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Context-Aware Prediction Model for Offloading Mobile Application Tasks to Mobile Cloud Environments

Author

Listed:
  • Hamid A. Jadad

    (SQU, Muscat, Oman)

  • Abederezak Touzene

    (SQU, Muscat, Oman)

  • Khaled Day

    (SQU, Muscat, Oman)

  • Nasser Alziedi

    (SQU, Muscat, Oman)

  • Bassel Arafeh

    (University of Louisville, Louisville, USA)

Abstract

Offloading intensive computation parts of the mobile application code to the cloud computing is a promising way to enhance the performance of the mobile device and save the battery consumption. Recent works on mobile cloud computing mainly focus on making a decision of which parts of application may be executed remotely, assuming that mobile and server processors have no other loads, mobile battery always full of charge, and have static network bandwidth. However, the mobile cloud environment parameters changes continuously. In this paper, the authors propose a new offloading approach which uses cost models to decide at runtime either to offload execution of the code to the remote cloud or not. This article considers the dynamic changes of the mobile cloud environment in the system cost models. Moreover, this article enhances the offloading process by considering parallel execution of application independent tasks in the cloud. The evaluation results show that the approach reduces the execution time and battery consumption by 75% and 55%, respectively, compared with existing offloading approaches.

Suggested Citation

  • Hamid A. Jadad & Abederezak Touzene & Khaled Day & Nasser Alziedi & Bassel Arafeh, 2019. "Context-Aware Prediction Model for Offloading Mobile Application Tasks to Mobile Cloud Environments," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 9(3), pages 58-74, July.
    • Handle: RePEc:igg:jcac00:v:9:y:2019:i:3:p:58-74
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