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    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/s00607-020-00900-y

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    Embargo ends: 30/01/22

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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The evolution of distributed computing systems: from fundamental to new frontiers

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>30/01/2021
<mark>Journal</mark>Computing
Number of pages20
Publication StatusE-pub ahead of print
Early online date30/01/21
<mark>Original language</mark>English

Abstract

Distributed systems have been an active field of research for over 60 years, and has played a crucial role in computer science, enabling the invention of the Internet that underpins all facets of modern life. Through technological advancements and their changing role in society, distributed systems have undergone a perpetual evolution, with each change resulting in the formation of a new paradigm. Each new distributed system paradigm—of which modern prominence include cloud computing, Fog computing, and the Internet of Things (IoT)—allows for new forms of commercial and artistic value, yet also ushers in new research challenges that must be addressed in order to realize and enhance their operation. However, it is necessary to precisely identify what factors drive the formation and growth of a paradigm, and how unique are the research challenges within modern distributed systems in comparison to prior generations of systems. The objective of this work is to study and evaluate the key factors that have influenced and driven the evolution of distributed system paradigms, from early mainframes, inception of the global inter-network, and to present contemporary systems such as edge computing, Fog computing and IoT. Our analysis highlights assumptions that have driven distributed systems appear to be changing, including (1) an accelerated fragmentation of paradigms driven by commercial interests and physical limitations imposed by the end of Moore’s law, (2) a transition away from generalized architectures and frameworks towards increasing specialization, and (3) each paradigm architecture results in some form of pivoting between centralization and decentralization coordination. Finally, we discuss present day and future challenges of distributed research pertaining to studying complex phenomena at scale and the role of distributed systems research in the context of climate change.

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The final publication is available at Springer via http://dx.doi.org/10.1007/s00607-020-00900-y