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A joint computational and resource allocation model for fast parallel data processing in fog computing

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Abstract

Fog computing can be an effective way to improve quality of services and solve network problems, with the demand for real-time, latency-sensitive applications increasing as well as limitations such as network bandwidth and Internet of Things users' resources. Due to the fact that different tasks in a network can create overhead that can reduce the quality of service, dynamic voltage, and frequency scaling along with a ranking function and a high number of physical and virtual machines were used in this research. A profit function phase is used to analyze network tasks in order to improve QoS by sending them to physical machines and sending them via the network to physical machines. The simulation results demonstrate that this method is the most effective in allocating radio and computational resources to IoT devices in fog computing. A comparison is presented in the results section between the proposed method and the SPA, Markov-Fog, and TRAM methods. Criteria for evaluating performance include the response time for heterogeneous environments, energy consumption against tasks and users, memory processing, energy consumption for physical and virtual machines, and network profitability.

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Change history

  • 06 April 2022

    In affiliation 1 branch and university name were interchanged.

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Authors and Affiliations

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

    Mahmood Lakzaei & Amir Sabbagh Molahosseini

  2. Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

    Vahid Sattari-Naeini

  3. School of Computer Science and Technology, Guangzhou University, Guangzhou, 510006, China

    Amir Javadpour

Authors
  1. Mahmood Lakzaei
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  2. Vahid Sattari-Naeini
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  3. Amir Sabbagh Molahosseini
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  4. Amir Javadpour
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Corresponding author

Correspondence to Vahid Sattari-Naeini.

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Table 4 This table summarizes some similar literature finding in this research
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4.

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Lakzaei, M., Sattari-Naeini, V., Sabbagh Molahosseini, A. et al. A joint computational and resource allocation model for fast parallel data processing in fog computing. J Supercomput 78, 12662–12685 (2022). https://doi.org/10.1007/s11227-022-04374-x

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  • DOI: https://doi.org/10.1007/s11227-022-04374-x

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