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Is there a free lunch for cloud-based evolutionary algorithms?
- 1. Is there a free lunch for cloud-based evolutionary algorithms? Mario García-Valdez, Alejandra Mancilla and Leonardo Trujillo Inst. Tec. de Tijuana, México Juan-J. Merelo University of Granada, Spain 2013 IEEE Congress on Evolutionary Computation Cancún, México June 20-23, 2013 Francisco Fernández-de-Vega Universidad de Extremadura, Spain 1 1
- 2. Are you hungry? 2 How much computing power do you need? 2
- 4. 4 When you’re done, do you share? 4
- 5. “The Network is the Computer” John Gage, Sun Microsystems 5 5
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- 8. 8 8
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- 14. Recently there has been several efforts to exploit the computing resources available in the Internet for Evolutionary Computation (EC) research. 10 10
- 16. Users share their computing resources Through downloaded software: N. Cole et al. “Evolutionary algorithms on volunteer computing platforms” 12 12
- 17. Through their browsers: J. Merelo et al. “Asynchronous distributed genetic algorithms with Javascript and JSON,” Users share their computing resources 13 13
- 18. Cloud Computing Some services share their computing resources. Di Martino et al. uses the Google App Engine platform to implement an island model Genetic Algorithm. Garcia-‐Arenas et al. used Dropbox as a cloud-‐based storage server for a pool-‐based EA 14 14
- 19. Cloud Computing Some research is currently oriented towards the development of cloud based EAs and libraries. Tulum Clouds 15 15
- 20. Pool Based A-‐Teams system (S. Talukdar et al.) A distributed pool architecture for GAs (Roy et al. ) SOFEA (Merelo et al.) EvoSpace (García et al.) Map-Reduce FlexGP (Sherry et al.) EAs using Map/Reduce (Fazenda et al.) 16 16
- 21. In this paper we present a distributed evolutionary algorithm using exclusively low-‐cost cloud services. EvoSpace is tested using the free tier of two services (one for the pool and other for the clients) and also the paying tier, and speedup is measured and its limits assessed. 17 17
- 22. EvoSpace 1. The EvoSpace container that stores the evolving population. 2. Remote clients called EvoWorkers, which execute the actual evolutionary process. EvoSpace consists of two main components : 18 18
- 42. evospace phantoms from s1 are deleted evoWorker 2 evoWorker 1 25 25
- 43. evospace phantoms from s1 are deleted evoWorker 2 evoWorker 1 25 25
- 48. evospace re_insert( ) evoWorker 2 evoWorker 1 27 27
- 49. evospace re_insert( ) evoWorker 2 evoWorker 1 27 27
- 57. EvoSpace 30 30
- 58. Implementation Redis evospace.py cherrypy javascript Python c# Objective-C Others json-rpc application-server redis-server process python 31 31
- 59. Implementation Redis evospace.py cherrypy javascript Python c# Objective-C Others json-rpc application-server redis-server process python core 31 31
- 60. Implementation Redis evospace.py cherrypy javascript Python c# Objective-C Others json-rpc application-server redis-server process python 31 31
- 61. Advanced key-‐value store Keys can contain strings, hashes,lists, sets, sorted sets. In-‐memory but persistent on disk database. Single threaded. Master-‐slave replication. 32 32
- 62. Efficient implementation of Set operations needed for evospace: Add, remove, and test for existence of members in O(1). Returning and removing a random member from a set is also O(1). 33 33
- 63. Each individual is a unique key and a JSON object string. ind:123 -‐> { chromosome:[0,1,0,1], fitness:0.2} A Population is a set of keys. pop:1-‐> {ind:123, ind:124, ind:13, ind:132} Samples are also sets. Re-‐Insertion is a union of sets. 34 34
- 64. EvoSpace Use Case Can be used in Interactive Evolution Algorithms: Remote Clients EvoWorker EvoSpace Re-Insertion Manager Server Get Sample1 Feed to Processing Script 2User evaluates Representations 3 Put Sample Back 4 After n evaluations Evolve a sample 5 35 35
- 65. Implementation Redis evospace.py cherrypy javascript Python c# Objective-C Others json-rpc application-server redis-server process python 36 36
- 66. Implementation Redis evospace.py cherrypy javascript Python c# Objective-C Others json-rpc application-server redis-server process python evoWorkers 36 36
- 67. evoWorkers 1. Local processes. 2. Html-‐5 Web Workers in FireFox Browsers. 3. Processes in Virtual Machines. First implementations of EvoWorkers 37 37
- 68. EvoSpace Based on Tuple Spaces. Simple Model. Inherent load-‐balancing mechanism. Provides mutual exclusion. Scalable. Variable population size. 38 38
- 70. Heroku Heroku is a multi-‐language PaaS, supporting among others Ruby, Python and Java applications. The basic unit of composition on Heroku is a Dyno, a lightweight container running a single user-‐specified process. These process types are the prototypes from which one or more dynos can be instantiated. Redis Add-On cherryPy Service 40 40
- 71. Heroku Heroku is a multi-‐language PaaS, supporting among others Ruby, Python and Java applications. The basic unit of composition on Heroku is a Dyno, a lightweight container running a single user-‐specified process. These process types are the prototypes from which one or more dynos can be instantiated. Redis Add-On cherryPy Service cherryPy Service 40 40
- 72. Heroku Heroku is a multi-‐language PaaS, supporting among others Ruby, Python and Java applications. The basic unit of composition on Heroku is a Dyno, a lightweight container running a single user-‐specified process. These process types are the prototypes from which one or more dynos can be instantiated. Redis Add-On cherryPy Service cherryPy Service cherryPy Service 40 40
- 73. PiCloud PiCloud is a platform specialized in high performance and scientific computing. PiCloud is a PaaS, with deep Python integration. Python functions are transparently uploaded to PiCLoud’s servers as units of computational work they call jobs . 41 41
- 74. PiCloud Jobs Standard: Jobs are always added to a queue; the time they remain on queue depends on available resources. Real-‐Time: Can be reserved (for a fee) for certain amount of time, when reserved, cores are available immediately. 42 42
- 75. Is there a free lunch? Limited by three budgets: $0.00, $10.00, $20.00 Free Options: Heroku gives 750 Dyno hours each month. Picloud gives for free 20 Compute hours a month. Notes: Heroku requires credit card verification for Add-‐Ons. PiCloud requires a reservation fee for Real Time cores. 43 43
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- 79. Kennedy and Spears proposed P=100 N=100 as a difficult configuration. Alba et al. [21] considered an instance with greater difficulty with P = 512 and N = 512. In this paper P = 256 peaks and N = 512 bits are used. Multimodal Problem Generator P-Peaks 47 47
- 80. 48 48
- 81. Population: 512 Sample Size: 16 2:480 4 : 448 8 : 384 16: 256 28: 64 49 49
- 82. 50 50
- 83. Conclusions PaaS can offer a transparent integration with current tools. Pool-‐Based EAs can be easily deployed. Cloud-‐Based alternatives can reduce computational costs of demanding EAs. 51 51
- 84. EvoSpace in Cloud Starvation or reduction of population size can be a problem. We have to adjust population size depending on the number of workers. Can be done automatically with a worker. Conclusions 52 52
- 85. Thank You Please visit www.evospace.org for more information. All code, data, matplotlib scripts, latex files with authors original comments and edits (in spanish) available in: https://github.com/mariosky/cec2013 53 53