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Apoorv Nayak Prathyusha Dasari Traffic Grooming

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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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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.

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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

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Terminology SONET ADM WADM

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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.

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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.

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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.

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Example M

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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.

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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.

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Example of a SONET ring OC-48 SONET ring

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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.

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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).

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Logical Rings

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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.

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Network Topology fiber t1 t2 t3 t4 t5t6 a) Physical Networkb) Traffic on the Network

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Traffic Grooming Approach1 (Random) Total number of ADM’s needed = 8

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Traffic Grooming Approach 2 Total number of ADM’s needed = 7

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Single hop traffic grooming algorithm do{ dcost = perturb(); if(∆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)

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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).

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

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Example of a unidirectional multihop WDM ring network

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Terminology and assumptions W - Number of wavelengths. D i - Number of ADM’s in the i th node. G - Grooming ratio (Ratio of the wavelength channel rate to the lowest traffic rate). t ij - Traffic requirement (Number of low rate circuits between i and j for i-j pair). t ij = 1 for uniform traffic. Given data Number of nodes- N Traffic matrix- T Grooming ratio- G

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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); }

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

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Segment Swapping Helps in wavelength combining by “manipulating” wavelengths such that all link capacities are less than G W1 = 2 W2 = 3 W3=1 W1 =3 W2 =2 W3=1 W1 =3 W2 =2 W3=1 W1 =2 W2 =3 W3=1 W1=3 W2=3 W1=3 W2=3 W1=3 W2=3 W1=3 W2=3 W1 =2 W2 =3 W3=1 W1 =2 W2 =3 W3=1

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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

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Final result Number of wavelengths = 4 Number of ADM’s = 12

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

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A Novel generic Approach Objectives Generic Graph Model Auxiliary Graph - Vertices - Edges IGABAG Example Grooming Policies - Comparison

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In Heterogenenous Networks In Heterogenenous Networks CSC 778 Fall 2007 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.

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CSC 778 Fall 2007 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. Objectives

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CSC 778 Fall 2007 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 Generic Graph Model

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CSC 778 Fall 2007 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 Auxiliary Graph - Vertices

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Auxiliary Graph - Edges CSC 778 Fall 2007 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.

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Auxiliary Graph - Edges CSC 778 Fall 2007 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.

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Auxiliary Graph - Edges CSC 778 Fall 2007 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

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. CSC 778 Fall 2007 WBE GrmE MuxE DmxE TxE RxE CvtE WLE Auxiliary Graph - Edges

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Integrated Grooming Based on the Auxiliary Graph (IGABAG) CSC 778 Fall 2007 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.

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Example CSC 778 Fall 2007 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)

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Example.. CSC 778 Fall 2007

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0 12 Example…

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Example – Single-Hop Grooming CSC 778 Fall 2007 Request 2: T(2, 0, OC-12, 1) - new lightpath on 2 from N2-N1-N0 Request 3: T(1, 0, OC-48, 1)

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CSC 778 Fall 2007 Example: single-hop grooming

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CSC 778 Fall Example: single-hop grooming

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Example – Multi-hop Grooming CSC 778 Fall 2007 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)

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CSC 778 Fall 2007 Example: multi-hop grooming

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CSC 778 Fall Example: multi-hop grooming

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Grooming Operations CSC 778 Fall 2007 Add New Lightpath(s) Single-hop or multi-hop grooming Operation 1NoSingle-hop Operation 2NoMulti-hop Operation 3YesSingle-hop Operation 4YesMulti-hop

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Grooming Policies CSC 778 Fall 2007 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

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Ordering of Requests for Static Case CSC 778 Fall 2007 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

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Comparison of Policies – Non Blocking Model CSC 778 Fall 2007

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Comparison of Policies – Blocking Model CSC 778 Fall 2007

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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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Questions??

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