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Data Processing on Edge and Cloud: A Performability Evaluation and Sensitivity Analysis

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Abstract

Nowadays, the Internet of Things (IoT) allows monitoring and automation in diverse contexts, such as hospitals, homes, or even smart cities, just to name a few examples. IoT data processing may occur, at the edge of the network or in the cloud, but frequently the processing must be divided between the two layers. Aiming to guarantee that the IoT systems works efficiently, it is essential to evaluate the system even in initial design stages. However, evaluating hybrid systems composed by multiple layers is not an easy task as a myriad of parameters are involved in the process. Thus, this paper presents two SPN models (one base and extended one) that can represent an abstract distributed system composed of IoT, edge and cloud layers. The models are highly configurable to be used in diverse simulation scenarios. Besides a sensitivity analysis evidenced the most impacting components in the studied architecture and made it possible to optimize the base SPN model. Finally a case study explores multiple metrics of interest concurrently and works as a guide of the model utilization. Ultimately, the proposed approach can assist system designers to avoid unnecessary investment in original equipment.

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Authors and Affiliations

  1. Universidade Federal do Piauí- (UFPI), R. Cícero Duarte, no 905 - Junco, Picos , PI, 64607-670, Brazil

    Lucas Santos, Benedito Cunha & Francisco Airton Silva

  2. Departamento de Engenharia Informática, Universidade de Coimbra, Polo II - Pinhal de Marrocos, 3030-290, Coimbra, Portugal

    Iure Fé

  3. Exército Brasileiro, Terceiro BEC, Picos, Piauí, Brazil

    Marco Vieira

Authors
  1. Lucas Santos
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  2. Benedito Cunha
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  3. Iure Fé
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  4. Marco Vieira
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  5. Francisco Airton Silva
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Correspondence to Francisco Airton Silva.

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Santos, L., Cunha, B., Fé, I. et al. Data Processing on Edge and Cloud: A Performability Evaluation and Sensitivity Analysis. J Netw Syst Manage 29, 27 (2021). https://doi.org/10.1007/s10922-021-09592-x

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