A context-aware and self-adaptive offloading decision support model for mobile cloud computing system

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Mobile cloud computing is one of the main ways to augment the resource-constrained mobile devices to run rich mobile applications through the offloading technique, which leverages resources and services from remote server in the cloud. However, an efficient and intelligent use of cloud resources is required due to changing environment conditions and application variability usage. In order to help address this issue we present CoSMOS—Context-Sensitive Model for Offloading System—a context-aware and self-adaptive offloading decision support model for mobile cloud computing systems, based on self-aware and self-expressive systems. It employs decision-taking estimation based on application’s time execution and energy consumption to decide efficiently when and which application components should be offloaded in order to improve system’s execution. Our experiments show that the model is capable of inferring appropriate decisions with acceptable performance in a range of environment conditions.

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  1. Ahmed E, Gani A, Sookhak M, Hamid SHA, Xia F (2015) Application optimization in mobile cloud computing: motivation, taxonomies, and open challenges. J Netw Comput Appl 52:52–68

  2. Chen T, Faniyi F, Bahsoon R, Lewis PR, Yao X, Minku LL, Esterle L (2014) The handbook of engineering self-aware and self-expressive systems. arXiv:1409.1793 [CoRR abs]

  3. Chen X, Chen S, Zeng X, Zheng X, Zhang Y, Rong C (2017) Framework for context-aware computation offloading in mobile cloud computing. J Cloud Comput 6(1):1

  4. COMSCORE (2014) The US mobile app report.

  5. Costa PB, Rego PAL, Rocha LS, Trinta FAM, de Souza JN (2015) Mpos: A multiplatform offloading system. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing. SAC ’15, ACM, New York, pp 577–584

  6. Deb K (2011) Multi-objective optimization using evolutionary algorithms: an introduction. In: Multi-objective evolutionary optimisation for product design and manufacturing, vol 1. Springer, London, pp 3–34

  7. Dutt N, Jantsch A, Sarma S (2016) Toward smart embedded systems: a self-aware system-on-chip (soc) perspective. ACM Trans Embed Comput Syst 15(2):22:1–22:27.

  8. Fernando N, Loke SW, Rahayu W (2013) Mobile cloud computing: a survey. Future Gen Comput Syst 29(1):84–106 [including Special section: AIRCC-NetCoM 2009 and Special section: Clouds and Service-Oriented Architectures]

  9. Gent IP, Jefferson C, Nightingale P (2017) Complexity of n-queens completion. J Artif Intell Res 59:815–848.

  10. Kemp R, Palmer N, Kielmann T, Bal H (2012) Cuckoo: a computation offloading framework for smartphones. Springer, Berlin, Heidelberg, pp 59–79

  11. Khan AR, Othman M, Xia F, Khan AN (2015) Context-aware mobile cloud computing and its challenges. IEEE Cloud Comput 2(3):42–49

  12. Kosta S, Aucinas A, Hui P, Mortier R, Zhang X (2012) Thinkair: dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In: 2012 Proceedings IEEE INFOCOM, pp 945–953

  13. Kovachev D, Yu T, Klamma R (2012) Adaptive computation offloading from mobile devices into the cloud. In: Proceedings of the 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications. ISPA ’12, IEEE Computer Society, Washington, DC, pp 784–791

  14. Lewis PR, Chandra A, Parsons S, Robinson E, Glette K, Bahsoon R, Torresen J, Yao X (2011) A survey of self-awareness and its application in computing systems. In: Self-Adaptive and Self-Organizing Systems Workshops (SASOW), 2011 Fifth IEEE Conference on, pp 102–107

  15. Naqvi NZ, Devlieghere J, Preuveneers D, Berbers Y (2016) Mascot: Self-adaptive opportunistic offloading for cloud-enabled smart mobile applications with probabilistic graphical models at runtime. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), pp 5701–5710

  16. Olteanu AC, Ţăpuş N (2014) Offloading for mobile devices: a survey. UPB Sci Bull 76:3–16

  17. Qualcomm (2015) Trepn power profiler.

  18. Satyanarayanan M, Bahl P, Caceres R, Davies N (2009) The case for vm-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23

  19. Verbelen T, Simoens P, Turck FD, Dhoedt B (2012) Aiolos: Middleware for improving mobile application performance through cyber foraging. J Syst Softw 85(11):2629–2639

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The authors would like to thank the Group of Computer Networks, Software Engineering and Systems (GREat) for the all the support offered during this work’s design and development stages, and for the MpOS framework and BenchImage mobile application used on this project. The authors would also like to thank the support provided by Brazilian Higher Education Funding Council (CAPES).

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Correspondence to Flávio Akira Nakahara.

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Nakahara, F.A., Beder, D.M. A context-aware and self-adaptive offloading decision support model for mobile cloud computing system. J Ambient Intell Human Comput 9, 1561–1572 (2018) doi:10.1007/s12652-018-0790-7

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  • Mobile cloud computing
  • Decision support
  • Context-awareness
  • Dynamic offloading