Traffic Grooming

1. Traffic Grooming Apoorv Nayak Prathyusha Dasari

2. 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

3. 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.

4. 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

5. Terminology • SONET • ADM • WADM

6. 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.

7. 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.

8. 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.

9. Example M<N

10. 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.

11. 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.

12. Example of a SONET ring OC-48 SONET ring

13. 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.

14. 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).

15. Logical Rings 1 2 5 3 4

16. 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.

17. 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

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

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

20. 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)

21. 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).

22. 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.

23. Example of a unidirectional multihop WDM ring network

24. 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

25. 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); }

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

27. 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

28. 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

29. 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

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

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

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

33. 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

34. 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

35. 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

36. 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

37. 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

38. 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

39. 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

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

41. 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

42. 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

43. Example.. CSC 778 Fall 2007

44. Example… 0 1 2 CSC 778 Fall 2007

45. 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

46. Example: single-hop grooming CSC 778 Fall 2007

47. Example: single-hop grooming 0 1 2 CSC 778 Fall 2007

48. 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

49. Example: multi-hop grooming CSC 778 Fall 2007

50. Example: multi-hop grooming 0 1 2 CSC 778 Fall 2007