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fouad_kharroubi_PhD_Defense_05_06_2014.ppt

  1. 1. School of Information Science and Engineering Hunan University, Changsha 410082, China 2013-2014 Random Search Algorithms for Solving the Routing and Wavelength Assignment in WDM Networks Presented by the PhD Candidate : Fouad KHARROUBI (方达) Supervisors: Professor Chen Lin and Assistant Prof Jing He June 2014 PhD Defense
  2. 2. • Introduction • Thesis Contributions • Published Papers • Conclusion and Future Work Outline Fouad KHARROUBI June 2014 2
  3. 3. Background (1) Fouad KHARROUBI June 2014 3 Introduction Thesis Contributions Published Papers Conclusion and Future Work The huge demand for higher bandwidth speeds has transformed the cabling technology
  4. 4. Background (2) Fouad KHARROUBI June 2014 4 Introduction Thesis Contributions Published Papers Conclusion and Future Work WDM
  5. 5. Background(3) Fouad KHARROUBI June 2014 5 Introduction Thesis Contributions Published Papers Conclusion and Future Work WDM RWA New Technology New Problem The Wavelength Clash Constraint The Wavelength Continuity Constraint
  6. 6. Background (3) Routing Wavelength Assignment K-shortest Path shortest Path Backtracking Random methods Exact methods Hybrid methods Fouad KHARROUBI June 2014 6 Background Thesis Contributions Published Papers Conclusion and Future Work RWA Subproblems
  7. 7. Background (3) Routing Wavelength Assignment K-shortest Path shortest Path Backtracking Random methods Exact methods Hybrid methods Fouad KHARROUBI June 2014 7 Background Thesis Contributions Published Papers Conclusion and Future Work RWA Subproblems
  8. 8. Thesis Contributions Fouad KHARROUBI June 2014 8 Background Thesis Contributions Published Papers Conclusion and Future Work Therefore, we investigated four random search algorithms combined with the Backtracking algorithm to solve the Max-RWA problem in WDM networks. The contributions of our conducted research are as follows: 1. A new mathematical formulation for the Max-RWA problem in WDM optical networks is proposed and its constraints are analyzed 2. Four efficient random search algorithms are proposed and experimentally demonstrated to solve the problem of Max-RWA in WDM optical networks. Namely: ROA, GA, TSA and EP; 3. To the best of our knowledge, a novel efficient Backtracking algorithm is proposed and investigated in WDM optical network for the first time. 4. A huge number of experiments that consist of 1080 extensive tests are conducted in the first part of our experiments and 480 tests in the second part by running ROA, GA, TSA and EP simultaneously under different circumstances.
  9. 9. 1. Mathematical Formulation Problem . To simplify the problem . To formulate and solve the problem Our Aim . To maximize the number of optical connection requests that can be established for a given number of wavelengths on a given physical topology. Proposed Solution . A simple mathematical formulation that can be used to solve the problem efficiently. Fouad KHARROUBI June 2014 9 Background Thesis Contributions (1) Published Papers Conclusion and Future Work
  10. 10. 1. Mathematical Formulation . The Max-RWA problem can be mathematically formulated as follows: Fouad KHARROUBI June 2014 10 Background Thesis Contributions (1) Published Papers Conclusion and Future Work
  11. 11. Fouad KHARROUBI June 2014 11 1. Mathematical Formulation Background Thesis Contributions (1) Published Papers Conclusion and Future Work
  12. 12. Fouad KHARROUBI June 2014 12 1. Mathematical Formulation Background Thesis Contributions (1) Published Papers Conclusion and Future Work
  13. 13. 2. Random Search Algorithms Problem . To solve approximately the RWA problem using several metheuristics. Our Aim . To maximize the number of optical connection requests that can be established for a given number of wavelengths on a given physical topology. Proposed Solution . four random search algorithms have been proposed to solve the RWA problem. Namely: ROA, GA, TSA and EP. Fouad KHARROUBI June 2014 13 Background Thesis Contributions (2) Published Papers Conclusion and Future Work
  14. 14. Part I: Local search algorithms 1. Random Optimization 2. Simple Descent 3. Deepest Descent 4. Multi-start Descent 5. Variable Neighborhood Search (VNS) 6. Tabu Search (TS) Part II: Stochastic search algorithms 1. Simulated annealing 2. Threshold Accept Part II: Evolutionary algorithms 1. Evolutionary programming 2. Evolution Strategy 3. Genetic algorithms 4. Genetic programming 5. Learning classifier system Part III: Inspired algorithms 1. Stochastic Diffusion Search 2. Generalized external optimization 3. Harmony search 4. Ant colony optimization 5. Differential evolution 6. Particle swarm optimization 7. Invasive weed optimization algorithm 8. Gaussian adaptation Fouad KHARROUBI June 2014 14 Background Thesis Contributions (2) Published Papers Conclusion and Future Work 2. Random Search Algorithms
  15. 15. Part I: Local search algorithms 1. Random Optimization 2. Simple Descent 3. Deepest Descent 4. Multi-start Descent 5. Variable Neighborhood Search (VNS) 6. Tabu Search (TS) Part II: Stochastic search algorithms 1. Simulated annealing 2. Threshold Accept Part II: Evolutionary algorithms 1. Evolutionary programming 2. Evolution Strategy 3. Genetic algorithm 4. Genetic programming 5. Learning classifier system Part III: Inspired algorithms 1. Stochastic Diffusion Search 2. Generalized external optimization 3. Harmony search 4. Ant colony optimization 5. Differential evolution 6. Particle swarm optimization 7. Invasive weed optimization algorithm 8. Gaussian adaptation Fouad KHARROUBI June 2014 15 Background Thesis Contributions (2) Published Papers Conclusion and Future Work 2. Random Search Algorithms
  16. 16. Our Proposed Random Optimization Algorithm The Random Optimization Algorithm (ROA) is a local search method which is known for its simplicity, speed in terms of execution time as well as its effectiveness in terms of quality of solutions found. Step (1): Initialization Step (2): Wavelength Assignment Step (3): Update of the found solution Step (4): Copy back of the last found solution Fouad KHARROUBI June 2014 16 Background Thesis Contributions (2) Published Papers Conclusion and Future Work 2. Random Search Algorithms
  17. 17. Fouad KHARROUBI June 2014 17 2. Random Search Algorithms Background Thesis Contributions (2) Published Papers Conclusion and Future Work Our Proposed Genetic Algorithm The Genetic Algorithm (GA), proposed in the 1970s by John Holland at University of Michigan, is a particular class of evolutionary algorithms and it is considered as a random search algorithm that imitates the process of biological evolution in order to solve combinatorial optimization problems. Step (1): Initialization Step (2): Crossover Step (3): Mutation Step (4): Update of the found solution Step (5): Copy back of the last found solution
  18. 18. Our Proposed Genetic Algorithm (Crossover Operator) Fouad KHARROUBI June 2014 18 Background Thesis Contributions (2) Published Papers Conclusion and Future Work 2. Random Search Algorithms
  19. 19. Our Proposed Genetic Algorithm (Mutation Operator) Fouad KHARROUBI June 2014 19 Background Thesis Contributions (2) Published Papers Conclusion and Future Work 2. Random Search Algorithms
  20. 20. Tabu Search Algorithm (TSA) is an iterative random search algorithm that can be used for solving combinatorial optimization problems. This metaheuristic developed by Glover uses a local search procedure in which we will move iteratively from an initial random solution to another better solution in the neighborhood of the former one. TSA is useful to help the search move away from previously visited portions of the search space and thus perform more extensive exploration. Step (1): Initialization Step (2): Primary Solutions Step (3): Wavelength Assignment Step (4): The main loop Step (5): Choose the best solution non-tabu list Step (6): Update of the found solution Step (7): Copy back of the last found solution Fouad KHARROUBI June 2014 20 Background Thesis Contributions (2) Published Papers Conclusion and Future Work 2. Random Search Algorithms Our Proposed Tabu Search Algorithm
  21. 21. Our Proposed Evolutionary Programming This random optimization metaheuristic is relatively similar to Genetic algorithm (GA) since both of them imitate the process of biological evolution in order to solve combinatorial optimization problems. For this purpose EP exploits a myriad of techniques (also known as operators) inspired by natural evolution, such as selection, mutation, replacement so that it can generate the best approximate solutions to optimization problems. Step (1): Initialization Step (2): Mutation Step (3): Update of the found solution (Evaluation) Step (4): Copy back of the last found solution (Replacement) Fouad KHARROUBI June 2014 21 Background Thesis Contributions (2) Published Papers Conclusion and Future Work 2. Random Search Algorithms
  22. 22. Fouad KHARROUBI June 2014 22 3. New Backtracking Algorithm Background Thesis Contributions (3) Published Papers Conclusion and Future Work Problem . To solve the routing subproblem by generating all the possible lightpaths. Our Aim . To give more chance for each connection request to be satisfied by generating more lightpaths. Proposed Solution . A new backtracking is proposed for the first time to the best of our knowledge in combination with ROA, GA, TSA and EP.
  23. 23. Fouad KHARROUBI June 2014 23 3. New Backtracking Algorithm Background Thesis Contributions (3) Published Papers Conclusion and Future Work
  24. 24. Fouad KHARROUBI June 2014 24 3. New Backtracking Algorithm Background Thesis Contributions (3) Published Papers Conclusion and Future Work Step (1): The Stopping Condition Step (2): We keep the new lightpath into the list of all lightpaths Step (3): Backtrack
  25. 25. Fouad KHARROUBI June 2014 25 Problem . To evaluate the performance of our proposed algorithms. Our Aim . To carry out an experiment that consist of 1080 extensive tests by running ROA, GA and TSA simultaneously on randomly-generated topology networks (case 1) as well as a on fixed-generated topology networks (case 2) which are composed of 4, 8, 14, 19, 25 and 38 nodes for each of the cases. Proposed Solution . We run ROA, GA and TSA simultaneously under different conditions. Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Experiments (Part I)
  26. 26. The hardware used for our experiments is an Intel(R) Core(TM) i5-3470 CPU 3.20 GHZ within a 16 GB for the RAM, running under Kernel Ubuntu Linux 3.5.0-17-generic operating system. All algorithms were compiled by GCC compiler of Qt Creator 2.6.0 (based on Qt 5.0.0 “64Bit”). Algorithms characteristics 4. Experiments (Part I) Experimental Setup (1) Fouad KHARROUBI June 2014 26 Background Thesis Contributions (4) Published Papers Conclusion and Future Work
  27. 27. Experimental Setup(2) Fouad KHARROUBI June 2014 27 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Experiments (Part I)
  28. 28. 1 2 3 4 Fouad KHARROUBI June 2014 28 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Experiments (Part I)
  29. 29. Experimental Setup(3) Fouad KHARROUBI June 2014 29 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Experiments (Part I)
  30. 30. Experimental Setup(4) Fouad KHARROUBI June 2014 30 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Experiments (Part I)
  31. 31. Performance Analysis 0 100 200 300 400 500 600 700 800 N=4 N=8 N=14 N=19 N=25 N=38 N=4 N=8 N=14 N=19 N=25 N=38 N=4 N=8 N=14 N=19 N=25 N=38 Number of Nodes Satisfied Connection Requests RO_fixed GA_fixed TS_fixed RO_rand GA_rand TS_rand B ons MaxIterati N mult N sd sd    ; 1000 ) ( ; 1 30  wav N 90  wav N 270  wav N Fouad KHARROUBI June 2014 31 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Experiment Results (Part I)
  32. 32. B ons MaxIterati N mult N sd sd    ; 1000 ) ( ; 1 Time Analysis 0 10000 20000 30000 40000 50000 60000 70000 80000 N=4 N=8 N=14 N=19 N=25 N=38 N=4 N=8 N=14 N=19 N=25 N=38 N=4 N=8 N=14 N=19 N=25 N=38 Number of Nodes Time(sec.) RO_fixed GA_fixed TS_fixed RO_Rand GA_Rand TS_Rand 30  wav N 90  wav N 270  wav N Fouad KHARROUBI June 2014 32 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Experiment Results (Part I)
  33. 33. B ons MaxIterati N mult N sd sd    ; 20 ) ( ; 5 Performance Analysis 0 10 20 30 40 50 60 70 80 N=4 N=8 N=14 N=19 N=38 N=4 N=8 N=14 N=19 N=38 N=4 N=8 N=14 N=19 N=38 Number of Nodes Satisfied Connection Requests RO_fixed GA_fixed TS_fixed RO_rand GA_rand TS_rand 30  wav N 90  wav N 270  wav N Fouad KHARROUBI June 2014 33 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Experiment Results (Part I)
  34. 34. B ons MaxIterati N mult N sd sd    ; 20 ) ( ; 5 Time Analysis 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 N=4 N=8 N=14 N=19 N=38 N=4 N=8 N=14 N=19 N=38 N=4 N=8 N=14 N=19 N=38 Number of Nodes Time(sec.) RO_fixed GA_fixed TS_fixed RO_fixed GA_fixed TS_fixed 30  wav N 90  wav N 270  wav N Fouad KHARROUBI June 2014 34 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Experiment Results (Part I)
  35. 35. •The performance using ROA, GA or TSA to solve the Max-RWA increases rapidly as the number of wavelength increases. •The performance of TSA is the best when the total number of all connection-requests desired to satisfy is equal to 5, 20 or 100. Thus, in terms of accepted connection- requests TSA achieves up to a 23% improvement over GA and a 10% improvement over ROA. •ROA have also shown a good performance which is better than GA and close to TSA when the number of connection requests is equal to 5 or 20. However, the gap is widening in favor of TSA when is equal to 100. Indeed, in terms of accepted connection-requests ROA achieves up to a 14% improvement over GA. Fouad KHARROUBI June 2014 35 4. Experiment Results (Part I) Background Thesis Contributions (4) Published Papers Conclusion and Future Work
  36. 36. • When we increase the number exponentially till it is equal to 1000, the surprise is only GA which will perform well with a better results than ROA or TSA. This can be explained by the crossover (or recombination) operator used by GA which maintains diversity in the solution space. In fact, in terms of accepted connection-requests we found that GA achieves up to a 6% improvement over TSA and a 7% improvement over ROA. Algorithms characteristics Fouad KHARROUBI June 2014 36 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Experiment Results (Part I)
  37. 37. •The fixed-generated topology networks (case 2) consume more time to be solved with whatever ROA, GA and TSA than the time taken in the randomly-generated topology networks (case 1). This is due to the number of edges. In fact, in case 2 the number of edges is relatively larger than those in case 1 especially when the number of nodes is between 14 and 38. Edge Analysis 0 10 20 30 40 50 60 70 N=4 N=8 N=14 N=19 N=25 N=38 Nodes Number of Edges Random_Topology Fixed_Topology Fouad KHARROUBI June 2014 37 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Experiment Results (Part I)
  38. 38. • The time spent by ROA, GA or TSA to solve the Max-RWA increases rapidly depending on the number of nodes as well as that of the wavelengths. •In general and comparing to TSA, we found that GA and more especially ROA perform very efficiently in terms of speed with an edge for ROA compared to GA. In fact, the time spent by GA, on average, is 5 times higher than ROA. •The time spent by TSA, on average, is 5 times higher than GA and 29 times higher than ROA. •The time spent by TSA is significantly higher, but since we are dealing with the static case, computations are performed offline, so the runtimes of TSA are reasonable given this constraint. Fouad KHARROUBI June 2014 38 4. Experiment Results (Part I) Background Thesis Contributions (4) Published Papers Conclusion and Future Work
  39. 39. Fouad KHARROUBI June 2014 39 4. Experiments (Part II) Background Thesis Contributions (4) Published Papers Conclusion and Future Work
  40. 40. iterations ons MaxIterati N mult N C Case sd sd 5000 ; 20 ) ( ; 5 :    Fouad KHARROUBI May 2014 40 4. Experiment Results (Part II) Background Thesis Contributions (4) Published Papers Conclusion and Future Work
  41. 41. iterations n MaxIteatio N mult N C Case sd sd 15000 ; 20 ) ( ; 5 :    Fouad KHARROUBI June 2014 41 4. Experiment Results (Part II) Background Thesis Contributions (4) Published Papers Conclusion and Future Work
  42. 42. Fouad KHARROUBI June 2014 42 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Experiment Results (Part II)
  43. 43. Fouad KHARROUBI June 2014 43 Algorithms Characteristics Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Experiment Results (Part II)
  44. 44. Fouad KHARROUBI June 2014 44 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Performance Comparison
  45. 45. Fouad KHARROUBI June 2014 45 4. Performance Comparison Background Thesis Contributions (4) Published Papers Conclusion and Future Work
  46. 46. Fouad KHARROUBI June 2014 46 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Performance Comparison
  47. 47. Fouad KHARROUBI June 2014 47 4. Performance Comparison Background Thesis Contributions (4) Published Papers Conclusion and Future Work
  48. 48. • We successfully covered 100% of all the generated paths. • We find that, in terms of accepted connection-requests, on average, TSA achieves up to a 11% improvement over GA, a 5% improvement over ROA and only a 1% improvement over EP. • Time spent by TSA, on average, is 10 times higher than EP, 2 times higher than ROA and 3 times higher than GA. Fouad KHARROUBI June 2014 48 Background Thesis Contributions (4) Published Papers Conclusion and Future Work 4. Performance Comparison
  49. 49. Published Papers Fouad Kharroubi, Jing He, and Lin Chen, (2014) "Performance Analysis of GA, ROA, and TSA for Solving the Max-RWA Problem in Optical Networks," in Optical Fiber Communication Conference, OSA Technical Digest (online) (Optical Society of America, 2014), paper W2A.48. (EI) (Accepted and already published Online via OSA). Fouad Kharroubi, Jing He, Tang Jin, Chen Ming, Chen Lin (2013) “Evaluation performance of genetic algorithm and tabu search algorithm for solving the Max-RWA problem in all-optical networks”. Journal of Combinatorial Optimization (JOCO). Springer New York. DOI 10.1007/s10878-013-9676-y. (SCI, Level 3, IF: 0.59), (Accepted and already published Online via Springer). Fouad Kharroubi, Lin Chen, and Jianjun. Yu (2012), “Approaches and controllers to solving the contention problem for packet switching networks: A survey,” in Internet of Things, ser. Communications in Computer and Information Science (CCIS). 2012, vol. 312, pp. 172–182. Springer Berlin Heidelberg. (EI), (Accepted and already published Online via Springer). Fouad KHARROUBI May 2014 49 Background Thesis Contributions Published Papers Conclusion and Future Work
  50. 50. Conclusion and Future Work • In this research work, we have implemented and compared four metaheuristics to deal with the Max-RWA problem in all optical networks. •The objective was to maximize the number established connections depending on the set of wavelength at stake. (static-case). •The RWA problem was mathematically formulated and solved approximately by three efficient random search algorithms namely; ROA, GA, TSA and EP. •The routing subproblem was insured exactly by the backtracking algorithm while the wavelength assignment subproblem was solved randomly. •A relevant comparison, including the performance and the time involved, was made between the three algorithms, making a total of 1560 experiments in different circumstances and variations. Fouad KHARROUBI June 2014 50 Background Thesis Contributions Published Papers Conclusion and Future Work
  51. 51. Conclusion and Future Work •One of the most significant results to be noted is that TSA performed very well and showed very good results in comparison to EP, GA and ROA. However, its drawback is in the fact that it is the slowest one in terms of the consumed running time to solve Max-RWA problem •For future work, we would like to implement more metaheurisics but this time on networks with full, sparse and limited-range wavelength conversion capability. A comparison between the backtracking method and the k-shortest path method would also need to be explored further. We will also consider Max-RWA problem for the multicast advance reservation case. The mathematical formulation and heuristics can be adapted for this case. Fouad KHARROUBI June 2014 51 Background Thesis Contributions Published Papers Conclusion and Future Work
  52. 52. Any Question? Thank You  Fouad KHARROUBI June 2014 52 Background Contributions Published Papers Conclusion and Future Work

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