Centralized and distributed approaches to control optical point-to-multipoint systems near-real-time

H. Shakespear-Miles; Q. Lin; S. Barzegar; M. Ruiz; X. Chen; L. Velasco

doi:10.1364/JOCN.516137

Journal of Optical Communications and Networking
Vol. 16,
Issue 5,
pp. 565-576
(2024)
•https://doi.org/10.1364/JOCN.516137

Centralized and distributed approaches to control optical point-to-multipoint systems near-real-time

H. Shakespear-Miles, Q. Lin, S. Barzegar, M. Ruiz, X. Chen, and L. Velasco

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H. Shakespear-Miles, Q. Lin, S. Barzegar, M. Ruiz, X. Chen, and L. Velasco, "Centralized and distributed approaches to control optical point-to-multipoint systems near-real-time," J. Opt. Commun. Netw. 16, 565-576 (2024)

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Abstract

Optical point-to-multipoint (P2MP) connectivity based on digital subcarrier multiplexing (DSCM) has been shown as a solution for the metro-access segment that is able to reduce capital and operational costs and support the capacity and high dynamicity needs of future 6G services. To achieve maximum performance, activation and deactivation of subcarriers must be done near-real-time to provide just the capacity needed to support the input traffic. In this paper, we investigate the applicability of various approaches capable of supporting the near-real-time operation requirement. Starting from the centralized approach that can be carried out on the centralized software-defined networking (SDN) controller, we also explore distributed approaches that might relieve the SDN controller from near-real-time operation. In particular, we explore the performance of deploying a multiagent system (MAS), where intelligent agents run on top of the nodes in the P2MP tree and communicate among them. Illustrative results show that the distributed approaches can achieve a performance close to that of the centralized one, while reducing communication needs. Results also show the importance of traffic/capacity prediction to anticipate the activation of subcarriers.

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Sets and parameters
$T$	Set of Txs, index $t$
SC	Set of available SCs, index sc
$S$	Set of slots, index $s$ ; each slot is a set of SCs
$k_{SC}$	Capacity of a single SC
$v_{t}$	Traffic observed for Tx $t$
$k_{t}$	Capacity required for Tx $t$
$k_{s}$	Capacity of slot $s$
$o_{t}$	Spectrum operation (increase, decrease, none) for Tx $t$
$l_{ts}$	Traffic loss in the case of selecting slot $s$ for Tx $t$ ( $l_{ts} = v_{t} - k_{s}$ , if $v_{t} > k_{s}$ , 0 otherwise)
$δ_{ts}$	Equal to 1 if slot $s$ is a candidate for Tx $t$ , 0 otherwise
$γ_{ts}$	Equal to 1 if slot $s$ is currently allocated for Tx $t$ , 0 otherwise
$δ_{ssc}$	Equal to 1 if SC sc is in slot $s$ , 0 otherwise
$σ_{s}$	Number of SCs in slot $s$
$α_{i}$	Coefficients of the objective function
$M_{(\cdot)}$	Number of messages exchanged
Decision variables
$x_{ts}$	Binary, equal to 1 if slot $s$ is assigned to Tx $t$ ; 0 otherwise
$r_{t}$	Binary, spectral allocation for Tx $t$ has changed

INPUT: SC, numTx		OUTPUT: $[x_{ts}, r_{t}]$
1: $T \leftarrow {}; S \leftarrow {}$
2: while $\| T \| < n u m T x$ do
3: $t \leftarrow r e c e i v e R q$ ()
4: $T \leftarrow T \cup {t}$
5: if $t . o = 0$ then
6: $S_{t} \leftarrow p r e c o m p u t e$ (SC, { $t$ .s, $t . s ≪ 1$ , $t . s ≫ 1}$ )
7: else if $t . o = - 1$ then
8: $S_{t} \leftarrow p r e c o m p u t e$ (SC, { $t$ .s \ { $t$ .s[0]}, $t$ .sc \ { $t$ .sc[-1]})
9: else // $t . o = + 1$
10: $S_{t} \leftarrow p r e c o m p u t e$ (SC, { $t$ .s, $t$ .s U { ${S C}^{-}$ }, $t$ .s U { ${S C}^{+}$ })
11: $S \leftarrow S \cup S_{t}$
12: return solveSA ( $S$ , $T$ )

$F_{t}$	Strategy space for Tx $t$ , index $f$
$S_{tf}$	Slot to be allocated if strategy $f$ of Tx $t$ is selected
$n_{f}$	Number of contiguous SCs in the slot of strategy $f$
$p_{t}$	Probability distribution for Tx $t$
$u_{tf}$ (·)	Utility function for strategy $f$ of Tx $t$
$L_{tf}$	Likelihood of slot $S_{tf}$ being accepted
$δ_{tfsc}$	Equal to 1 if SC $sc \in S_{tf}$ , 0 otherwise

INPUT: SC, numTx	OUTPUT: $s_{t}$
1: $T \leftarrow {}$
2: while $\| T \| < n u m T x$ do
3: $t \leftarrow r e c e i v e R q$ ()
4: $T \leftarrow T \cup {t}$
5: if $t . o = 0$ then
6: $F_{t} \leftarrow c o m p S t r a t e g y$ (SC, { $t$ .s, $t . s ≪ 1$ , $t . s ≫ 1}$ )
7: else if $t . o = - 1$ then
8: $F_{t} \leftarrow c o m p S t r a t e g y$ (SC, { $t$ .s \ { $t$ .s[0]}, $t$ .sc \ { $t$ .sc[ $- 1$ ]})
9: else // $t . o = + 1$
10: $F_{t} \leftarrow c o m p S t r a t e g y$ (SC, $t$ .s, $t$ .s ∪ { ${S C}^{-}$ }, $t$ .s ∪ { ${S C}^{+}$ })
11: calculate $s u p (u_{tf})$ and $i n f (u_{tf})$ for each $f$ in $F_{t}$
12: while $\exists f, f^{'} \in F_{t} \| s u p (u_{t} f) < i n f (u_{t f^{'}})$ do
13: delete $f$ from $F_{t}, s u p (u_{t} f) < i n f (u_{t f^{'}}), f^{'} \in F_{t}$
14: recalculate $s u p (u_{tf})$ and $i n f (u_{tf})$ for $\forall t, f$
15: initialize $p_{t}$ for $t \in T$
16: while $\frac{\| u_{t f} - \bar{u_{t}} \|}{\| \bar{u_{t}} \|} >$ th, $\forall t, f$ , where ${\bar{u}}_{t} = \frac{1}{\| F_{t} \|} \cdot \sum_{f \in F_{t}} u_{t f}$ do
17: $p^{*} \leftarrow c a l c u l a t e P r o b (p_{tf})$
18: $f \leftarrow s e l e c t S t r a t e g y A t R a n d o m (F_{t})$
19: return $s_{f}$

		Distributed
	Centralized	MSG	DD-MAS	MARL
Traffic/capacity prediction	Threshold	Threshold	Threshold	DRL
SC management	ILP	Mixed strategy	Deter-ministic	Deter-ministic
Inter-agent communication	–	Traffic prediction, spectrum	Spectrum, coord.	Spectrum, coord.

	Centralized	MSG	DD-MAS	MARL
w/o sp. planning	3863.8	4969.9	3863.8	3863.8
w/ sp. planning	0	144.3	0	0

	5 Leaf Nodes				6 Leaf Nodes				7 Leaf Nodes
Load	Cent.	MSG	DD-MAS	MARL	Cent.	MSG	DD-MAS	MARL	Cent.	MSG	DD-MAS	MARL
70	0	8437	0	0	0	6893	2	3	0	4827	1	3
75	0	9161	0	0	0	7927	3	4	0	2977	2	6
80	3	7303	2	2	4	6452	4	4	2	2989	3	4
85	2	6474	3	4	4	5140	3	4	0	2260	2	5
90	2	6266	2	2	4	4790	3	3	0	2663	2	3
95	2	6244	2	2	4	4147	5	5	0	1269	3	4

Approach	Advantages	Drawbacks
Centralized	• Optimal SC allocation provides good performance	• Requires strict synchronization since leaf nodes need to
	with minimal overprovisioning	collect traffic measurements and send them to the
		centralized module making decisions
Mixed-Strategy	• Distributed decision making to liberate the SDN	• Requires strict synchronization of the leaf nodes since
Gaming	controller from near-real-time operation	they need to share traffic and spectral allocation
		• Random loss even under low load and a huge number of
		spectrum operations (no cooperation among the leaves)
		• Number of messages increases quadratically with the
		number of agents in the connection
Collaborative	• Distributed decision making to liberate the SDN	• Requires communication and collaboration among the leaf
Multi-Agent	controller from near-real-time operation	nodes
Systems	• MARL can anticipate better for traffic changes	• The performance is (slightly) below the centralized
(DD-MAS/MARL)	• Only spectral allocation is shared, so decisions can	approach
	be made asynchronously, which also reduces the
	number of messages exchanged
	• Few spectrum shifting operations (cooperation)

Abstract

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Equations (12)

Journal of Optical Communications and Networking