Latency-Aware CU Placement/Handover in Dynamic WDM Access-Aggregation Networks

Francesco Musumeci; Omran Ayoub; Monica Magoni; Massimo Tornatore

doi:10.1364/JOCN.11.000B71

Journal of Optical Communications and Networking
Vol. 11,
Issue 4,
pp. B71-B82
(2019)
•https://doi.org/10.1364/JOCN.11.000B71

Latency-Aware CU Placement/Handover in Dynamic WDM Access-Aggregation Networks

Francesco Musumeci, Omran Ayoub, Monica Magoni, and Massimo Tornatore

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Francesco Musumeci, Omran Ayoub, Monica Magoni, and Massimo Tornatore, "Latency-Aware CU Placement/Handover in Dynamic WDM Access-Aggregation Networks," J. Opt. Commun. Netw. 11, B71-B82 (2019)

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Abstract

Centralized radio access networks (C-RANs) have recently been proposed to cope with the unprecedented requirements of future 5G services, in terms of network capacity, latency, service availability, and network coordination. C-RANs are based on the idea of separating baseband signal processing from radio units (RUs), namely, antenna sites in the mobile network, in such a way that baseband processing can eventually be concentrated in common locations, the central units (CUs), which can be shared among several RUs. Although C-RAN brings significant CapEx/OpEx savings, it also requires transport of high-capacity and low-latency fronthaul traffic. Hence, due to the highly dynamic nature of mobile traffic, proper placement of CUs in the optical access-aggregation network should adapt to spatio–temporal traffic variation while maintaining a high degree of RAN centralization and low service blocking. In this paper, we provide an adaptive latency-aware algorithm for dynamic CU placement in optical access-aggregation networks, which targets the minimization of the number of CUs and also preforms grooming, routing, and wavelength assignment (GRWA) for mobile network traffic demands. When given the possibility to perform CU handover, i.e., to move CUs even when they are active, our algorithm, also in high load situations, provides a low number of CUs compared with fixed CU placement and keeps the blocking probability within an acceptable range.

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Variable	Description
$E$	Set of bidirectional optical fiber links
$N$	Set of network nodes
$P$	Set of active pools
$L$	Set of active lightpaths
$L_{f, x}$	Set of fronthaul lightpaths used from the RU in node $x$
$L_{b, y}$	Set of backhaul lightpaths used from the CU in node $y$
$d$	Incoming traffic demand
$c_{d}$	Source CS for demand $d$
$b_{d}$	Required backhaul traffic for demand $d$
$f_{d}$	Required fronthaul traffic for demand $d$
$F$	Fronthaul-versus-backhaul scaling factor ( $f_{d} = F \cdot b_{d}$ )
$t_{d}$	Duration of demand $d$
$X_{pools, d}$	Binary variable, equal to 1 if $d$ originates from an RU with no associated CU
$N_{lightpaths, d}$	Number of newly deployed lightpaths used to accommodate demand $d$
$C_{d, n}$	Cost of locating the CU for demand $d$ at node $n$
$Z$	Current list of candidate CU nodes $z$ , ordered with decreasing value of $C_{d, z}$
handover	Boolean, it is TRUE if a CU handover is necessary to accommodate current demand $d$
$k$	Number of shortest paths used in the Yen algorithm
$K$	Current list of candidate GRWA solutions $g$

DCPH Algorithm	Fronthaul Transport Architecture	CU Handover	Traffic Bifurcation
MaxC-h	OTN w/o grooming	Unconstrained	Allowed
Overlay	Overlay w/o grooming	Unconstrained	Allowed
$T$ -constrained	OTN w/o grooming	Allowed after $T$ seconds from previous handover	Allowed
Non-bifurcated	OTN w/o grooming	Unconstrained	Not allowed

Parameter	Value
Number of macro CSs	50
Number of COs	30
Number of small cells per macro CS	10
Macro CS density	$0.4 {km}^{- 2}$
Number of wavelengths per link	8
Wavelength capacity	100 Gbit/s
Required backhaul traffic per demand	$b_{d} = 300 Mbit / s$
RAN split option	${II}_{d}$ [6] ( $F ≃ 4$ )
Required fronthaul traffic per demand	$f_{d} = 1.2 Gbit / s$
Maximum backhaul traffic per macro CS	60 Gbit/s
Maximum backhaul traffic per SC	20 Gbit/s
Maximum fronthaul latency	100 μs
Number of shortest paths used in the Yen algorithm	$k = 10$
Time-constraint in the $T$ -constrained MaxC-h	$T = 0.5 s$

Variable	Description
$E$	Set of bidirectional optical fiber links
$N$	Set of network nodes
$P$	Set of active pools
$L$	Set of active lightpaths
$L_{f, x}$	Set of fronthaul lightpaths used from the RU in node $x$
$L_{b, y}$	Set of backhaul lightpaths used from the CU in node $y$
$d$	Incoming traffic demand
$c_{d}$	Source CS for demand $d$
$b_{d}$	Required backhaul traffic for demand $d$
$f_{d}$	Required fronthaul traffic for demand $d$
$F$	Fronthaul-versus-backhaul scaling factor ( $f_{d} = F \cdot b_{d}$ )
$t_{d}$	Duration of demand $d$
$X_{pools, d}$	Binary variable, equal to 1 if $d$ originates from an RU with no associated CU
$N_{lightpaths, d}$	Number of newly deployed lightpaths used to accommodate demand $d$
$C_{d, n}$	Cost of locating the CU for demand $d$ at node $n$
$Z$	Current list of candidate CU nodes $z$ , ordered with decreasing value of $C_{d, z}$
handover	Boolean, it is TRUE if a CU handover is necessary to accommodate current demand $d$
$k$	Number of shortest paths used in the Yen algorithm
$K$	Current list of candidate GRWA solutions $g$

DCPH Algorithm	Fronthaul Transport Architecture	CU Handover	Traffic Bifurcation
MaxC-h	OTN w/o grooming	Unconstrained	Allowed
Overlay	Overlay w/o grooming	Unconstrained	Allowed
$T$ -constrained	OTN w/o grooming	Allowed after $T$ seconds from previous handover	Allowed
Non-bifurcated	OTN w/o grooming	Unconstrained	Not allowed

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Journal of Optical Communications and Networking