RT Journal Article
T1 Energy efficient assignment and deployment of tasks in structurally variable infrastructures
A1 Cañete Valverde, Ángel Jesús
K1 Datos - Transmisión
K1 Proceso electrónico de datos
AB The importance of cyber-physical systems is growing very fast,being part of the Internet of Things vision. These devices generatedata that could collapse the network and can not be assumed by thecloud. New technologies like Mobile Cloud Computing and MobileEdge Computing are taking importance as solution for this issue.The idea is offloading some tasks to devices situated closer to theuser device, reducing network congestion and improving applicationsperformance (e.g., in terms of latency and energy). However,the variability of the target devices’ features and processing tasks’requirements is very diverse, being difficult to decide which deviceis more adequate to deploy and run such processing tasks. Oncedecided, task offloading used to be done manually. Then, it is necessarya method to automatize the task assignation and deploymentprocess. In this thesis we propose to model the structural variabilityof the deployment infrastructure and applications using featuremodels, on the basis of a SPL engineering process. Combining SPLmethodology with Edge Computing, the deployment of applicationsis addressed as the derivation of a product. The data of thevalid configurations is used by a task assignment framework, whichdetermines the optimal tasks offloading solution in different networkdevices, and the resources of them that should be assigned toeach task/user. Our solution provides the most energy and latencyefficient deployment solution, accomplishing the QoS requirementsof the application in the process.
YR 2019
FD 2019-10-04
LK https://hdl.handle.net/10630/18523
UL https://hdl.handle.net/10630/18523
LA eng
NO Plan Propio de Investigación de la UMAUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026