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Applying the dynamics of evolution to achieve reliability in master–worker computing
Author(s) -
Christoforou Evgenia,
Anta Antonio Fernández,
Georgiou Chryssis,
Mosteiro Miguel A.,
Sánchez Angel
Publication year - 2013
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3104
Subject(s) - computer science , task (project management) , convergence (economics) , reliability (semiconductor) , computation , process (computing) , the internet , incentive , dynamics (music) , microeconomics , engineering , algorithm , world wide web , psychology , pedagogy , power (physics) , physics , quantum mechanics , systems engineering , economics , economic growth , operating system
SUMMARY We consider Internet‐based master–worker task computations, such as SETI@home, where a master process sends tasks, across the Internet, to worker processes; workers execute and report back some result. However, these workers are not trustworthy, and it might be at their best interest to report incorrect results. In such master–worker computations, the behavior and the best interest of the workers might change over time. We model such computations using evolutionary dynamics, and we study the conditions under which the master can reliably obtain task results. In particular, we develop and analyze an algorithmic mechanism based on reinforcement learning to provide workers with the necessary incentives to eventually become truthful. Our analysis identifies the conditions under which truthful behavior can be ensured and bounds the expected convergence time to that behavior. The analysis is complemented with illustrative simulations. Copyright © 2013 John Wiley & Sons, Ltd.