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A latency-aware and energy-efficient computation offloading in mobile fog computing: a hidden Markov model-based approach

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Abstract

In recent years, Fog Computing (FC) is known as a good infrastructure for the Internet of Things (IoT). Using this architecture for the mobile applications in the IoT is named the Mobile Fog Computing (MFC). If we assume that an application includes some modules, thus, these modules can be sent to the Fog or Cloud layer because of the resource limitation or increased runtime at the mobile. This increases the efficiency of the whole system. As data is entered sequentially, and the input is given to the modules, the number of executable modules increases. So, this research is conducted to find the best place in order to run the modules that can be on the mobile, Fog, or Cloud. According to the proposed method, when the modules arrive at gateway, then, a Hidden Markov model Auto-scaling Offloading (HMAO) finds the best destination to execute the module to create a compromise between the energy consumption and execution time of the modules. The evaluation results obtained regarding the parameters of the energy consumption, execution cost, delay, and network resource usage shows that the proposed method on average is better than the local execution, First-Fit (FF), and Q-learning based method.

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Authors and Affiliations

  1. Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran

    Fatemeh Jazayeri, Ali Shahidinejad & Mostafa Ghobaei-Arani

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  1. Fatemeh Jazayeri
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  2. Ali Shahidinejad
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  3. Mostafa Ghobaei-Arani
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Correspondence to Ali Shahidinejad.

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Jazayeri, F., Shahidinejad, A. & Ghobaei-Arani, M. A latency-aware and energy-efficient computation offloading in mobile fog computing: a hidden Markov model-based approach. J Supercomput 77, 4887–4916 (2021). https://doi.org/10.1007/s11227-020-03476-8

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  • DOI: https://doi.org/10.1007/s11227-020-03476-8

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