2026-04-23T05:28:56Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/190872026-02-03T12:05:09Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Cañete Valverde, Ángel Jesús
author
Amor-Pinilla, María Mercedes
author
Fuentes-Fernández, Lidia
2019-12-18T11:52:36Z
2019-12-18T11:52:36Z
2019-12-18
https://hdl.handle.net/10630/19087
In the last few years, the number of devices connected to the Internet has increased
considerably; so has the data interchanged between these devices and the Cloud, as well as energy
consumption and the risk of network congestion. The problem can be alleviated by reducing
communication between Internet-of-Things devices and the Cloud. Recent paradigms, such as Edge
Computing and Fog Computing, propose to move data processing tasks from the Cloud to nearby
devices to where data is produced or consumed. One of the main challenges of these paradigms is to
cope with the heterogeneity of the infrastructures where tasks can be offloaded. This paper presents a
solution for the optimal allocation of computational tasks to edge devices, with the aim of minimizing
the energy consumption of the overall application. The heterogeneity is represented and managed
by using Feature Models, widely employed in Software Product Lines. Given the application and
infrastructure configurations, our Optimal Tasks Assignment Framework generates the optimal task
allocation and resources assignment. The resultant deployment represents the most energy efficient
configuration at load-time, without compromising the user experience. The scalability and energy
saving of the approach are evaluated in the domain of augmented reality applications
eng
Programación ubicua
Internet de las Cosas
Optimal Assignment of Augmented Reality Tasks for Edge-Based Variable Infrastructures
conference output
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URL
https://riuma.uma.es/bitstreams/0b09e7b6-51c3-4f24-a9b4-0a6ae53e176c/download
File
MD5
5013103d4ac8be615a9994ed2e35d4c8
711550
application/pdf
proceedings-31-00028(1).pdf