The Rise of Cloud Computing in the Era of Emerging Networked Society

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Part of the Computer Communications and Networks book series (CCN)

Abstract

Five years ago, we envisioned a taxonomy for cloud computing. During this period, the role and scope of cloud computing has drastically changed. In this chapter, we provide a complement to that taxonomy and survey, denoting the rapid technological advancements since then. More specifically, cloud computing has been widely used in several areas and has become one of the major key technologies in our day-to-day life – from cellular phones to connected vehicles to the networked society. Networked society – a new paradigm – is not only about technology, but it creates an ecosystem of device vendors, application developers, network operators, telecom operators, and cloud services/infrastructure providers to create a foreseeable new business value chain that will not only accelerate every area but also brings new innovative ideas and services. In fact, the networked society is the networks of everything (NoE), that is, beyond the upcoming 5G networks. Cloud technologies became a major part of networked society. On the other hand, cloud computing is shifting from silos to the so-called Internet of Things (IoT) that will create tremendous opportunities and challenges. This chapter first provides an overview of cloud computing. A taxonomy of enabling technologies of networked society is presented in great detail. Based on this proposed taxonomy, this chapter discusses the emerging trends in cloud computing including edge computing (cloudlets, mobile-edge computing, and fog computing), IoT (smart grids, smart cities) and big data, 5G networks, cloud radio access network (C-RAN), tactile Internet, software define networking (SDN), network function virtualization (NFV), augmented reality, virtual reality, and mixed reality, network slicing, and containerization.

Keywords

Cloud Computing Cloud Service Smart Grid Smart City Software Define Networking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of QuébecINRSMontŕealCanada
  2. 2.York UniversityTorontoCanada
  3. 3.Univa CorporationHoffman EstatesUSA

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