δ_{e}$	Set of optical channels that use edge $e$
$Γ$	Robust formulation solution conservativeness parameter
${\hat{B}}_{d}$	Deviation of $B_{d}$
$p_{ij}^{d}$	Robust dual variable
$z_{ij}$	Robust dual variable
$s r c (d)$	Source node for demand $d$
$d s t (d)$	Destination node for demand $d$

Parameter	Values
Topology	{6N, NSFNET}
Number of demands	{50, 250}
$α$	{0, 0.1, 1}
$β$	{1, 2}
$Γ$	${0.1, 0.3, 0.5} \cdot ‖ D ‖$
${\hat{B}}_{d}$	${0.1, 0.3, 0.5} \cdot B_{d}$

Symbol	Description
$G$	Granularity
$w_{e}$	Number of wavelengths available on link $e$
$B_{d}$	Bandwidth requirement for demand $d$
$α$	Optical channel weight coefficient
$β$	Optical switch weight coefficient
$ϵ$	Very small positive non-zero number
$y_{i}$	Binary variable denoting if an OTN switch exists at node $i$
$C_{ij}$	Capacity of optical channel with source $i$ and destination $j$ in terms of number of slots
$x_{ij}^{d}$	Fractional flow for demand $d$ across optical channel with source $i$ and destination $j$
$θ_{ij}$	Number of optical channels with source $i$ and destination $j$
$D$	Set of demands
$O$	Set of optical channels
$Φ_{i}$	Set of nodes forming optical channels with node $i$ as the source
$Ψ_{i}$	Set of nodes forming optical channels with node $i$ as the destination
$E$	Set of edges in physical network
$N$	Set of nodes in physical network
$Δ_{e}$	Set of optical channels that use edge $e$
$Γ$	Robust formulation solution conservativeness parameter
${\hat{B}}_{d}$	Deviation of $B_{d}$
$p_{ij}^{d}$	Robust dual variable
$z_{ij}$	Robust dual variable
$s r c (d)$	Source node for demand $d$
$d s t (d)$	Destination node for demand $d$

Parameter	Values
Topology	{6N, NSFNET}
Number of demands	{50, 250}
$α$	{0, 0.1, 1}
$β$	{1, 2}
$Γ$	${0.1, 0.3, 0.5} \cdot ‖ D ‖$
${\hat{B}}_{d}$	${0.1, 0.3, 0.5} \cdot B_{d}$

Robust traffic grooming and infrastructure placement in OTN-over-DWDM networks

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Abstract

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Equations (21)

Journal of Optical Communications and Networking