The growing popularity of the Fog Computing paradigm is driven by the increasing availability of large amount of sensors and smart devices on a geographically distributed area. The scenario of a smart city is a clear example of this trend. As we face an increasing presence of sensors producing a huge volume of data, the classical cloud paradigm, with few powerful data centers that are far away from the data sources, becomes inadequate. There is the need to deploy a highly distributed layer of data processors that filter, aggregate and pre-process the incoming data according to a fog computing paradigm. However, a fog computing architecture must distribute the incoming workload over the fog nodes to minimize communication latency while avoiding overload. In the present paper we tackle this problem in a twofold way. First, we propose a formal model for the problem of mapping the data sources over the fog nodes. The proposed optimization problem considers both the communication latency and the processing time on the fog nodes (that depends on the node load). Furthermore, we propose a heuristic, based on genetic algorithms to solve the problem in a scalable way. We evaluate our proposal on a geographic testbed that represents a smart-city scenario. Our experiments demonstrate that the proposed heuristic can be used for the optimization in the considered scenario. Furthermore, we perform a sensitivity analysis on the main heuristic parameters.

A fog computing service placement for smart cities based on genetic algorithms / Canali, C.; Lancellotti, R.. - (2019), pp. 81-89. (Intervento presentato al convegno 9th International Conference on Cloud Computing and Services Science, CLOSER 2019 tenutosi a grc nel 2019) [10.5220/0007699400810089].

A fog computing service placement for smart cities based on genetic algorithms

Canali C.;Lancellotti R.
2019

Abstract

The growing popularity of the Fog Computing paradigm is driven by the increasing availability of large amount of sensors and smart devices on a geographically distributed area. The scenario of a smart city is a clear example of this trend. As we face an increasing presence of sensors producing a huge volume of data, the classical cloud paradigm, with few powerful data centers that are far away from the data sources, becomes inadequate. There is the need to deploy a highly distributed layer of data processors that filter, aggregate and pre-process the incoming data according to a fog computing paradigm. However, a fog computing architecture must distribute the incoming workload over the fog nodes to minimize communication latency while avoiding overload. In the present paper we tackle this problem in a twofold way. First, we propose a formal model for the problem of mapping the data sources over the fog nodes. The proposed optimization problem considers both the communication latency and the processing time on the fog nodes (that depends on the node load). Furthermore, we propose a heuristic, based on genetic algorithms to solve the problem in a scalable way. We evaluate our proposal on a geographic testbed that represents a smart-city scenario. Our experiments demonstrate that the proposed heuristic can be used for the optimization in the considered scenario. Furthermore, we perform a sensitivity analysis on the main heuristic parameters.
2019
9th International Conference on Cloud Computing and Services Science, CLOSER 2019
grc
2019
81
89
Canali, C.; Lancellotti, R.
A fog computing service placement for smart cities based on genetic algorithms / Canali, C.; Lancellotti, R.. - (2019), pp. 81-89. (Intervento presentato al convegno 9th International Conference on Cloud Computing and Services Science, CLOSER 2019 tenutosi a grc nel 2019) [10.5220/0007699400810089].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1185779
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