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Profit optimized task scheduling for vehicular fog computing

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Abstract

Vehicular fog computing has emerged as a promising paradigm that provisions computing at the network edge and alleviates the computation workload of static edge computing servers. In this regard, building computing facilities on top of jammed vehicles is particularly attractive and practically viable. However, the respective offloading mechanisms and resource sharing have been less explored. In this work, we propose a novel jammed vehicular cloudlet (JVC) assisted task offloading framework that aggregates and leverages underutilized communication and computation resources of congested vehicles and nearby road side unit to serve resource-intensive tasks of mobile users. To motivate resource provisioning by the JVCs in a non-competitive environment, we design an incentive mechanism that charges offloading user and rewards the serving JVC. With aim to maximize the total profit earned by JVCs, we formulate joint task assignment and resource allocation problem in presence of data segmentation, task deadline, and budget constraints. The formulated problem is mixed integer non-linear programming problem, and we directly obtain its solution using genetic algorithm (GA). We further devise a greedy fractional-knapsack based resource allocation scheme named profit-aware task scheduling (PATS). The extensive evaluation under realistic human mobility trajectories demonstrates that, GA outperforms other baseline schemes in maximizing the total profit of JVCs while PATS achieves comparable performance and serves more users with much lower computation complexity.

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ArticleOpen access24 February 2021

Data availibility

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China

    Umber Saleem, Tong Li & Yong Li

  2. School of Electronic Engineering, Dublin City University, Dublin, Ireland

    Sobia Jangsher

Authors
  1. Umber Saleem

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  2. Sobia Jangsher

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  3. Tong Li

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  4. Yong Li

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Contributions

US desgined, implemented and wrote the manuscript with support from Dr. SJ and Dr. TL. Dr. YL conceived the original idea and supervised the research. All authors discussed the results and contributed to the final manuscript.

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Correspondence toTong Li.

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