1. Examination Committee: Dr. Poompat Saengudomlert (Chairperson) Assoc. Prof. Tapio Erke Dr. R.M.A.P. Rajatheva 1 Telecommunications FoS Asian Institute of Technology

2.  WDM networks and the problem of capacity expansion  Our model of WDM networks  PART 1: Minimum Cost Capacity Expansion  PART2: Optimal Capacity Expansion with Budgetary Constraint  Conclusion  Further research ! 2

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4.  Predicted in 2007 : Present traffic could quadruple by 2011  Youtube : Feb, 2005 4 WDM networks..> Huge Bandwidth Requirement P2P Video Conferencing Multimedia on the Internet Video on demand

5.  Analogy: Different modes of transport on the road  Optical transport technologies:  SDH  PDH  Metro Ethernet  WDM/DWDM 5 WDM networks..>

6.  Preferred choice for the future ▪ Multiply the capacity of a single fiber ▪ Easy to expand ▪ Cost: Scale linearly with capacity  Full wavelength conversion at nodes  Any input to any output 6 WDM networks..>

7.  We know: Network topology, source- destination pairs  Constraints to be satisfied:  Traffic demand prediction  Need to find the best possible capacity allocation  Objective: Save the budget 7 WDM networks..>

8.  Similar to dimensioning  Differences:  Existing capacity in the network  Existing connections  Existing connections must be preserved 8 WDM networks..>

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10.  WDM networks vs. Traditional Telephone Networks  (Assuming Poisson arrivals and Exponential Holding times)  Due to slowness of WDM traffic:  Significant traffic growth: Arrival rates change during network operation  Linear growth  Exponential growth  WDM Networks may not operate in steady state (Nayak and Sivarajan, 2002) TelephonyWDM TrafficTelephone callsLightpaths Arrival rates1 – 10 calls per hour1-10 lightpaths per year Holding timesFew minutesFew months or years 10 Motivation>

11.  Nayak and Sivarajan (2002)  Continuous time Markov Chain model of a WDM link  Absorption probability instead of blocking probability:  An imaginary state  Time dependant  Existing Method to compute absorption probabilities  Complex  Only for networks at initially zero state 11 Motivation>

12. 1. A technique of dimensioning and expansion of WDM, under traffic growth with minimum cost  Based on absorption probabilities 2. Solve dimensioning & expansion of WDM with budgetary constraint and traffic uncertainty  Contribution  For dimensioning and expansion with a minimum cost: ▪ Simple algorithm instead of Non-linear optimization that exists  For dimensioning and expansion with budgetary constraint: ▪ A linear optimization technique ▪ A heuristic algorithm that gives optimal solution (Maximum lifetime) ▪ Consider all possible demand scenarios 12

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14.  Based on discrete time Markov Chain of a single link  Approximate link arrival rate using a method similar to Erlang Fixed Point method (consider bi-directional, symmetric traffic)  Arrival rate, termination rate, growth -> Absorption probability  At each small time interval δt,  Iterative computations required to get final values 14 Proposed Model > K δt = T

15.  Existing method is a non linear optimization  Link Criticality based Capacity Expansion  Proposed algorithm  gives results close to optimal  Can be used for networks at any initial state  Can incorporate any traffic growth model  The First time multi-period capacity expansion is performed for WDM networks based on transient state analysis  Published at the International Conference on Electrical Engineering/Electronics, Computer, Telecommunications, and Information Technology -2009 Gunawardena B. and Saengudomlert P.,“Dimensioning and Expansion Algorithm for WDM Networks Under Traffic Growth”, ECTI-CON’09 15 Proposed Model >

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17.  To make full use of the budget  Network have to last longer without further expansion  a relationship between capacity allocation and life  Can consider an s-d as an isolated logical link  λ(0), μ and τ  Absorption prob. of s-d  99%-guarantee lifetime:  L 99= Time at which Absorption probability exceed 0.01 for a single s-d pair Optimal Capacity Expansion with Budgetary Constraint > 17

18.  Need to Maximize the guaranteed lifetime 18 Optimal Capacity Expansion with Budgetary Constraint > Variation of life expectancy with capacity for a single s-d pair log 10 (Life expectancy) with capacity Convex Concave

19.  Simple modification: make the problem MIP:  Non-linear function to Piecewise linear function x – 1 x x + 1 Capacity allocated (x sd ) Log 10 (Life Expectancy) Utility Function ( U sd ) g x-1 (x sd ) g x (x sd ) g x+1 (x sd ) Optimal Capacity Expansion with Budgetary Constraint > 19

20.  Objective: Maximize the utility  Constraints:  Total cost must be below budget  At least the demand at time T must be satisfied  Conservation of existing traffic  Other constraints  Multiple paths are considered for an s-d pair  Tools used: CPLEX, Matlab, C# Express Edition, Excel 20 Optimal Capacity Expansion with Budgetary Constraint >

21.  To compare with the results of optimization  Only shortest paths are used 1. Demand based Capacity Allocation (DeCA)  An instinctive solution to the problem  Excess capacities are allocated to s-d pairs based on demand  Until budget is fully used 2. Minimum Utility based Capacity Allocation (MUCA)  Step by step allocation  Each step, capacity allocated to s-d pair with minimum utility 21 Optimal Capacity Expansion with Budgetary Constraint >

22.  To validate and compare optimization and heuristic algorithms 1. 99%-guarantee lifetime of resulting network  Optimization: Objective function gives the answer  Heuristics: Explicitly calculated 2. Simulation  Simulate the arrival and termination process for all s-d pairs  Find out lifetimes of all s-d pairs in every trial 22 Optimal Capacity Expansion with Budgetary Constraint >

23. (s,d)Source Node Destination Node Initial Arrival Rate Paths (2-shortest link disjoint paths) 11931-11-9, 1-2-4-6-8-9 211741-11-13-14-17, 1-3-5-7-10-19-17 341744-5-7-10-19-17, 4-6-8-12-18-17 441824-6-8-12-18, 4-5-7-10-19-17-18 551125-3-1-11, 5-7-10-9-11 681948-10-19, 8-12-18-17-19 71014110-19-17-14, 10-8-12-15-14 81115311-13-14-15, 11-9-8-12-15 91418314-17-18, 14-15-18 101520415-20,15-18-17-19-20 Optimal Capacity Expansion with Budgetary Constraint > Table of Parameters Planning Horizon, T2 year P th 0.01 Termination rate, μ1 per year Traffic Growth param., τ2 year Cost of 1 wavelength1 unit Min Budget = 287 23

24.  Best Solution: Optimal with 2 paths  Not too different from 1 path case.  because most cases use only the shortest paths  Other path uses too much resources  MiLECA is as best as optimal with 1 path  Instinctive solution, not suitable (DeCA) Optimal Capacity Expansion with Budgetary Constraint > Extra guaranteed lifetime gained by using MiLECA instead of DeCA 24

25.  Within 1% at all values of budget  Approach is accurate Optimal Capacity Expansion with Budgetary Constraint > 25

26.  Life expectancy: A direct representation of the objective  No significant advantage of using multiple link- disjoint paths  MiLECA can replace optimization for 1 path case  Opens up a lot of possibilities..  Spin-off Project:  With the Electricity Generating Authority of Thailand (EGAT) :  “Development of Optimization Algorithm and Program for Dimensioning and Expansion of WDM Optical Fiber Networks” Optimal Capacity Expansion with Budgetary Constraint > 26

27.  Path Criticality Based Dimensioning and Expansion Algorithm  Can be extended for multiple path case  Expansion with Budgetary Constraint  Using multiple, none link-disjoint paths  Using single path, but sharing of capacities among s-d pairs  Considerations for different wavelength conversion techniques, protection & grooming Optimal Capacity Expansion with Budgetary Constraint > 27

28.  My advisor  Dr. Poompat Saengudomlert  Examination committee members  Assoc. Prof. Tapio Erke  Dr. R.M.A.P.Rajatheva  Scholarship donors  My friends at AIT  My family back home 28