Traffic Grooming - Rudra Dutta, Associate Professor
Download
Report
Transcript Traffic Grooming - Rudra Dutta, Associate Professor
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
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.
Example
M<N
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).
Logical Rings
1
1
5
3
2
3
4
1
2
3
2
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
a) Physical Network
0
1
b) Traffic on the Network
t1
0
t5
1
t6
fiber
t2
t4
3
2
3
t3
2
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.
Example of a unidirectional multihop WDM ring network
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) <G
And capacity (i) + capacity (j) ≤ G
Then the two wavelengths can be combined
W1 =2
1
W1 =1
W2 =2
W2 =1
W1=3
W1=3
4
2
W1 =2
W2 =1
W1=3
W1 =1
W1=3
W2 =2
3
Segment Swapping
Helps in wavelength combining by “manipulating” wavelengths such
that all link capacities are less than G.
1
W1
W1=2
=3
W2
=3
W1=3
W2
=2
W1 = 2
W2 = 3
W1=3
W3=1
W2=3
W3=1
W2=3
W3=1
4
2
W1
=2=3
W1
W1=3
W2
=3
W2
=2
W2=3
W3=1
W3=1
W1 =2
W2 =3
W1=3
3
W3=1
W2=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
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
1
3
1
3
1
3
1
2
1
2
1
2
1
2
1
1
1
1
1
Final result
Number of wavelengths = 4
Number of ADM’s = 12
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 capabilitieswavelength/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
.
Auxiliary Graph - Edges
GrmE
DmxE
MuxE
TxE
CvtE
RxE
WBE
WLE
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…
0
1
2
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: single-hop grooming
0
1
2
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
Example: multi-hop grooming
0
1
2
CSC 778 Fall 2007
Grooming Operations
Add New
Lightpath(s)
Single-hop or
multi-hop
grooming
Operation 1
No
Single-hop
Operation 2
No
Multi-hop
Operation 3
Yes
Single-hop
Operation 4
Yes
Multi-hop
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
CSC 778 Fall 2007
Comparison of Policies – Blocking Model
CSC 778 Fall 2007
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
Questions??