RT Conference Proceedings
T1 A Model Driven Approach for Supporting the Cloud Target Selection Process
A1 Kopaneli, Aliki
A1 Kousiouris, George
A1 Velez, Gorka Echevarria
A1 Evangelinou, Athanasia
A1 Varvarigou, Theodora
AB The decision making process for the selection of one cloud target over another plays a major role during the migration to the Cloud, affecting not only the operational costs, functional characteristics and QoS, but also the development, monitoring and maintaining experience of the IT professionals. As the Cloud gains ground, a progressively growing number of cloud providers, services and technologies are exposed in the market rendering the research and selection upon them complex and time consuming. Proposed efforts for automatic support, fail to follow the quick paste of evolution, demanding, thus, even more effort for maintaining the supporting systems. In this paper the Cloud Target Selection (CTS) tool methodology and prototype implementation are presented introducing a novel approach: The CloudML@artist modeling language is exploited as a representation of real-world cloud environments becoming a source of information for an extensible decision making mechanism. The proposed work contributes in the direction towards the construction of an adaptive solution, which will follow the technological advances requiring the minimum of human intervention.
SN 1877-0509
YR 2015
FD 2015
LK https://hdl.handle.net/11556/3258
UL https://hdl.handle.net/11556/3258
LA eng
NO Kopaneli , A , Kousiouris , G , Velez , G E , Evangelinou , A & Varvarigou , T 2015 , ' A Model Driven Approach for Supporting the Cloud Target Selection Process ' , Procedia Computer Science , vol. 68 , pp. 89-102 . https://doi.org/10.1016/j.procs.2015.09.226
NO Publisher Copyright: © 2015 The Authors.
NO The research leading to these results is partially supported by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 317859, in the context of the ARTIST Project.
DS TECNALIA Publications
RD 28 jul 2024