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Introducing k4.0s: a Model for Mixed-Criticality Container Orchestration in Industry 4.0 (extended)

Authors :
Barletta, Marco
Cinque, Marcello
De Simone, Luigi
Della Corte, Raffaele
Source :
2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress
Publication Year :
2022

Abstract

Time predictable edge cloud is seen as the answer for many arising needs in Industry 4.0 environments, since it is able to provide flexible, modular, and reconfigurable services with low latency and reduced costs. Orchestration systems are becoming the core component of clouds since they take decisions on the placement and lifecycle of software components. Current solutions start introducing real-time containers support for time predictability; however, these approaches lack of determinism as well as support for workloads requiring multiple levels of assurance/criticality. In this paper, we present k4.0s, an orchestration model for real-time and mixed-criticality environments, which includes timeliness, criticality and network requirements. The model leverages new abstractions for both node and jobs, e.g., node assurance, and requires novel monitoring strategies. We sketch an implementation of the proposal based on Kubernetes, and present an experimentation motivating the need for node assurance levels and adequate monitoring.<br />Comment: This paper is an extended version of the paper published at "2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress"

Details

Database :
arXiv
Journal :
2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress
Publication Type :
Report
Accession number :
edsarx.2205.14188
Document Type :
Working Paper
Full Text :
https://doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927896