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An Analysis of Methods and Metrics for Task Scheduling in Fog Computing

Author

Listed:
  • Javid Misirli

    (Computer Science Department, Sapienza University of Rome, 00185 Rome, Italy)

  • Emiliano Casalicchio

    (Computer Science Department, Sapienza University of Rome, 00185 Rome, Italy)

Abstract

The Internet of Things (IoT) uptake brought a paradigm shift in application deployment. Indeed, IoT applications are not centralized in cloud data centers, but the computation and storage are moved close to the consumers, creating a computing continuum between the edge of the network and the cloud. This paradigm shift is called fog computing, a concept introduced by Cisco in 2012. Scheduling applications in this decentralized, heterogeneous, and resource-constrained environment is challenging. The task scheduling problem in fog computing has been widely explored and addressed using many approaches, from traditional operational research to heuristics and machine learning. This paper aims to analyze the literature on task scheduling in fog computing published in the last five years to classify the criteria used for decision-making and the technique used to solve the task scheduling problem. We propose a taxonomy of task scheduling algorithms, and we identify the research gaps and challenges.

Suggested Citation

  • Javid Misirli & Emiliano Casalicchio, 2023. "An Analysis of Methods and Metrics for Task Scheduling in Fog Computing," Future Internet, MDPI, vol. 16(1), pages 1-22, December.
    • Handle: RePEc:gam:jftint:v:16:y:2023:i:1:p:16-:d:1310793
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    References listed on IDEAS

    as
    1. Rabab Farouk Abdel-Kader & Noha Emad El-Sayad & Rawya Yehia Rizk, 2021. "Efficient energy and completion time for dependent task computation offloading algorithm in industry 4.0," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-22, June.
    2. Jagdeep Singh & Parminder Singh & El Mehdi Amhoud & Mustapha Hedabou, 2022. "Energy-Efficient and Secure Load Balancing Technique for SDN-Enabled Fog Computing," Sustainability, MDPI, vol. 14(19), pages 1-22, October.
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