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  • 1. Using Free Cloud Storage Services For Distributed Evolutionary Algorithms Maribel García-Arenas, Juan-J. Merelo, Antonio M. Mora, Pedro Castillo
  • 2. Outline <ul><li>Idea and how to test it
  • 3. Dropbox features
  • 4. Putting in practice with Evolutionary Computation
  • 5. File-individuals
  • 6. Island Algorithm
  • 7. Goals
  • 8. Problems
  • 9. Results </li></ul>
  • 10. IDEA <ul><li>What do you know about cloud storage services?
  • 11. Why not use them for computing?
  • 12. How can we use all our computers to make a multicomputer? </li><ul><li>Desktop computer
  • 13. Portable computer
  • 14. Home computer
  • 15. Any other computers... </li></ul></ul>
  • 16. How to test the idea <ul><li>Look for some free storage services and test them: What are their features and what is the availability for storing, sharing and synchronizing information
  • 17. After that, We have selected Dropbox </li></ul>
  • 18. Dropbox TM features <ul><li>It is free up to a certain level of use (measured in traffic and usage)
  • 19. It is popular , so many people use it, and we may found many volunteers for computation
  • 20. It monitors the local filesystem and uploads information asynchronously
  • 21. It looks like a local directory </li></ul>
  • 22. Putting in practice with Evolutionary Computation <ul><li>What do we need to build Evolutionary Distributed Algorithms? </li><ul><li>Exchange individuals among populations: Phenotype and Genotype </li></ul><li>We can exchange this information using files. So the name of the file represents the phenotype and genotype and all connected PCs share it with Dropbox </li></ul>
  • 23. Let's go <ul><li>File distribution via Dropbox
  • 24. It synchronizes the file-individuals with other computers
  • 25. Each computer evolves an island
  • 26. Dropbox folder contains a pool of individuals and each computer adds and gets file-individuals from it </li></ul>
  • 27. Let's go (II) <ul><li>Each computer connected or synchronized by Dropbox is part of a multi-computer
  • 28. Each Island-computer evolves a population of individuals and exchanges with the pool file-individuals when the migration process must be done </li></ul>
  • 29. File-individuals <ul><li>How to include phenotype and genotype into a file </li><ul><li>As the contents of the file? It is not a good idea because we have to open and close files and Dropbox has to synchonize them.
  • 30. Into the filesystem attributes ? Dropbox is working on that and we will be testing in the future
  • 31. Into the filename ? It is our approach </li></ul></ul>
  • 32. File-individuals (II) <ul><li>The filename problem </li><ul><li>How many gens can we include into the name?
  • 33. We have to code the genotype into base 32
  • 34. Ex: 00000 -> 0, 00001-> 1, 01010->A ... 111111->V </li></ul><li>The filename includes : Fitness, genotypeBase32codification and the id of the computer which generates the individual </li></ul>
  • 35. Island Algorithm <ul><li>Creates and evaluates the initial population
  • 36. Until to reach a number of evaluations into the multi-computer </li></ul><ul><ul><li>Breed the population
  • 37. Evaluate
  • 38. Generational replacement with 1-elitism
  • 39. After a fixed number of generations, Immigrate (gets one file-individual from the pool and incorporates it to the population)
  • 40. After a fixed number of generations, Migrate (adds the best or a random file-individual to the pool) </li></ul></ul><ul><li>Adds the best individual to the pool </li></ul>
  • 41. Control of the number of evaluations <ul><li>Each computer creates a file whose name is the number of evaluations performed and its identification (random initial seed)
  • 42. Each computer looks for this kind of file within the Dropbox folder and adds the total of evaluations.
  • 43. When the sum of this evaluations is greater than the fixed minimum, the evolution of this island ends. </li></ul>
  • 44. Goals <ul><li>What do we want to test? </li><ul><li>We want test if we save time when use the multi-computer for computing a fixed number of evaluations. </li></ul><li>How can we test it? </li><ul><li>Making a distributed evolutionary algorithm based on pool and testing that the time for reaching the fixed evaluations decreases when you add new nodes to our multi-computer linked by Dropbox . </li></ul></ul>
  • 45. Problems: MMDP <ul><li>Multimodal Deceptive Problem
  • 46. It is composed of k (k=80) subproblems of 6 bits each one called s i for i=0 to 79 .
  • 47. Depending of the number of ones s i takes the values detailed into the table </li></ul>ones fitness 0 or 6 1 5 or 1 0 2 or 4 0,360384 3 0,640576
  • 48. Problems: TRAP <ul><li>It is defined for the unitation function (number of ones in a binary string) using the following function.
  • 49. For our problem, the trap is defined for l=4, a=3, b=4 and z = 3
  • 50. With 30 traps
  • 51. into the genome </li></ul>{
  • 52. Parameters <ul><li>We use as multi-computer one, two or four heterogeneous computers so we use one, two, three or four island
  • 53. Population size: 1000 individuals
  • 54. Selection: Tournament
  • 55. Crossover: uniform
  • 56. Mutation: bit-flit
  • 57. Replacement: Generational with 1-elitism
  • 58. Stop criteria: minimum number of evaluations for the multi-computer
  • 59. WiFi with WPA/Enterprise encryption. </li></ul>
  • 60. Results for MMDP
  • 61. Results for TRAP
  • 62. Conclusions <ul><li>The Dropbox File-storage and sharing system, can be used as a migration device for distributed evolutionary computation experiments without needing to acquire or set up complicated cloud or grid infrastructure.
  • 63. With this approach everyone can use a multicomputer running an evolutionary algorithm with a good scaling behavior. </li></ul>
  • 64. Others results for MMDP
  • 65. Questions