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Towards Mobile Cloud Computing in 5G Mobile Networks: Applications, Big Data Services and Future Opportunities

  • Georgios Skourletopoulos
  • Constandinos X. Mavromoustakis
  • George Mastorakis
  • Jordi Mongay Batalla
  • Ciprian Dobre
  • Spyros Panagiotakis
  • Evangelos Pallis
Chapter
Part of the Studies in Big Data book series (SBD, volume 22)

Abstract

The highly computationally capable mobile devices and the continuously increasing demand for high data rates and mobility, which are required by several mobile network services, enabled the research on fifth-generation (5G) mobile networks that are expected to be deployed beyond the year 2020 in order to support services and applications with more than one thousand times of today’s network traffic. On the other hand, the huge and complex location-aware datasets exceed the capability of spatial computing technologies. In this direction, the mobile cloud computing (MCC) technology was introduced as the combination of cloud computing and mobile computing, enabling the end-users to access the cloud-supported services through mobile devices (e.g., smartphones, tablets, portable computers or wearable devices). The mobile applications exploit cloud technologies for data processing, storage and other intensive operations, as they are executed on resource providers external to the devices. This tutorial article is a comprehensive review of the current state-of-the-art and the latest developments on mobile cloud computing under the 5G era, which helps early-stage researchers to have an overview of the existing solutions, techniques and applications and investigate open research issues and future challenges in this domain.

Keywords

Mobile cloud computing Mobile cloud-based service level 5G mobile networks Big data Data-driven Modelling Capacity Lease cloud-based mobile services 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive comments and feedback on the manuscript. Their suggestions have contributed significantly to the high quality and improvement of this survey chapter. The research is supported by the ICT COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications” (cHiPSet).

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Georgios Skourletopoulos
    • 1
  • Constandinos X. Mavromoustakis
    • 1
  • George Mastorakis
    • 2
  • Jordi Mongay Batalla
    • 3
  • Ciprian Dobre
    • 4
  • Spyros Panagiotakis
    • 2
  • Evangelos Pallis
    • 2
  1. 1.Mobile Systems (MoSys) Laboratory, Department of Computer ScienceUniversity of NicosiaNicosiaCyprus
  2. 2.Department of Informatics EngineeringTechnological Educational Institute of CreteHeraklionGreece
  3. 3.National Institute of TelecommunicationsWarsawPoland
  4. 4.Faculty of Automatic Control and Computers, Department of Computer ScienceUniversity Politehnica of BucharestBucharestRomania

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