The trend of an ever-increasing number of geographically distributed sensors producing data for a plethora of applications, from environmental monitoring to smart cities and autonomous driving, is shifting the computing paradigm from cloud to fog. The increase in the volume of produced data makes the processing and the aggregation of information at a single remote data center unfeasible or too expensive, while latency-critical applications cannot cope with the high network delays of a remote data center. Fog computing is a preferred solution as latency-sensitive tasks can be moved closer to the sensors. Furthermore, the same fog nodes can perform data aggregation and filtering to reduce the volume of data that is forwarded to the cloud data centers, reducing the risk of network overload. In this paper, we focus on the problem of designing a fog infrastructure considering both the location of how many fog nodes are required, which nodes should be considered (from a list of potential candidates), and how to allocate data flows from sensors to fog nodes and from there to cloud data centers. To this aim, we propose and evaluate a formal model based on a multi-objective optimization problem. We thoroughly test our proposal for a wide range of parameters and exploiting a reference scenario setup taken from a realistic smart city application. We compare the performance of our proposal with other approaches to the problem available in literature, taking into account two objective functions. Our experiments demonstrate that the proposed model is viable for the design of fog infrastructure and can outperform the alternative models, with results that in several cases are close to an ideal solution.

A Location-allocation model for fog computing infrastructures / De Queiroz, T. A.; Canali, C.; Iori, M.; Lancellotti, R.. - (2020), pp. 253-260. ((Intervento presentato al convegno 10th International Conference on Cloud Computing and Services Science, CLOSER 2020 tenutosi a Online event due to COVID pandemic nel 2020 [10.5220/0009324702530260].

A Location-allocation model for fog computing infrastructures

Canali C.;Iori M.;Lancellotti R.
2020

Abstract

The trend of an ever-increasing number of geographically distributed sensors producing data for a plethora of applications, from environmental monitoring to smart cities and autonomous driving, is shifting the computing paradigm from cloud to fog. The increase in the volume of produced data makes the processing and the aggregation of information at a single remote data center unfeasible or too expensive, while latency-critical applications cannot cope with the high network delays of a remote data center. Fog computing is a preferred solution as latency-sensitive tasks can be moved closer to the sensors. Furthermore, the same fog nodes can perform data aggregation and filtering to reduce the volume of data that is forwarded to the cloud data centers, reducing the risk of network overload. In this paper, we focus on the problem of designing a fog infrastructure considering both the location of how many fog nodes are required, which nodes should be considered (from a list of potential candidates), and how to allocate data flows from sensors to fog nodes and from there to cloud data centers. To this aim, we propose and evaluate a formal model based on a multi-objective optimization problem. We thoroughly test our proposal for a wide range of parameters and exploiting a reference scenario setup taken from a realistic smart city application. We compare the performance of our proposal with other approaches to the problem available in literature, taking into account two objective functions. Our experiments demonstrate that the proposed model is viable for the design of fog infrastructure and can outperform the alternative models, with results that in several cases are close to an ideal solution.
10th International Conference on Cloud Computing and Services Science, CLOSER 2020
Online event due to COVID pandemic
2020
253
260
De Queiroz, T. A.; Canali, C.; Iori, M.; Lancellotti, R.
A Location-allocation model for fog computing infrastructures / De Queiroz, T. A.; Canali, C.; Iori, M.; Lancellotti, R.. - (2020), pp. 253-260. ((Intervento presentato al convegno 10th International Conference on Cloud Computing and Services Science, CLOSER 2020 tenutosi a Online event due to COVID pandemic nel 2020 [10.5220/0009324702530260].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1207852
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