4.7 Article

Quantifying the Influence of Intermittent Connectivity on Mobile Edge Computing

期刊

IEEE TRANSACTIONS ON CLOUD COMPUTING
卷 10, 期 1, 页码 619-632

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCC.2019.2926702

关键词

Intermittent connectivity; mobile edge computing; task processing time

资金

  1. National Key R&D Program of China [2018YFB0204100]
  2. Nature Science Foundation of China [61802452, 61572538, U1801266]
  3. Natural Science Foundation of Guangdong [2018A030310079]
  4. Guangdong Special Support Program [2017TX04X148]
  5. China Postdoctoral Science Foundation [2018M631025]

向作者/读者索取更多资源

This paper quantifies the influence of intermittent connectivity on mobile edge computing from a theoretical perspective. The proposed analytical framework and extensive simulations demonstrate the accuracy of our models in capturing the impact of intermittent connectivity on MEC.
Mobile edge computing (MEC) is a key technology that enables the deployment of applications (or services) at the proximity of mobile users. However, the performance of mobile edge computing is sensitive to the quality and availability of underlying connection links. It is still unclear to what extent intermittent connectivity affects the performance of mobile edge computing. In this paper, we make the first attempt to quantify the influence of intermittent connectivity on mobile edge computing from a theoretical perspective. Specifically, we propose an analytical framework based on discrete-time Markov chain and derive a closed-form expression of the task processing time under different network conditions. Our model can be further extended to account for the case with group task arrivals. We also conduct extensive simulations to examine the accuracy of our proposed analytical models with both synthetic and real-world user mobility traces. The results show that our model can well capture the influence of intermittent connectivity on MEC. Our model sheds important insights into the impact of intermittent connectivity on task processing in MEC, which we believe should be taken into account when designing future MEC systems.

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