Using Free Cloud Storage Services For Distributed Evolutionary Algorithms Maribel García-Arenas, Juan-J. Merelo,  Antonio ...
Outline <ul><li>Idea and how to test it
Dropbox features
Putting in practice with Evolutionary Computation
File-individuals
Island Algorithm
Goals
Problems
Results </li></ul>
IDEA <ul><li>What do you know about cloud storage services?
Why not use them for computing?
How can we use all our computers to make a multicomputer? </li><ul><li>Desktop computer
Portable computer
Home computer
Any other computers... </li></ul></ul>
How to test the idea <ul><li>Look for some  free  storage services and test them: What are their  features  and what is th...
After that, We have selected  Dropbox   </li></ul>
Dropbox  TM  features <ul><li>It is free  up to a certain level of use (measured in traffic and usage)
It is popular , so many people use it, and we may found many volunteers for computation
It monitors  the local filesystem and uploads information asynchronously
It looks like a  local directory </li></ul>
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Presentation dropbox

  1. 1. Using Free Cloud Storage Services For Distributed Evolutionary Algorithms Maribel García-Arenas, Juan-J. Merelo, Antonio M. Mora, Pedro Castillo
  2. 2. Outline <ul><li>Idea and how to test it
  3. 3. Dropbox features
  4. 4. Putting in practice with Evolutionary Computation
  5. 5. File-individuals
  6. 6. Island Algorithm
  7. 7. Goals
  8. 8. Problems
  9. 9. Results </li></ul>
  10. 10. IDEA <ul><li>What do you know about cloud storage services?
  11. 11. Why not use them for computing?
  12. 12. How can we use all our computers to make a multicomputer? </li><ul><li>Desktop computer
  13. 13. Portable computer
  14. 14. Home computer
  15. 15. Any other computers... </li></ul></ul>
  16. 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. 17. After that, We have selected Dropbox </li></ul>
  18. 18. Dropbox TM features <ul><li>It is free up to a certain level of use (measured in traffic and usage)
  19. 19. It is popular , so many people use it, and we may found many volunteers for computation
  20. 20. It monitors the local filesystem and uploads information asynchronously
  21. 21. It looks like a local directory </li></ul>
  22. 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. 23. Let's go <ul><li>File distribution via Dropbox
  24. 24. It synchronizes the file-individuals with other computers
  25. 25. Each computer evolves an island
  26. 26. Dropbox folder contains a pool of individuals and each computer adds and gets file-individuals from it </li></ul>
  27. 27. Let's go (II) <ul><li>Each computer connected or synchronized by Dropbox is part of a multi-computer
  28. 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. 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. 30. Into the filesystem attributes ? Dropbox is working on that and we will be testing in the future
  31. 31. Into the filename ? It is our approach </li></ul></ul>
  32. 32. File-individuals (II) <ul><li>The filename problem </li><ul><li>How many gens can we include into the name?
  33. 33. We have to code the genotype into base 32
  34. 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. 35. Island Algorithm <ul><li>Creates and evaluates the initial population
  36. 36. Until to reach a number of evaluations into the multi-computer </li></ul><ul><ul><li>Breed the population
  37. 37. Evaluate
  38. 38. Generational replacement with 1-elitism
  39. 39. After a fixed number of generations, Immigrate (gets one file-individual from the pool and incorporates it to the population)
  40. 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. 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. 42. Each computer looks for this kind of file within the Dropbox folder and adds the total of evaluations.
  43. 43. When the sum of this evaluations is greater than the fixed minimum, the evolution of this island ends. </li></ul>
  44. 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. 45. Problems: MMDP <ul><li>Multimodal Deceptive Problem
  46. 46. It is composed of k (k=80) subproblems of 6 bits each one called s i for i=0 to 79 .
  47. 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. 48. Problems: TRAP <ul><li>It is defined for the unitation function (number of ones in a binary string) using the following function.
  49. 49. For our problem, the trap is defined for l=4, a=3, b=4 and z = 3
  50. 50. With 30 traps
  51. 51. into the genome </li></ul>{
  52. 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. 53. Population size: 1000 individuals
  54. 54. Selection: Tournament
  55. 55. Crossover: uniform
  56. 56. Mutation: bit-flit
  57. 57. Replacement: Generational with 1-elitism
  58. 58. Stop criteria: minimum number of evaluations for the multi-computer
  59. 59. WiFi with WPA/Enterprise encryption. </li></ul>
  60. 60. Results for MMDP
  61. 61. Results for TRAP
  62. 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. 63. With this approach everyone can use a multicomputer running an evolutionary algorithm with a good scaling behavior. </li></ul>
  64. 64. Others results for MMDP
  65. 65. Questions

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