dc.contributor.author |
Vilaplana Mayoral, Jordi |
dc.contributor.author |
Solsona Tehàs, Francesc |
dc.contributor.author |
Abella i Pons, Francesc |
dc.contributor.author |
Filgueira, Rosa |
dc.contributor.author |
Rius Torrentó, Josep Maria |
dc.date |
2015-06-26T09:36:01Z |
dc.date |
2015-06-26T09:36:01Z |
dc.date |
2013 |
dc.identifier |
1472-6947 |
dc.identifier |
http://hdl.handle.net/10459.1/48381 |
dc.identifier |
https://doi.org/10.1186/1472-6947-13-35 |
dc.identifier.uri |
http://hdl.handle.net/10459.1/48381 |
dc.description |
Background: Cloud computing is a new paradigm that is changing how enterprises, institutions and people
understand, perceive and use current software systems. With this paradigm, the organizations have no need to
maintain their own servers, nor host their own software. Instead, everything is moved to the cloud and provided on
demand, saving energy, physical space and technical staff. Cloud-based system architectures provide many
advantages in terms of scalability, maintainability and massive data processing.
Methods: We present the design of an e-health cloud system, modelled by an M/M/m queue with QoS capabilities,
i.e. maximum waiting time of requests.
Results: Detailed results for the model formed by a Jackson network of two M/M/m queues from the queueing
theory perspective are presented. These results show a significant performance improvement when the number of
servers increases.
Conclusions: Platform scalability becomes a critical issue since we aim to provide the system with high Quality of
Service (QoS). In this paper we define an architecture capable of adapting itself to different diseases and growing
numbers of patients. This platform could be applied to the medical field to greatly enhance the results of those
therapies that have an important psychological component, such as addictions and chronic diseases. |
dc.language |
eng |
dc.publisher |
BioMed Central |
dc.relation |
Reproducció del document publicat a https://doi.org/10.1186/1472-6947-13-35 |
dc.relation |
BMC Medical Informatics and Decision Making, 2013, vol. 13, p. 1-10 |
dc.rights |
cc-by, (c) Vilaplana et al., 2013 |
dc.rights |
http://creativecommons.org/licenses/by/3.0/es/ |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.subject |
Cloud systems |
dc.subject |
e-Health |
dc.subject |
Queue systems |
dc.title |
The cloud paradigm applied to e-Health |
dc.type |
article |
dc.type |
publishedVersion |