Internet of Things (IoT) based applications have recently experienced a remarkable diffusion in many different contexts, such as automotive, e-health, public security, industrial applications, energy, and waste management. These kinds of applications are characterized by geographically distributed sensors that collect data to be processed through algorithms of Artificial Intelligence (AI). Due to the vast amount of data to be processed by AI algorithms and the severe latency requirements of some applications, the emerging Edge Computing paradigm may represent the preferable choice for the supporting infrastructure. However, the design of edge computing infrastructures opens several new issues concerning the allocation of data flows coming from sensors over the edge nodes, and the choice of the number and the location of the edge nodes to be activated. The service placement issue can be modeled through a multi-objective optimization aiming at minimizing two aspects: the response time for data transmission and processing in the sensors-edge-cloud path; the (energy or monetary) cost related to the number of turned on edge nodes. Two heuristics, based on Variable Neighborhood Search and on Genetic Algorithms, are proposed and evaluated over a wide range of scenarios, considering a realistic smart city application with 100 sensors and up to 10 edge nodes. Both heuristics can return practical solutions for the given application. The results indicate a suitable topology for a network-bound scenario requires less enabled edge nodes comparatively with a CPU-bound scenario. In terms of performance gain, the VNS outperformed in almost every condition the GA approach, reaching a performance gain up to almost 40% when the network delay plays a significant role and when the load is higher. Hence, the experimental tests demonstrate that the proposed heuristics are useful to support the design of edge computing infrastructures for modern AI-based applications relying on data collected by geographically distributed IoT sensors.

An Optimization View to the Design of Edge Computing Infrastructures for IoT Applications / de Queiroz, Thiago Alves; Canali, Claudia; Iori, Manuel; Lancellotti, Riccardo. - (2021), pp. 1-30. [10.1007/978-3-030-80821-1_1]

An Optimization View to the Design of Edge Computing Infrastructures for IoT Applications

Canali, Claudia
;
Iori, Manuel;Lancellotti, Riccardo
2021

Abstract

Internet of Things (IoT) based applications have recently experienced a remarkable diffusion in many different contexts, such as automotive, e-health, public security, industrial applications, energy, and waste management. These kinds of applications are characterized by geographically distributed sensors that collect data to be processed through algorithms of Artificial Intelligence (AI). Due to the vast amount of data to be processed by AI algorithms and the severe latency requirements of some applications, the emerging Edge Computing paradigm may represent the preferable choice for the supporting infrastructure. However, the design of edge computing infrastructures opens several new issues concerning the allocation of data flows coming from sensors over the edge nodes, and the choice of the number and the location of the edge nodes to be activated. The service placement issue can be modeled through a multi-objective optimization aiming at minimizing two aspects: the response time for data transmission and processing in the sensors-edge-cloud path; the (energy or monetary) cost related to the number of turned on edge nodes. Two heuristics, based on Variable Neighborhood Search and on Genetic Algorithms, are proposed and evaluated over a wide range of scenarios, considering a realistic smart city application with 100 sensors and up to 10 edge nodes. Both heuristics can return practical solutions for the given application. The results indicate a suitable topology for a network-bound scenario requires less enabled edge nodes comparatively with a CPU-bound scenario. In terms of performance gain, the VNS outperformed in almost every condition the GA approach, reaching a performance gain up to almost 40% when the network delay plays a significant role and when the load is higher. Hence, the experimental tests demonstrate that the proposed heuristics are useful to support the design of edge computing infrastructures for modern AI-based applications relying on data collected by geographically distributed IoT sensors.
30-lug-2021
Artificial Intelligence for Cloud and Edge Computing
978-3-030-80820-4
978-3-030-80821-1
An Optimization View to the Design of Edge Computing Infrastructures for IoT Applications / de Queiroz, Thiago Alves; Canali, Claudia; Iori, Manuel; Lancellotti, Riccardo. - (2021), pp. 1-30. [10.1007/978-3-030-80821-1_1]
de Queiroz, Thiago Alves; Canali, Claudia; Iori, Manuel; Lancellotti, Riccardo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1258281
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