Game Theoretic Approaches in Mobile Cloud Computing Systems for Big Data Applications: A Systematic Literature Review

  • Georgios Skourletopoulos
  • Constandinos X. Mavromoustakis
  • George Mastorakis
  • Jordi Mongay Batalla
  • Ciprian Dobre
  • John N. Sahalos
  • Rossitza I. Goleva
  • Nuno M. Garcia
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 10)


The constant technological innovations in wireless communications and network technologies as well as the increasing number of smart mobile devices generate an enormous volume of data stemming from a set of user equipments (UEs). Since an exponential growth of data and analytics is witnessed, new technical and application challenges emerge associated with underlying models that exploit cloud computing technologies, such as the Big Data-as-a-Service (BDaaS) or Analytics-as-a-Service (AaaS). In this context, this survey chapter summarizes and establishes to what extend existing research studies have progressed towards applying game theoretic approaches in mobile cloud computing systems for big data applications. We identify and critically evaluate the findings of relevant works addressing this research problem by shedding light on contradictions and gaps in the literature. We therefore propose a cost-benefit model formulation in mobile cloud computing environments and a new game theoretic conceptualization, which accounts for the dynamic storage allocation in cloud systems formulated as a benefit optimization problem. Diverse experimental scenarios are adopted to verify and evaluate the optimality and effectiveness of the developed theory in real-world scenarios.


Game theory Cloud computing Mobile computing Big data Data analytics Risk analysis 



The authors would like to acknowledge networking support by the EU ICT COST Action IC1303 on ‘Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)’ and the EU ICT COST Action IC1406 on ‘High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)’.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Georgios Skourletopoulos
    • 1
  • Constandinos X. Mavromoustakis
    • 1
  • George Mastorakis
    • 2
  • Jordi Mongay Batalla
    • 3
  • Ciprian Dobre
    • 4
  • John N. Sahalos
    • 5
  • Rossitza I. Goleva
    • 6
  • Nuno M. Garcia
    • 7
  1. 1.Mobile Systems Laboratory (MoSys Lab), Department of Computer ScienceUniversity of NicosiaNicosiaCyprus
  2. 2.Department of Informatics EngineeringTechnological Educational Institute of CreteHeraklion, CreteGreece
  3. 3.National Institute of Telecommunications and Warsaw University of TechnologyWarsawPoland
  4. 4.Faculty of Automatic Control and Computers, Department of Computer Science and EngineeringUniversity Politehnica of BucharestBucharestRomania
  5. 5.Radio-Communications Laboratory (RCLab), Department of PhysicsAristotle University of ThessalonikiThessalonikiGreece
  6. 6.Faculty of Telecommunications, Department of Communication NetworksTechnical University of SofiaSofiaBulgaria
  7. 7.Assisted Living Computing and Telecommunications Laboratory (ALLab), Faculty of Engineering, Department of Computer Science, Instituto de TelecomunicaçõesUniversity of Beira InteriorCovilhãPortugal

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