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Application of Micro-Genetic Algorithm for Task Based Computing

Abstract : Pervasive computing calls for applications which are often composed from independent and distributed components using facilities from the environment. This paradigm has evolved into task based computing where the application composition relies on explicit user task descriptions. The composition of applications has to be performed at run-time as the environment is dynamic and heterogeneous due to e.g., mobility of the user. An algorithm that decides on a component set and allocates it onto hosts accordingly to user task preferences and the platform constraints plays a central role in the application composition process. In this paper we will describe an algorithm for task-based application allocation. The algorithm uses micro-genetic approach and is characterized by a very low computational load and good convergence properties. We will compare the performance and the scalability of our algorithm with a straightforward evolutionary algorithm. Besides, we will outline a system for task-based computing where our algorithm is used.
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https://hal.inria.fr/inria-00400321
Contributor : Oleg Davidyuk <>
Submitted on : Tuesday, June 30, 2009 - 3:06:05 PM
Last modification on : Thursday, May 2, 2019 - 2:52:08 PM
Long-term archiving on: : Tuesday, June 15, 2010 - 7:08:09 PM

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  • HAL Id : inria-00400321, version 1

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Oleg Davidyuk, Istvan Selek, Josu Ceberio, Jukka Riekki. Application of Micro-Genetic Algorithm for Task Based Computing. 1st International IEEE Conference on Intelligent Pervasive Computing (IPC'07), Oct 2007, Jeju city, South Korea. pp.140-145. ⟨inria-00400321⟩

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