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Performance for the Masses: Experiments with A Web Based Architecture to Harness Volunteer Resources for Low Cost Distributed Evolutionary Computation

Published: 20 July 2016 Publication History

Abstract

Using volunteer's browsers as a computing resource presents several advantages, but it remains a challenge to fully harness the browser's capabilities and to model the user's behavior so that those capabilities can be leveraged optimally. These are the objectives of this paper, where we present the results of several evolutionary computation experiments with different implementations of a volunteer computing framework called NodIO, designed to be easily deployable on freely available cloud resources. We use different implementations to find out which one is able to get the user to lend more computing cycles and test different problems to check the influence it has on said performance, as measured by the time needed to find a solution, but also by the number of users engaged. From these experiments we can already draw some conclusions, besides the fact that volunteer computing can be a valuable computing resource and that it is essential to be as open as possible with software and data: the user has to be kept engaged to obtain as many computing cycles as possible, the client has to be built to use the computer capabilities fully, and, finally, that the user contributions follow a common statistical distribution.

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Cited By

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  • (2022)A Novel Distributed Nature-Inspired Algorithm for Solving Optimization ProblemsNew Perspectives on Hybrid Intelligent System Design based on Fuzzy Logic, Neural Networks and Metaheuristics10.1007/978-3-031-08266-5_8(107-119)Online publication date: 1-Oct-2022
  • (2018)Increasing Performance via Gamification in a Volunteer-Based Evolutionary Computation SystemInformation Processing and Management of Uncertainty in Knowledge-Based Systems. Applications10.1007/978-3-319-91479-4_29(342-353)Online publication date: 18-May-2018
  • (2017)Exploiting the social graph: Increasing engagement in a collaborative Interactive Evolution application2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969385(749-756)Online publication date: Jun-2017
  • Show More Cited By

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cover image ACM Conferences
GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference 2016
July 2016
1196 pages
ISBN:9781450342063
DOI:10.1145/2908812
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 20 July 2016

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Author Tags

  1. cloud computing
  2. distributed computing
  3. volunteer computing

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  • Research-article

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  • CONACYT
  • MECYT

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GECCO '16
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GECCO '16: Genetic and Evolutionary Computation Conference
July 20 - 24, 2016
Colorado, Denver, USA

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GECCO '16 Paper Acceptance Rate 137 of 381 submissions, 36%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2022)A Novel Distributed Nature-Inspired Algorithm for Solving Optimization ProblemsNew Perspectives on Hybrid Intelligent System Design based on Fuzzy Logic, Neural Networks and Metaheuristics10.1007/978-3-031-08266-5_8(107-119)Online publication date: 1-Oct-2022
  • (2018)Increasing Performance via Gamification in a Volunteer-Based Evolutionary Computation SystemInformation Processing and Management of Uncertainty in Knowledge-Based Systems. Applications10.1007/978-3-319-91479-4_29(342-353)Online publication date: 18-May-2018
  • (2017)Exploiting the social graph: Increasing engagement in a collaborative Interactive Evolution application2017 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2017.7969385(749-756)Online publication date: Jun-2017
  • (2017)Time series forecasting using evolutionary neural nets implemented in a volunteer computing systemIntelligent Systems in Accounting, Finance and Management10.1002/isaf.140924:2-3(87-95)Online publication date: 8-Aug-2017

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