Reputation-guided evolutionary scheduling algorithm for independent tasks in inter-clouds environments
Self-adaptation provides software with flexibility to different behaviours (configurations) it incorporates and the (semi-) autonomous ability to switch between these behaviours in response to changes. To empower clouds with the ability to capture and respond to quality feedback provided by users at...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/79709 |
| Acceso en línea: | https://hdl.handle.net/2117/79709 https://dx.doi.org/10.1504/IJWGS.2015.067159 |
| Access Level: | acceso abierto |
| Palabra clave: | Cloud computing Computer algorithms scheduling algorithms cloud computing optimisation metrics reputation evolutionary computing computing systems Computació en núvol Algorismes computacionals Àrees temàtiques de la UPC::Informàtica::Programació |
| Sumario: | Self-adaptation provides software with flexibility to different behaviours (configurations) it incorporates and the (semi-) autonomous ability to switch between these behaviours in response to changes. To empower clouds with the ability to capture and respond to quality feedback provided by users at runtime, we propose a reputation guided genetic scheduling algorithm for independent tasks. Current resource management services consider evolutionary strategies to improve the performance on resource allocation procedures or tasks scheduling algorithms, but they fail to consider the user as part of the scheduling process. Evolutionary computing offers different methods to find a near-optimal solution. In this paper we extended previous work with new optimisation heuristics for the problem of scheduling. We show how reputation is considered as an optimisation metric, and analyse how our metrics can be considered as upper bounds for others in the optimisation algorithm. By experimental comparison, we show our techniques can lead to optimised results. |
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