RT info:eu-repo/semantics/article
T1 Designing an efficient clustering strategy for combined Fog-to-Cloud scenarios
A1 Asensio, Adrián
A1 Masip-Bruin, Xavi
A1 Durán Barroso, Ramón José
A1 Miguel Jiménez, Ignacio de
A1 Ren, Guang-Jie
A1 Daijavad, Shahrokh
A1 Jukan, Admela
K1 Machine learning
K1 Aprendizaje automático
AB The evolution of the Internet of Things (IoT) is imposing many distinct challenges, particularly to guarantee both wide and global systems communication, and to ensure an optimal execution of services. To that end, IoT services must make the most out of both cloud and fog computing (turning into combined fog–cloud scenarios), which indeed requires novel and efficient resource management procedures to handle such diversity of resources in a coordinated way. Most of the related works that can be found in the literature focus on resource mapping for service-specific execution in fog–cloud; however, those works assume a control and management architecture already deployed. Interestingly, few works propose algorithms to set that control architecture, necessary to execute the services and effectively implement services and resource mapping. This paper addresses that challenge by solving the problem of optimal clustering of devices located at the edge of the network offering their resources to support fog computing while defining the control and management role of each of them in the architecture, in order to ensure access to management functions in combined fog–cloud​ scenarios. In particular, we set out the Fog–Cloud Clustering (FCC) problem as an optimization problem, which is based on multi-objective optimization, including realistic, novel and stringent constraints; e.g., to improve the architecture’s robustness by means of a device acting as a backup in the cluster. We model the FCC problem as a Mixed Integer Linear Programming (MILP) formulation, for which a lower and an upper bound on the number of required clusters is derived. We also propose a machine learning-based heuristic that provides scalable and near-optimal solutions in realistic scenarios in which, due to the high number of connected devices, solving the MILP formulation is not viable. By means of a simulation study, we demonstrate the effectiveness of the algorithms comparing its results with those of MILP formulation.
PB Elsevier
SN 0167-739X
YR 2020
FD 2020
LK https://uvadoc.uva.es/handle/10324/52824
UL https://uvadoc.uva.es/handle/10324/52824
LA eng
NO Future Generation Computer Systems, 2020, vol. 109, p. 392-406
NO Producción Científica
DS UVaDOC
RD 28-nov-2024