Brief Announcement: Achieving Reliability in Master-Worker Computing via Evolutionary Dynamics
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| Název: | Brief Announcement: Achieving Reliability in Master-Worker Computing via Evolutionary Dynamics |
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| Autoři: | Evgenia Christoforou, Miguel A. Mosteiro, Antonio Fernández, Anta Chryssis Georgiou, Angel (anxo Sánchez |
| Přispěvatelé: | The Pennsylvania State University CiteSeerX Archives |
| Zdroj: | http://www.cs.rutgers.edu/%7Emosteiro/pub/paperPODC12.pdf. |
| Sbírka: | CiteSeerX |
| Témata: | C.2.4 [Computer-Communication Networks, Distributed Systems General Terms Reliability, Algorithms Keywords Internet-based task computing, Evolutionary dynamics, Reinforcement learning, Algorithmic |
| Popis: | This work considers Internet-based task computations in which a master process assigns tasks, over the Internet, to rational workers and collect their responses. The objective is for the master to obtain the correct task outcomes. For this purpose we formulate and study the dynamics of evolution of Internet-based master-worker computations through reinforcement learning. Categories and Subject Descriptors |
| Druh dokumentu: | text |
| Popis souboru: | application/pdf |
| Jazyk: | English |
| Relation: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.261.1256; http://www.cs.rutgers.edu/%7Emosteiro/pub/paperPODC12.pdf |
| Dostupnost: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.261.1256 http://www.cs.rutgers.edu/%7Emosteiro/pub/paperPODC12.pdf |
| Rights: | Metadata may be used without restrictions as long as the oai identifier remains attached to it. |
| Přístupové číslo: | edsbas.6AC1EB7F |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | This work considers Internet-based task computations in which a master process assigns tasks, over the Internet, to rational workers and collect their responses. The objective is for the master to obtain the correct task outcomes. For this purpose we formulate and study the dynamics of evolution of Internet-based master-worker computations through reinforcement learning. Categories and Subject Descriptors |
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