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A systematic mapping study on Quality of Service in industrial cloud computing
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
2020 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Context -- The rapid development of Industry 4.0 and Industrial Cyber-Physical Systems is leading to the exponential growth of unprocessed volumes of data. Industrial cloud computing has shown great potential as a solution that can provide the necessary resources for processing these data. However, in order to be widely adopted, it must provide satisfactory levels of QoS. The lack of a standardized model of quality attributes to be used for assessing QoS raises significant concerns.

Objective -- This study aims to provide a map of current research on QoS in industrial cloud computing, focusing on identifying and classifying the quality attributes that are currently most commonly used to evaluate QoS.

Method -- To achieve our objective, we conducted a systematic mapping study of the state-of-the-art of QoS in industrial cloud computing. Our search yielded 1063 potentially relevant studies that were subject to a rigorous selection process, resulting in a final set of 42 primary studies. Key information from the primary studies was extracted according to the categories of a well-defined classification framework.

Results -- The analysis of the extracted data highlighted the following main findings: (i) research largely focuses on providing solution proposals that require a more solid validation, (ii) the adoption of cloud technologies is closely related to performance indicators, while research on other quality attributes is quite limited, (iii) there is a lack of research on security in industrial cloud computing, (iv) approaches are in most cases not targeting explicitly a specific industrial domain, (v) there is a strong focus on the impact of virtualization solutions on QoS, and (vi) research efforts are oriented towards the improvement of QoS through scheduling.

Conclusion -- These results can help the research community identify trends, limitations, and research gaps on QoS in industrial cloud computing, and reveal possible directions for future research.

Place, publisher, year, edition, pages
2020. , p. 50
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-48612OAI: oai:DiVA.org:mdh-48612DiVA, id: diva2:1438758
Subject / course
Computer Science
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Available from: 2020-06-16 Created: 2020-06-11 Last updated: 2020-06-16Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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