Venue & metadata

• Journal/Proceedings: Simulation Modelling Practice and Theory
• Volume: 109
• Note: Cited by: 11; All Open Access, Green Open Access
• Author keywords: Distance learning; Hybrid cloud; Moving average; Performance evaluation; Simulation; VARFIMA

Abstract

The COVID-19 emergency suddenly obliged schools and universities around the world to deliver on-line lectures and services. While the urgency of response resulted in a fast and massive adoption of standard, public on-line platforms, generally owned by big players in the digital services market, this does not sufficiently take into account privacy-related and security-related issues and potential legal problems about the legitimate exploitation of the intellectual rights about contents. However, the experience brought to attention a vast set of issues, which have been addressed by implementing these services by means of private platforms. This work presents a modeling and evaluation framework, defined on a set of high-level, management-oriented parameters and based on a Vectorial Auto Regressive Fractional (Integrated) Moving Average based approach, to support the design of distance learning architectures. The purpose of this framework is to help decision makers to evaluate the requirements and the costs of hybrid cloud technology solutions. Furthermore, it aims at providing a coarse grain reference organization integrating low-cost, long-term storage management services to implement a viable and accessible history feature for all materials. The proposed solution has been designed bearing in mind the ecosystem of Italian universities. A realistic case study has been shaped on the needs of an important, generalist, polycentric Italian university, where some of the authors of this paper work. © 2021 Elsevier B.V.

Keywords

Computer architecture GS Decision making GS Distance education GS Privacy by design GS Storage management GS Asynchronous learning GS Auto-regressive GS Decision makers GS Digital services GS Evaluation framework GS Long-term storage GS Management service GS Moving averages GS Storage as a service (STaaS) GS

Links & artifacts

DOI Publisher Copy BibTeX Download .bib

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Suggested citation

Barbierato, E., Campanile, L., Gribaudo, M., Iacono, M., Mastroianni, M., & Nacchia, S. (2021). Performance evaluation for the design of a hybrid cloud based distance synchronous and asynchronous learning architecture [Article]. Simulation Modelling Practice and Theory, 109. https://doi.org/10.1016/j.simpat.2021.102303