Offloading as a Service Middleware for Mobile Cloud Apps

Hamid A. Jadad, Abderezak Touzene, Khaled Day

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Recently, much research has focused on the improvement of mobile app performance and their power optimization, by offloading computation from mobile devices to public cloud computing platforms. However, the scalability of these offloading services on a large scale is still a challenge. This article describes a solution to this scalability problem by proposing a middleware that provides offloading as a service (OAS) to large-scale implementation of mobile users and apps. The proposed middleware OAS uses adaptive VM allocation and deallocation algorithms based on a CPU rate prediction model. Furthermore, it dynamically schedules the requests using a load-balancing algorithm to ensure meeting QoS requirements at a lower cost. The authors have tested the proposed algorithm by conducting multiple simulations and compared our results with state-of-the-art algorithms based on various performance metrics under multiple load conditions. The results show that OAS achieves better response time with a minimum number of VMs and reduces 50% of the cost compared to existing approaches.

Original languageEnglish
Pages (from-to)36-55
Number of pages20
JournalInternational Journal of Cloud Applications and Computing
Issue number2
Publication statusPublished - 2020


  • Apps
  • Load Balancing
  • Mobile Cloud
  • Offloading
  • Scheduling

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications

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