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Journal of Network and Systems Management

Erschienen in:

01.07.2021

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

verfasst von: Lucas Santos, Benedito Cunha, Iure Fé, Marco Vieira, Francisco Airton Silva

Erschienen in: Journal of Network and Systems Management | Ausgabe 3/2021

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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.

Vorheriger Artikel AAC: Adaptively Adjusting Concurrency by Exploiting Path Diversity in Datacenter Networks

Nächster Artikel Data Transfers Using Bandwidth Reservation Through Multiple Disjoint Paths of Dynamic HPNs

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Titel: Data Processing on Edge and Cloud: A Performability Evaluation and Sensitivity Analysis
verfasst von: Lucas Santos
Benedito Cunha
Iure Fé
Marco Vieira
Francisco Airton Silva
Publikationsdatum: 01.07.2021
Verlag: Springer US
Erschienen in: Journal of Network and Systems Management / Ausgabe 3/2021
Print ISSN: 1064-7570
Elektronische ISSN: 1573-7705
DOI: https://doi.org/10.1007/s10922-021-09592-x

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