Run-time evaluation of architectures: a case study of diversification in IoT

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Run-time evaluation of architectures : a case study of diversification in IoT. / Sobhy, Dalia; Minku, Leandro; Bahsoon, Rami; Chen, Tao; Kazman, Rick.

In: Journal of Systems and Software, Vol. 159, 110428, 01.2020.

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@article{f3e8dac9140f4933ab1f5ff0a1722d3e,
title = "Run-time evaluation of architectures: a case study of diversification in IoT",
abstract = "Run-time properties of modern software system environments, such as Internet of Things (IoT), are a challenge for existing software architecture evaluation methods. Such systems are largely data-driven, characterized by their dynamism, unpredictability in operation, hyper-connectivity, and scale. Properties, such as performance, delayed delivery, and scalability, are acknowledged to pose great risk and are difficult to evaluate at design-time. Run-time evaluation could potentially be used to complement design-time evaluation, enabling significant deviations from the expected performance values to be captured. However, there are no systematic software architecture evaluation methods that intertwine and interleave design-time and run-time evaluation. This paper addresses this gap by proposing a novel run-time architecture evaluation method suited for systems that exhibit uncertainty and dynamism in their operation. Our method uses machine learning and cost-benefit analysis at run-time to continuously profile the architecture decisions made, to assess their added value. We demonstrate the applicability and effectiveness of this approach in the context of an IoT system architecture, where some architecture design decisions were diversified to meet Quality of Service (QoS) requirements. Our approach provides run-time assessment for these decisions which can inform deployment, refinement, and/or phasing-out decisions.",
keywords = "Design diversity, Internet of things, IoT, Run-time architecture evaluation, Runtime architecture evaluation, Software architectures for dynamic environments",
author = "Dalia Sobhy and Leandro Minku and Rami Bahsoon and Tao Chen and Rick Kazman",
year = "2020",
month = jan,
doi = "10.1016/j.jss.2019.110428",
language = "English",
volume = "159",
journal = "Journal of Systems and Software",
issn = "0164-1212",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Run-time evaluation of architectures

T2 - a case study of diversification in IoT

AU - Sobhy, Dalia

AU - Minku, Leandro

AU - Bahsoon, Rami

AU - Chen, Tao

AU - Kazman, Rick

PY - 2020/1

Y1 - 2020/1

N2 - Run-time properties of modern software system environments, such as Internet of Things (IoT), are a challenge for existing software architecture evaluation methods. Such systems are largely data-driven, characterized by their dynamism, unpredictability in operation, hyper-connectivity, and scale. Properties, such as performance, delayed delivery, and scalability, are acknowledged to pose great risk and are difficult to evaluate at design-time. Run-time evaluation could potentially be used to complement design-time evaluation, enabling significant deviations from the expected performance values to be captured. However, there are no systematic software architecture evaluation methods that intertwine and interleave design-time and run-time evaluation. This paper addresses this gap by proposing a novel run-time architecture evaluation method suited for systems that exhibit uncertainty and dynamism in their operation. Our method uses machine learning and cost-benefit analysis at run-time to continuously profile the architecture decisions made, to assess their added value. We demonstrate the applicability and effectiveness of this approach in the context of an IoT system architecture, where some architecture design decisions were diversified to meet Quality of Service (QoS) requirements. Our approach provides run-time assessment for these decisions which can inform deployment, refinement, and/or phasing-out decisions.

AB - Run-time properties of modern software system environments, such as Internet of Things (IoT), are a challenge for existing software architecture evaluation methods. Such systems are largely data-driven, characterized by their dynamism, unpredictability in operation, hyper-connectivity, and scale. Properties, such as performance, delayed delivery, and scalability, are acknowledged to pose great risk and are difficult to evaluate at design-time. Run-time evaluation could potentially be used to complement design-time evaluation, enabling significant deviations from the expected performance values to be captured. However, there are no systematic software architecture evaluation methods that intertwine and interleave design-time and run-time evaluation. This paper addresses this gap by proposing a novel run-time architecture evaluation method suited for systems that exhibit uncertainty and dynamism in their operation. Our method uses machine learning and cost-benefit analysis at run-time to continuously profile the architecture decisions made, to assess their added value. We demonstrate the applicability and effectiveness of this approach in the context of an IoT system architecture, where some architecture design decisions were diversified to meet Quality of Service (QoS) requirements. Our approach provides run-time assessment for these decisions which can inform deployment, refinement, and/or phasing-out decisions.

KW - Design diversity

KW - Internet of things

KW - IoT

KW - Run-time architecture evaluation

KW - Runtime architecture evaluation

KW - Software architectures for dynamic environments

UR - http://www.scopus.com/inward/record.url?scp=85073076930&partnerID=8YFLogxK

U2 - 10.1016/j.jss.2019.110428

DO - 10.1016/j.jss.2019.110428

M3 - Article

AN - SCOPUS:85073076930

VL - 159

JO - Journal of Systems and Software

JF - Journal of Systems and Software

SN - 0164-1212

M1 - 110428

ER -