Fog-oriented Hierarchical Resource Allocation Policy in Vehicular Clouds
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Data
2021-01-01
Autores
Pereira, Rickson S. [UNESP]
Gomides, Thiago S.
Quessada, Matheus S. [UNESP]
Meneguette, Rodolfo I.
Lieira, Douglas D. [UNESP]
Guidoni, Daniel L. [UNESP]
Nakamura, Luis H. V. [UNESP]
De Grande, Robson E. [UNESP]
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As we move more deeply into information-oriented services and systems, we clearly observe the importance and impact of smart and connected vehicles for urban computing. New Cloud-enabled paradigms have boosted information and service sharing. However, such paradigms rely heavily on the underlying communication layer, inheriting the challenges originated from the high mobility of vehicles. Several works have been devised to cope with highly dynamic vehicular environments in support of effective resource management and allocation, which we discuss in the paper. Moreover, we propose a Fog paradigm solution to resource allocation using a hierarchical method in vehicular clouds. Our method is based on the Multiplicative Analytic Hierarchy Process (MAHP) proposed by Lootsma. MAHP is a branch of another method called Analytic Hierarchy Process proposed by Saaty. Therefore, we used MAHP in the decision-making of the resource allocation process using a Fog paradigm to select the best Fog to allocate certain services. We evaluated the proposed solution comparing to three other decision methods, GREEDY, RANDOM, and RELIABLE. The proposed Fog-oriented Hierarchical Resource Allocation Policy in Vehicular Clouds (FRACTAL) performed better than the other decision methods, fulfilling more services and consequently denying fewer services.
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Fog Computing, Resource Allocation, Vehicular Cloud Computing
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Proceedings - 17th Annual International Conference on Distributed Computing in Sensor Systems, DCOS 2021, p. 212-219.