Traffic Grooming in WDM Networks Kevin Su University

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Traffic Grooming in WDM Networks Kevin Su University of Texas at San Antonio 9/22/2003

Traffic Grooming in WDM Networks Kevin Su University of Texas at San Antonio 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 1

WDM Technology WDM: stands for wavelength division multiplexing, it is a technology that divides

WDM Technology WDM: stands for wavelength division multiplexing, it is a technology that divides the bandwidth of an optical fiber into many non-overlapping wavelengths, so that multiple communication channels can operate simultaneously on different wavelengths. g Each piece of equipment which sends an optical signal has an illusion that it has its own fiber. g Increases the transmission capacity of optical fibers. g Allows simultaneously transmission of multiple wavelengths within a single fiber. (up to 320 wavelengths per fiber; per wavelength, 10 Gb/s, OC-192, today; expected to grow to 40 Gb/s, OC-768, soon) g 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 2

WDM Technology g g g SONET Add/Drop Multiplexer (SADM): can be used to aggregate

WDM Technology g g g SONET Add/Drop Multiplexer (SADM): can be used to aggregate lower rate stream from different end-users into a single high-rate SONET stream in Time Division Multiplexing (TDM) fashion. Optical cross connect (OXC): is a network element that perform wavelength switching and/or wavelength conversion. Wavelength switching: switch traffic from a wavelength of an input fiber link to the same wavelength of any outgoing fiber link. Wavelength conversion: traffic from a wavelength of an input fiber link can be switched to any wavelength in the outgoing fiber link. Lightpath: a traffic route that using same or different wavelength without optical-electronic-optical conversion in WDM optical networks. 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 3

Wavelength Switching / Conversion 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 4

Wavelength Switching / Conversion 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 4

Traffic Grooming g Motivation: g g g Problem Formulation: g g g Gap between

Traffic Grooming g Motivation: g g g Problem Formulation: g g g Gap between capacity of a WDM Channel (OC-48, or OC-192, or OC 768) and bandwidth requirement of a typical connection request (e. g. STS 1, OC-3, OC-12 etc) In order to use network efficiently, low-speed traffic streams need to be efficiently multiplexed, or “groomed” onto high-speed lightpaths Given a network configuration and a set of connection requests with different bandwidth granularities, such as OC-12. Determine how to set up lightpaths to satisfy the connection requests. Category: g g 9/22/2003 Static Case (static traffic): Set of connection requests can all be given in advance Dynamic Case (dynamic traffic): connection requests are given one at a time Kevin Su (xsu@cs. utsa. edu) 5

Network Topology g SONET Ring: SONET ring network is currently the most widely deployed

Network Topology g SONET Ring: SONET ring network is currently the most widely deployed optical network infrastructure. g Mesh Network: Due to the increase of Internet traffic, more WDM networks would be deployed in general mesh topology to meet this demands. Thus more work needs to be done in the mesh networks grooming problem. 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 6

Traffic Grooming Example 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 7

Traffic Grooming Example 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 7

Traffic Grooming Example 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 8

Traffic Grooming Example 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 8

Traffic Grooming Example 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 9

Traffic Grooming Example 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 9

Modeling Traffic Grooming Problem g Static Case: g g The problem is usually formulated

Modeling Traffic Grooming Problem g Static Case: g g The problem is usually formulated as an Integer Linear Program (ILP) problem and get different optimal solution according to different goals. Unfortunately, for large networks it is computationally infeasible to solve the ILP problem. Therefore, many heuristic algorithms were proposed. Dynamic Case: g 9/22/2003 Usually problem is divided into 4 sub-problems. Using different algorithms to solve different sub-problems. Recently one integrated algorithm was proposed to solve the 4 problems altogether. Kevin Su (xsu@cs. utsa. edu) 10

Static Case (ILP) 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 11

Static Case (ILP) 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 11

Subproblems in Traffic Grooming g (1) Determine the virtual topology that consists of lightpaths;

Subproblems in Traffic Grooming g (1) Determine the virtual topology that consists of lightpaths; g g NP-hard (2) Routing the lightpaths over the physical topology; g NP-hard g (3) Performs wavelength assignment to the lightpaths; (2) and (3) together are RWA problem g (4) Routing the traffic on the virtual topology. 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 12

Future Challenges g g Grooming with Protection Provide two routing path for each connection

Future Challenges g g Grooming with Protection Provide two routing path for each connection requests, one is primary traffic stream path (TSP), the other is link-disjoint backup traffic stream path (TSP). 1: 1 protection (dedicated), 1: m (shared backup TSP). Multicast Traffic Grooming The objective is to construct multicast trees (or light-trees) that optically carry the multicast traffic from the source to the destination nodes, which will reduce the cost of network. 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 13

Protection 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 14

Protection 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 14

Multicast Traffic Grooming 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 15

Multicast Traffic Grooming 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 15

Multicast Traffic Grooming • Session 1: Source = A; Destination = {B, C}; Traffic

Multicast Traffic Grooming • Session 1: Source = A; Destination = {B, C}; Traffic demand = 1 unit; • Session 2: Source = B; Destination = {C}; Traffic demand = 2 unit; • Session 3: Source = A; Destination = {F}; Traffic demand = 1 unit; • Routing the demands using an SMT requires 7 ADMs and two wavelengths, as shown in Figure 1. However, • using the routing shown in Figure 2 costs just 6 ADMs and one wavelength 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 16

References • Ruda Dutta and George N. Rouskas, Traffic Grooming in WDM Networks: Past

References • Ruda Dutta and George N. Rouskas, Traffic Grooming in WDM Networks: Past and Future, IEEE Network 2002 • Sashisekaran Thiagarajan, Arun K. Somani Traffic Grooming for Survivable WDM Mesh Networks Opti. Comm 2001 • Ahmed E. Kamal, Raza Ul-Mustafa, Multicast Traffic Grooming in WDM Networks, Opti. Comm 2003 • Hui Zang, Canhui Ou, Biswanath Mukherjee Path-Protection Routing and Wavelength Assignment (RWA) in WDM Mesh Networks Under Duct-Layer Constraints, IEEE/ACM Transactions on Networking, April 2003 9/22/2003 Kevin Su (xsu@cs. utsa. edu) 17