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## PowerPoint Slideshow about 'Traffic Grooming' - salaam

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Presentation Transcript

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

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.

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

- SONET
- ADM
- WADM

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

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

- 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.

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

- 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

OC-48 SONET ring

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

- 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

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.

Traffic Grooming Approach1 (Random)

Total number of ADM’s needed = 8

Traffic Grooming Approach 2

Total number of ADM’s needed = 7

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)

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)

- 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

- 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

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

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

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

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

Comparisons

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

A Novel generic Approach

Objectives

Generic Graph Model

Auxiliary Graph

- Vertices

- Edges

IGABAG

Example

Grooming Policies

- Comparison

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

- 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

- 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

- 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

- 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 - 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 - 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

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

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

Example..

CSC 778 Fall 2007

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 grooming

CSC 778 Fall 2007

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 grooming

CSC 778 Fall 2007

Grooming Operations

CSC 778 Fall 2007

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

• 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

Comparison of Policies – Non Blocking Model

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Comparison of Policies – Blocking Model

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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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