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Traffic Grooming. Apoorv Nayak Prathyusha Dasari. Agenda. Improved approaches for cost effective traffic grooming in WDM ring networks Motivation Terminology Single hop approach Multi hop approach A Novel Generic Graph Model for Traffic Grooming in Heterogeneous WDM Mesh Networks.

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traffic grooming

Traffic Grooming

Apoorv Nayak

Prathyusha Dasari

agenda
Agenda
  • Improved approaches for cost effective traffic grooming in WDM ring networks
    • Motivation
    • Terminology
    • Single hop approach
    • Multi hop approach
  • A Novel Generic Graph Model for Traffic Grooming in Heterogeneous WDM Mesh Networks
slide3
Motivation
  • With WDM technology we can have dozens of wavelengths on a fiber.
  • Increase in network capacity is accompanied with increase in the electronic multiplexing equipment.
  • Dominant cost is electronics and not fiber.
slide4
Aim
  • Goal is to minimize electronic costs by reducing the number of ADM’s and make efficient use of wavelengths.
  • “Groom” a number of low rate traffic streams onto a higher rate stream and vice versa.
  • Reducing the number of wavelengths
terminology
Terminology
  • SONET
  • ADM
  • WADM
sonet ring
SONET Ring
  • Much of today’s physical layer infrastructure is built around SONET rings.
  • Constructed using fiber (one or two pairs usually used to provide protection) to connect SONET ADM’s.
example
Example

Signal from A split into two; one copy transmitted over the working ring (1) other copy over protection ring (8-7-6).

B selects the best signal.

sonet adm
SONET ADM
  • Add/Drop multiplexer.
  • Each ADM can multiplex multiple lower rate streams to form a higher rate stream OR demultiplex a higher rate stream to several lower rate ones.
  • Employs O-E-O conversion.
  • Works at a particular wavelength.
slide10
WADM
  • Wavelength add/drop multiplexer.
  • Emergence of WDM technology has enabled a single fiber pair to support multiple wavelengths.
  • Since ADM works on a single wavelength, if there are W wavelengths, every node would need N*W ADM’s.
wadm contd
WADM contd
  • But a node may not need to add / drop streams on

every wavelength.

  • WADM’s can add/drop only the wavelengths carrying

traffic to/ from a node.

example of a sonet ring
Example of a SONET ring

OC-48 SONET ring

assumptions
Assumptions
  • Traffic demands are static and known a priori.
  • Traffic is uniform;total bandwidth required is same for any s-d pair.
  • Unidirectional ring considered.
single hop approach
Single hop approach
  • Uses the simulated annealing heuristic.
  • A node with a wavelength-k ADM can communicate directly with all other nodes having wavelength-k ADM.
  • Formation of a wavelength-k logical ring which consists of the subset of N nodes with a wavelength-k ADM.
  • Nodes within a logical ring communicate with each other directly (single hop).
example of single hop approach
Example of single hop approach

Given data

  • Network layout
  • Traffic demand matrix
  • Number of available wavelengths : 2
  • Capacity of each wavelength : OC-3
  • Uniform traffic between any two nodes is OC-1.
network topology
Network Topology

a) Physical Network

b) Traffic on the Network

t1

0

1

0

1

t5

t6

fiber

t2

t4

3

2

3

2

t3

traffic grooming approach1 random
Traffic Grooming Approach1 (Random)

Total number of ADM’s needed = 8

traffic grooming approach 2
Traffic Grooming Approach 2

Total number of ADM’s needed = 7

single hop traffic grooming algorithm
Single hop traffic grooming algorithm

do{

do{

dcost = perturb();

if(∆cost < 0 or (∆cost > 0 and exp(-∆cost/control) > rand [0,1)))

{

accept_change();

chain++;

}

else

reject_change();

} while(chain < ANN_CONST * G)

control = control * DEC_CONST;

} while(control > END)

terminology1
Terminology

Perturb() – Randomly swap positions of two circles in different

wavelengths.

ANN_CONST– Decides how long to run the algorithm before

system reaches equilibrium.

DEC_CONST - How fast to lower the control variable.

G – Grooming ratio (Ratio of the wavelength channel rate to the

lowest traffic rate).

multi hop approach hub based communication
Multi hop approach (Hub based communication)
  • Source and destination on different logical rings.
  • Solution
    • OXC?
  • Still maturing
  • Costly
  • Multiple ADM’s?
  • Relatively inexpensive as compared to OXC
  • More delay and reduced throughput
  • Price-Performance tradeoff.
  • Approach followed in paper?
  • A “hub” node with an ADM for each wavelength.
  • Multiple ADM’s at some nodes.
  • Decide which nodes, how many ADM’s, which

wavelengths.

terminology and assumptions
Terminology and assumptions
  • W - Number of wavelengths.
  • Di - Number of ADM’s in the ith node.
  • G - Grooming ratio (Ratio of the wavelength channel

rate to the lowest traffic rate).

  • tij - Traffic requirement (Number of low rate circuits

between i and j for i-j pair).

  • tij = 1 for uniform traffic.
  • Given data
  • Number of nodes- N
  • Traffic matrix- T
  • Grooming ratio- G
adm placement algorithm
ADM placement algorithm

Input N, G, t;

Compute number of ADM’s needed at each node by the equation:

Compute number of wavelengths by the equation:

Create an ADM hub node;

Place ADM’s needed at each node sequentially;

While (no of ADM’s and wavelengths can be reduced)

{

Assign traffic on each wavelength using shortest path;

Traffic grooming (wavelength combining and segment swapping);

}

wavelength combining
Wavelength Combining

If capacity (i) < G and capacity (j)

And capacity (i) + capacity (j) ≤ G

Then the two wavelengths can be combined

1

W1 =1

W2 =2

W1 =2

W2 =1

W1=3

W1=3

4

2

W1 =2

W2 =1

W1 =1

W2 =2

W1=3

W1=3

3

segment swapping
Segment Swapping

Helps in wavelength combining by “manipulating” wavelengths such

that all link capacities are less than G.

1

W1 = 2

W2 = 3

W3=1

W1 =2

W2 =3

W3=1

W1 =3

W2 =2

W3=1

W1=3

W2=3

W1=3

W2=3

4

2

W1 =2

W2 =3

W3=1

W1 =3

W2 =2

W3=1

W1=3

W2=3

W1 =2

W2 =3

W3=1

W1=3

W2=3

3

example of multihop approach
Example of multihop approach

Given data

N=5, node 0 is hub node

G = 3, tij = 1

By eqn 1, every node needs at least 2 ADMs

By eqn 2, total number of wavelengths is 8

slide29
0 1 2 3 4

3 3 3 3

3 3 3 3

3 3 3 3

3 3 3 3

1 1 1 1

3 3 3 3

1 1 1 1

2 2 2 2

1 1 1 1

1 1 1 1

final result
Final result

Number of wavelengths = 4

Number of ADM’s = 12

comparisons
Comparisons

Increase in G, decrease in W, less ADM’s in hub node

a novel generic approach
A Novel generic Approach

Objectives

Generic Graph Model

Auxiliary Graph

- Vertices

- Edges

IGABAG

Example

Grooming Policies

- Comparison

in heterogenenous networks
In Heterogenenous Networks
  • Traffic Grooming :
  • Can be applied to static or dynamic traffic grooming problem.
  • Each node is characterized by various parameters
  • - Optical switching/multiplexing capabilities-
  • wavelength/waveband/fiber.
  • - Electronic switching/multiplexing grooming
  • capabilities.
  • - Availability of wavelength conversion.
  • - Number of transmitters/receivers.

CSC 778 Fall 2007

objectives
Objectives
  • Traffic grooming problem may have various objectives
  • - Minimize cost (transmitters/receivers).
  • - Minimize overall traffic load.
  • - Minimize maximum traffic on any light path.
  • - Minimize maximum wavelengths on any fiber.

CSC 778 Fall 2007

generic graph model
Generic Graph Model
  • Construct auxiliary graph
  •  Add vertices and edges corresponding to network
  • elements.
  • - Links
  • - Wavelength converters
  • - Electronic ports (transmitters/receivers)
  •  Assign costs to links based on objective
  • Run shortest path algorithm

CSC 778 Fall 2007

auxiliary graph vertices
Auxiliary Graph - Vertices
  • Input and output vertex for each wavelength layer at each node
  • Input and output vertex for lightpath layer at each node
  • Input and output vertex for access layer at each node

CSC 778 Fall 2007

auxiliary graph edges
Auxiliary Graph - Edges
  • Wavelength Bypass Edges (WBE)
  • - From each input to output port on a given
  • wavelength layer.
  • - Optical wavelength switching capability
  • Grooming Edges (GmE)
  • - From input to output port on access layer if
  • grooming is available.
  • - Electronic switching capability.
  • Mux Edges (MuxE)
  • - From output port on access layer to output port
  • on lightpath layer.
  • Demux Edges (DmxE)
  • - From input port on lightpath layer to output port on access layer.

CSC 778 Fall 2007

auxiliary graph edges1
Auxiliary Graph - Edges

• Transmitter Edges (TxE)

- From output port on access layer to output port on

wavelength layer if transmitter is available .

• Receiver Edges (RxE)

- From input port on wavelength layer to input port on

access layer if receiver is available.

• Converter Edges (CvtE)

- From input port on wavelength layer 1 to output

port on wavelength layer 2 if optical wavelength

conversion is available.

CSC 778 Fall 2007

auxiliary graph edges2
Auxiliary Graph - Edges

• Wavelength-Link Edges (WLE)

- From output port on wavelength layer l at node i to

input port on wavelength layer l at node j if

wavelength l is available on the physical link between i

and j

• Lightpath Edges (LPE)

- From output port on the lightpath layer at node i to the

input port of the lightpath layer at node j if there is a

lightpath from node i to node j

CSC 778 Fall 2007

slide40
.

Auxiliary Graph - Edges

GrmE

DmxE

MuxE

TxE

CvtE

RxE

WBE

WLE

CSC 778 Fall 2007

integrated grooming based on the auxiliary graph igabag
Integrated Grooming Based on the Auxiliary Graph (IGABAG)

• Traffic demand: T(s,d,g,m)

- s : source, d : destination, g: granularity,

m: amount of traffic in units of g

• Step 1: Delete edges with capacity less than g.

• Step 2: Find shortest path p from output port on the access

layer of s to the input port on the access layer of d.

• Step 3: If p contains wavelength-link edges, set up

corresponding lightpaths.

• Step 4: Route traffic demand along path p. If the capacity

of lightpaths along p is less than m, route the maximum

amount possible.

• Step 5: Restore edges deleted in Step 1.

• Step 6: Update graph G.

CSC 778 Fall 2007

example2
Example

• Wavelength capacity: OC-48

• Each node has 2 transmitters/receivers

• Granularity: OC-12

• Request 1: T(1, 0, OC-12, 2)

-> Lightpath on 1 from N1

to N0

• Request 2: T(2, 0, OC-12, 1)

• Request 3: T(1, 0, OC-48, 1)

CSC 778 Fall 2007

example3
Example..

CSC 778 Fall 2007

example4
Example…

0

1

2

CSC 778 Fall 2007

example single hop grooming
Example – Single-Hop Grooming

• Request 2: T(2, 0, OC-12, 1)

- new lightpath on 2 from N2-N1-N0

• Request 3: T(1, 0, OC-48, 1)

CSC 778 Fall 2007

example single hop grooming2
Example: single-hop grooming

0

1

2

CSC 778 Fall 2007

example multi hop grooming
Example – Multi-hop Grooming

• Request 2: T(2, 0, OC-12, 1)

- new lightpath on 1 from N2-N1

- Existing lightpath on 1 from N1-N0

• Request 3: T(1, 0, OC-48, 1)

CSC 778 Fall 2007

example multi hop grooming2
Example: multi-hop grooming

0

1

2

CSC 778 Fall 2007

grooming operations
Grooming Operations

CSC 778 Fall 2007

grooming policies
Grooming Policies

• Minimize the Number of Traffic Hops (MinTH)

- Attempt Operation 1

- Attempt Operation 3

- Between Operation 2 and 4, choose the one with fewest logical

hops

• Minimize the Number of Lightpaths (MinLP)

- Attempt Operation 1

- Attempt Operation 2

- Attempt Operation 3 or 4

• Minimize the Number of Wavelength-Links (MinWL)

- Attempt Operation 1

- Attempt Operation 2

- Between Operation 3 and 4, choose the one with fewer wavelength

links

CSC 778 Fall 2007

ordering of requests for static case
Ordering of Requests for Static Case

• Least Cost First (LCF)

- Establish least-cost request first

- Cost = (weight of shortest path for demand)/(amount of traffic)

• Maximum Utilization First (MUF)

- Select connection with maximum utilization first

- Utilization = (amount of traffic)/(number of hops on physical

topology)

• Maximum Amount First (MAF)

- Select connection with largest traffic demand first

CSC 778 Fall 2007

acknowledgments
Acknowledgments
  • Improved Approaches for Cost-effective traffic grooming in WDM Ring Networks : Uniform-Traffic Case; Wonghong Cho, Jian Wang, Biswanath Mukherjee
  • A Novel generic graph model for traffic grooming in heterogenous WDM mesh networks; Hongyue Zhu, Hui Zang, Biswanath Mukherjee
  • Traffic grooming algorithms for reducing electronic multiplexing costs in WDM rings; Angela L. Chiu, Eytan H. Modiano
  • An effective and comprehensive approach for traffic grooming and wavelength in SONET/WDM rings; Xijun Zhang, Chunming Qiao
  • Improved approaches for cost-effective traffic grooming in WDM ring networks: Non-uniform traffic and bidirectional ring; Jian Wang, V. Rao Vemuri
  • Connection Oriented Networks, Harry Perros
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