TY - GEN
T1 - Akats
T2 - 8th International Conference on Smart and Sustainable Technologies, SpliTech 2023
AU - Diaz-De-Arcaya, Josu
AU - Torre-Bastida, Ana I.
AU - Bonilla, Lander
AU - López-De-Armentia, Juan
AU - Miñón, Raúl
AU - Zarate, Gorka
AU - Almeida, Aitor
N1 - Publisher Copyright:
© 2023 University of Split, FESB.
PY - 2023
Y1 - 2023
N2 - Edge computing is a game changer for IoT, as it allows IoT devices to independently process and analyze data instead of just sending it to the cloud. But managing this considerable number of devices and deploying workloads on them in a coordinated and intelligent manner remains a challenge nowadays. In this paper, we focus on introducing the resilience dimension into these deployments, and we provide two main contributions: the use of federated machine learning techniques to develop a collaborative tool between the different devices aimed at detecting the possibility of a device failure, and subsequently, the utilization of the inferred information to optimize deployment plans ensuring the resilience in the devices. These two advances are implemented in an intelligent system, Akats, whose architecture is described in detail in this article. Finally, an application scenario is presented, based on Industry 4.0 - Machine predictive maintenance, to exemplify the benefits of the proposed intelligent system.
AB - Edge computing is a game changer for IoT, as it allows IoT devices to independently process and analyze data instead of just sending it to the cloud. But managing this considerable number of devices and deploying workloads on them in a coordinated and intelligent manner remains a challenge nowadays. In this paper, we focus on introducing the resilience dimension into these deployments, and we provide two main contributions: the use of federated machine learning techniques to develop a collaborative tool between the different devices aimed at detecting the possibility of a device failure, and subsequently, the utilization of the inferred information to optimize deployment plans ensuring the resilience in the devices. These two advances are implemented in an intelligent system, Akats, whose architecture is described in detail in this article. Finally, an application scenario is presented, based on Industry 4.0 - Machine predictive maintenance, to exemplify the benefits of the proposed intelligent system.
KW - AIOps
KW - Edge Computing
KW - Federated Machine Learning
KW - FML
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85168151731&partnerID=8YFLogxK
U2 - 10.23919/SpliTech58164.2023.10193302
DO - 10.23919/SpliTech58164.2023.10193302
M3 - Conference contribution
AN - SCOPUS:85168151731
T3 - 2023 8th International Conference on Smart and Sustainable Technologies, SpliTech 2023
BT - 2023 8th International Conference on Smart and Sustainable Technologies, SpliTech 2023
A2 - Solic, Petar
A2 - Nizetic, Sandro
A2 - Rodrigues, Joel J. P. C.
A2 - Rodrigues, Joel J. P. C.
A2 - Rodrigues, Joel J. P. C.
A2 - Lopez-de-Ipina Gonzalez-de-Artaza, Diego
A2 - Perkovic, Toni
A2 - Catarinucci, Luca
A2 - Patrono, Luigi
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 June 2023 through 23 June 2023
ER -
