1. Application of Evolutionary Algorithms for Energy Efficient Grooming of Scheduled Sub-Wavelength Traffic Demands in Optical Networks Ala Shaabana University of Windsor School of Computer Science 13-05-2013

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3. Outline Optical Communication Overview Motivation Solution Outline Evolutionary Algorithms Related Research Energy Minimization for Scheduled Traffic Results Future work 3

4. Optical Communication Communication at a distance to carry information using light. In 1880, earliest electrical device created to perform optical communication is created. – Photophone Optical communication today relies on optical fibers to carry the information from point A to point B. 4

5. Optical Communication Communication at a distance to carry information using light using optical fibers. Optical fibers: thin glass cylinders or filaments which carry signals in the form of light. A Lightpath (LP): end-to-end optical communication channel – may traverse multiple fibers. 5

6. Optical Communication An optical communication system uses: – Transmitter: encodes a message into an optical signal. – Channel: carries the signal to its destination – Receiver: reproduces the message from the received optical signal. A. Shaabana — M.Sc. Thesis Defense 6 May 13, 2013

7. Motivation Immense growth in high-bandwidth applications  increase in energy consumption. – 1% improvement in total energy consumption  $5M USD savings per year in electricity cost [1]. Existing approaches: put network interfaces and components to sleep [2], switch off line cards [3][4], or even entire links or nodes[5][6]. Our approach: consider the applications that require periodic use of bandwidth at predetermined times. – Unlike static or dynamic traffic demands, this type of demands (called scheduled traffic demands) is periodic and predictable – Resource allocation can be optimized in both space and time. 7

8. Example 8

9. Motivation Integer Linear Programs (ILPs) are typically the go-to solution for this kind of optimization problem. However, although ILPs achieve an optimal solution, however they become computationally intractable once the problem set becomes larger (larger network sizes). It ends up taking too long and sometimes consuming too many computational resources to find its solution. 9

10. Solution Outline We present a Genetic Algorithm (GA) and a Memetic Algorithm (MA) to route a set of periodic, sub- wavelength traffic demands over the network. The primary goal for these two approaches is to: 1)Route the traffic demands in such a way that the maximum number of LPs can be switched off, hence reducing the overall power consumption. 2)Reduce the total number of LPs needed to realize the logical topology, such that the capacity constraints of the LPs are not exceeded. 10

11. Solution Outline The GA has reduced the energy consumption more so than the simple shortest path holding-time-aware heuristic, which was presented in our previous work in [7]. In contrast, the MA was designed to build upon the GA and improve on it further by adding local search capabilities,. – Solutions similar to the GA with considerably less time. 11

12. Solution Outline Note however, that evolutionary algorithms themselves have been applied to many computational problems. Computer simulations of evolution started as early as 1954 with the work of Nils Aal Barricelli. In 1989, Moscato et al. proposed Memetic Algorithms based on Richard Dawkin’s notion of a Meme [8]. 12

13. Solution Outline At the time being, there are no applications of evolutionary algorithms to optimize scheduled demands in optical networks, and the current approach seems to be to use a holding-time unaware (HTU) approach. 13

14. A little Biology… “Evolution is the survival of the fittest” is a great description of many evolutionary computation systems. When one uses evolutionary computation to solve a problem, it operates on a population (or a collection) of data structures (or creatures/genes). This fundamental difference in the notion of fitness is a key difference between biological evolution and most evolutionary computation. 14

15. A little Biology… 2 opposing forces that drive evolution: – Variation: process that produces new alleles/genes – Selection: process whereby some alleles survive and others do not 15

16. A little Biology… Evolutionary computing can accomplish variation by making random changes in these data structures and by blending parts of different data structures via mutation and crossover (referred to as variation operators). There are good and bad mutations operating on a population of data structures. 16

17. Evolutionary Algorithms Nearly three decades of R&D have demonstrated that the mimicked search process of natural evolution can yield very robust and direct computer algorithms, even though these imitations are crude simplifications of biological reality. The result of these efforts is Evolutionary Algorithms (EAs). 17

18. Evolutionary Algorithms The population evolves towards improving regions of the search space by means of randomized processes of mutation, selection and recombination. The population is arbitrarily initialized. The environment delivers fitness information of the individuals, and the selection process favors those individuals of higher fitness to reproduce more often than worse individuals. The recombination mechanism allows for the mixing of parental information while passing it to their descendants, while mutation introduces innovation into the population. 18

19. Related Research A number of recent works focus on reducing power consumption in today’s core/transport networks. For example, Orgerie et al. (2012) developed an energy- efficient framework for Bulk Data transfers in dedicated networks with advance reservation. Meanwhile, Musumeci et al. (2012) evaluated the power consumption of the various devices used at different network architectures. Coiro et al. (2011) consider the dynamic traffic scenario, and propose an energy aware routing scheme to improve the energy efficiency by minimizing the number of active optical amplifiers in the network [5]. 19

20. Genetic Algorithms (GA) Most common of Evolutionary Algorithms. Generally, a population of candidate solutions to an optimization problem is evolved towards better solutions. Iteratively, the evolution typically starts from a population of randomly generated individuals, with the population in each generation referred to as a generation. The more fit individuals are stochastically selected from the current population The new generation of candidate solutions is then used in the next iteration of the algorithm. 20

21. Genetic Algorithms (GA) 21

22. Memetic Algorithms (MA) While GAs have been inspired in trying to emulate biological evolution, Memetic Algorithms (MAs) try to mimic cultural evolution. Essentially, MAs are a marriage between population- based global search and the local search heuristic made by each of the individuals. 22

23. Memetic Algorithms (MA) 23

24. Energy Minimization in Scheduled Traffic 24

25. Energy Minimization in Scheduled Traffic 25

26. Energy Minimization in Scheduled Traffic The goal is to route the traffic demands in such a way that the maximum number of lightpaths can be switched off at any given time, reducing the overall power consumption. We also try to implement each logical edge using as few lightpaths as possible. 26

27. Chromosome Representation 27

28. Fitness Function It is necessary to calculate the fitness value of each new individual after we generate it. We use the following fitness function: 28

29. Fitness Function 29

30. GA/MA Selection and Crossover The selection of individuals from the initial population as parents is carried out using the Roulette-Wheel selection method. We have used k-point crossover for each crossover operation in order to produce new offspring from the selected parents. 30

31. GA/MA Selection and Crossover 31

32. Mutation May 13, 2013

33. Local Search 33

34. Local Search When local search is implemented, it searches through the neighborhoods within population and chooses the most locally optimal chromosome from each neighborhood. That chromosome is then guaranteed to make it through to the next generation. The local search can be integrated within the evolutionary cycle mainly in two ways. 34

35. Local Search The first way is the application of the local search to a candidate solution, called lifetime learning. We have implemented our method using the second way. The application of the local search during the solution generation phase, that is, the generation of a perfect child. 35

36. Local Search Procedure 36

37. Local Search Procedure 37

38. Results 38

39. Results Although the GA is less computationally demanding than an ILP, an Amazon “Elastic Cloud” server with 8GB of RAM memory and 4 Amazon EC2 Compute Units (ECU) was required. – Where each ECU is equivalent to a 1.0 – 2.0GHz 2007 Opteron or Xeon processor). In contrast, the MA experiments, although operating on the same data sets, required only a 2GB RAM memory server and utilizing 1 Amazon ECU. 39

40. Results A. Shaabana — M.Sc. Thesis Defense 40 May 13, 2013 knowledge of demand holding times result in significant energy improvements over HTU approaches (26%-40%). Additional improvements (8% - 13%) compared to the HTA shortest path heuristic in [8]. MA we can reduce the amount of computational resources and time used while achieving similar results.

41. Results The simulation results show that knowledge of demand holding times result in significant energy improvements over HTU approaches. The proposed GA leads to further improvements compared to the HTA shortest path heuristic we have proposed in [8]. Interestingly, they also demonstrate that using the MA we can reduce the amount of computational resources and time used while similar outputs to the solutions presented by the GA. 41

42. Results Specifically, knowledge of demand holding times significantly reduced energy consumption, with improvements between 26% and 40%, even using a simple shortest path routing approach. This reduction is achieved by simply switching off lightpaths when they are not carrying any traffic. 42

43. GA vs. MA Results 43

44. GA vs. MA Results 44

45. GA vs. MA Results 45

46. GA vs. MA Results 46

47. GA vs. MA Results 47

48. GA vs. MA Results 48

49. GA vs. MA Results 49

50. Chronological Analysis 50

51. Chronological Analysis We speculate that this is not due to the MA doing less computations than the GA. Likely due to the MA’s capability of detecting when there are no more better solutions to be obtained. – This causes it to stop, saving multiple useless computations that would lead to the same solution. 51

52. Future Work While the proposed GA and MA perform better than ILPs in terms of computational time and resources, there is still potential for future improvement. One of the fundamental strengths of GAs and MAs is the diversity of their parameters. It is very possible to achieve better results after optimizing the parameters for specific topologies. 52

53. Future Work MAs in particular have great potential in energy optimization problems in optical networks. – In terms of parameters, the way the local search mechanism defines neighborhoods can be changed to something more complex than “adjacent chromosomes”. – While this is a common and viable implementation, it may not be the most optimized option for our purposes. 53

54. Future Work We have also only used one of many “move” mechanisms in Local Search, there are countless other algorithms that can be explored and exploited in order to achieve more optimized results. 54

55. Future Work With respect to our current implementation, more experimentation and data analysis should be applied, not only with different size and complexity of instances of networks, but also with other network optimization problems, as this has proven to be a promising direction in optical network optimization problems. 55

56. Thank you for listening. 56

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