A systematic mapping study on Quality of Service in industrial cloud computing

• Main Author: Latifaj, Malvina
• Format: Others
• Language: English
• Published: Mälardalens högskola, Inbyggda system 2020
• Subjects: Computer Sciences; Datavetenskap (datalogi)
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48612

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.