Enhancing Service Management Systems with Machine Learning in Fog-to-Cloud Networks

  • Jasenka DizdarevićEmail author
  • Francisco Carpio
  • Mounir Bensalem
  • Admela Jukan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11339)


With the fog-to-cloud hybrid computing systems emerging as a promising networking architecture, particularly interesting for IoT scenarios, there is an increasing interest in exploring and developing new technologies and solutions to achieve high performances of these systems. One of these solutions includes machine learning algorithms implementation. Even without defined and standardized way of using machine learning in fog-to-cloud systems, it is obvious that machine learning capabilities of autonomous decision making would enrich both fog computing and cloud computing network nodes. In this paper, we propose a service management system specially designed to work in fog-to-cloud architectures, followed with a proposal on how to implement it with different machine learning solutions. We first show the global overview of service management system functionality with the current specific design for each of its integral components and, finally, we show the first results obtained with machine learning algorithm for its component in charge of traffic prediction.


Machine learning Fog-to-Cloud Service management 



This work has been partially performed in the framework of mF2C project funded by the European Union’s H2020 research and innovation programme under grant agreement 730929.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jasenka Dizdarević
    • 1
    Email author
  • Francisco Carpio
    • 1
  • Mounir Bensalem
    • 1
  • Admela Jukan
    • 1
  1. 1.Technische Universität BraunschweigBraunschweigGermany

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