Dimensioning of the Store-and-Transfer WDM Network With Limited Node Storage Under the Sliding Scheduled Traffic Model

Da Feng; Weiqiang Sun; Xiaojian Zhang; Weisheng Hu

doi:10.1364/JOCN.9.000275

Journal of Optical Communications and Networking
Vol. 9,
Issue 4,
pp. 275-290
(2017)
•https://doi.org/10.1364/JOCN.9.000275

Dimensioning of the Store-and-Transfer WDM Network With Limited Node Storage Under the Sliding Scheduled Traffic Model

Da Feng, Weiqiang Sun, Xiaojian Zhang, and Weisheng Hu

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Da Feng, Weiqiang Sun, Xiaojian Zhang, and Weisheng Hu, "Dimensioning of the Store-and-Transfer WDM Network With Limited Node Storage Under the Sliding Scheduled Traffic Model," J. Opt. Commun. Netw. 9, 275-290 (2017)

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Abstract

The so-called store-and-transfer WDM network (STWN) can store data in source storage and provision lightpaths at an optimal time when wavelengths are clear of conflicts. Consequently, blocking of requests can be reduced and resource utilization of the network can be improved. In this work, we investigate dimensioning of STWN and propose a two-step method to jointly determine the number of wavelengths and storage size required to satisfy the demand, which is given as a load matrix with a deadline, blocking rate, and wavelength utilization. The method models the STWN as a TDM network and first obtains the required storage size by maximizing the number of fixed time slots in each period, then calculates the required number of wavelengths with the number of time slots. Numerical results show the following: high load between far apart source and destination nodes has significant impact on the required wavelengths and storage. For instance, in a 24-node topology, 20% more wavelengths and storage may be required to satisfy a biased load matrix than a randomly generated one. When calculating the required number of wavelengths, our method outperforms traditional routing and wavelength assignment (RWA), because the TDM-based model supports fractional load. In the 24-node topology, 18% more wavelengths may be required by RWA than by our method. By installing a moderate amount of storage, wavelength utilization can be effectively improved. With number of wavelengths and storage size equaling 29 and 274, in the 24-node topology, utilization can reach 0.9. We also find wavelength is equivalent with storage regarding capacity of a STWN. For instance, with number of wavelengths and storage size equaling 114 and 131, in the 24-node topology, the blocking rate is the same as with number of wavelengths and storage size equaling 29 and 270.

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Category	Motivation	Related Work
Category	Motivation	Ref.	Summary
Traffic	A new traffic model is introduced to analyze scheduled traffic in WDM networks.	[4]	Gives a survey of advance reservation, which requires a service time and a transfer window longer than the service time. The sliding scheduled traffic model has a flexible window with a specified arrival time.
		[6]	Discusses the sliding scheduled traffic model in wavelength selective WDM networks. Each request can use multiple wavelengths, but one common path is used for one request to prevent unequal distance of lightpaths.
		[7]	Discusses the sliding scheduled traffic model in wavelength convertible WDM networks. The details are similar to those given in [6].
		[8]	Formulates ILP under the sliding scheduled traffic model and then proposes a Lagrangean-relaxation-based integrated approach to solve it efficiently.
		[9]	Discusses the sliding scheduled traffic model and partitions large data transfer requests into smaller ones. Smaller requests can use separate paths, wavelengths, and transfer begin times. But one common path is used for one request.
		[10]	Proposes time-slotted wavelength switching, which uses continuous time slots over multiple wavelengths.
Performance	Engineering of algorithms to improve utilization of network or reduce congestion (blocking).	[11]	Proposes a graph transformation approach to schedule data transfers in a network with time varying link capacity, node storage capacity, and their cost.
		[12]	Proposes end-to-end transfer, end-to-end scheduled transfer, and store-and-forward transfer with assisting intermediate storage nodes. The already paid for off-peak capacity can be fully utilized with the proposed algorithms.
		[13]	Proposes an algorithm to cooperate transfers globally to meet deadlines and reduce congestion of the network. Both the transfer rate and the path can vary at each time interval.
		[14]	Proposes a heuristic RWTA algorithm with three steps, routing, wavelength assignment, and time-slot assignment. The time slots can be non-continuous within each frame, but frames are continuous to ensure constant bandwidth. The time slots are shifted over each link due to transmission delay. One common path is used for one request.
		[15]	Proposes a time-shifted multilayer graph for delay tolerant data transfers. The auxiliary graph used includes both spatial and temporal links. Both edge storage and intermediate storage are discussed.
		[16]	Proposes a store wait forward algorithm for large data transfers, and it is based on a time-varying graph.
Resources	Planning of network to accept a given traffic load with expected performance.	[17]	Proposes to use the probability of a request being blocked before a given time as a performance metric to dimension WDM link capacity. Uses a transient solution of the Markov chain.
		[18]	Investigates dimensioning of the link capacity for elastic traffic with a deadline. Uses M/M/1/N-RPS to model capacity sharing among multiple malleable requests, which can transfer with a variable rate.
		[19]	Proposes an ILP formulation for routing, modulation format, and spectrum assignment. A traffic matrix of the connection rate is given, and a modulation format is selected for each alternate path. A two-step heuristic for network planning is proposed, which first calculates routing and modulation format, then assigns spectrum.

Metrics	Description
turnout	$\frac{total transferred data}{complete time}$
delay	The mean time requests spend inside the system
blocking	$b l k$ , $\frac{total rejected data}{total arrived data}$
utilization	Load of a wavelength

Names	Description
$G$	Digraph, given in Section III.B.
$E$	Links of $G$ .
$M$	Number of time slots in a TDM frame.
$T$	Duration of a TDM frame.
$Δ$	Duration of a time slot, constant, $\frac{T}{M}$ .
$\hat{W}$	Number of wavelengths of $G$ .
$S D$	Source–destination (s-d) pair set and all members are associated with nonzero weights, which are the elements of the integer matrix.
$\hat{N}$	Number of all s-d pairs with nonzero weights, $\| S D \|$ .
${\overset{ˇ}{P}}_{i, j, e}$	Link relations of s-d pairs and equals 1 if path $j$ of s-d pair $i$ contains link $e$ .
${\hat{Q}}_{i}$	Number of paths (KSPs) of s-d pair $i$ .
${\hat{U}}_{i}$	Weight of s-d pair $i$ .
$\hat{T}$	Total weight of s-d pairs, $\sum_{i \in [0, \hat{N})} {\hat{U}}_{i}$ .
${\overset{ˇ}{f}}_{t, w, i, j}$	The channels usage of the TDM routing and wavelength and time-slot assignment, and equals 1 if path $j$ of s-d pair $i$ is assigned wavelength $w$ in time slot $t$ .
$ρ_{i} = \frac{{\hat{U}}_{i} \cdot λ}{μ}$	Load applied to s-d pair $i$ . $λ$ is the base arrival rate of the load matrix and $μ$ is the base service rate. The demand is given as $[load matrix] = base load \cdot [integer matrix]$ and the elements of [integer matrix] ( $M x$ ) are mutually prime. If the base load is high, we can build multiple independent systems, each with a lower base load.

Category	Motivation	Related Work
Category	Motivation	Ref.	Summary
Traffic	A new traffic model is introduced to analyze scheduled traffic in WDM networks.	[4]	Gives a survey of advance reservation, which requires a service time and a transfer window longer than the service time. The sliding scheduled traffic model has a flexible window with a specified arrival time.
		[6]	Discusses the sliding scheduled traffic model in wavelength selective WDM networks. Each request can use multiple wavelengths, but one common path is used for one request to prevent unequal distance of lightpaths.
		[7]	Discusses the sliding scheduled traffic model in wavelength convertible WDM networks. The details are similar to those given in [6].
		[8]	Formulates ILP under the sliding scheduled traffic model and then proposes a Lagrangean-relaxation-based integrated approach to solve it efficiently.
		[9]	Discusses the sliding scheduled traffic model and partitions large data transfer requests into smaller ones. Smaller requests can use separate paths, wavelengths, and transfer begin times. But one common path is used for one request.
		[10]	Proposes time-slotted wavelength switching, which uses continuous time slots over multiple wavelengths.
Performance	Engineering of algorithms to improve utilization of network or reduce congestion (blocking).	[11]	Proposes a graph transformation approach to schedule data transfers in a network with time varying link capacity, node storage capacity, and their cost.
		[12]	Proposes end-to-end transfer, end-to-end scheduled transfer, and store-and-forward transfer with assisting intermediate storage nodes. The already paid for off-peak capacity can be fully utilized with the proposed algorithms.
		[13]	Proposes an algorithm to cooperate transfers globally to meet deadlines and reduce congestion of the network. Both the transfer rate and the path can vary at each time interval.
		[14]	Proposes a heuristic RWTA algorithm with three steps, routing, wavelength assignment, and time-slot assignment. The time slots can be non-continuous within each frame, but frames are continuous to ensure constant bandwidth. The time slots are shifted over each link due to transmission delay. One common path is used for one request.
		[15]	Proposes a time-shifted multilayer graph for delay tolerant data transfers. The auxiliary graph used includes both spatial and temporal links. Both edge storage and intermediate storage are discussed.
		[16]	Proposes a store wait forward algorithm for large data transfers, and it is based on a time-varying graph.
Resources	Planning of network to accept a given traffic load with expected performance.	[17]	Proposes to use the probability of a request being blocked before a given time as a performance metric to dimension WDM link capacity. Uses a transient solution of the Markov chain.
		[18]	Investigates dimensioning of the link capacity for elastic traffic with a deadline. Uses M/M/1/N-RPS to model capacity sharing among multiple malleable requests, which can transfer with a variable rate.
		[19]	Proposes an ILP formulation for routing, modulation format, and spectrum assignment. A traffic matrix of the connection rate is given, and a modulation format is selected for each alternate path. A two-step heuristic for network planning is proposed, which first calculates routing and modulation format, then assigns spectrum.

Metrics	Description
turnout	$\frac{total transferred data}{complete time}$
delay	The mean time requests spend inside the system
blocking	$b l k$ , $\frac{total rejected data}{total arrived data}$
utilization	Load of a wavelength

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Journal of Optical Communications and Networking