<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>It is a hot topic to investigate resource allocation in fog computing. However, currently resource allocation in fog computing mostly supports only fixed resources, that is, the resource requirements of users are satisfied with a fixed amount of resources during the usage time, which may result in low utility of resource providers and even cause a waste of resources. Therefore, we establish an integer programming model for the time-varying multidimensional resource allocation problem in fog computing to maximize the utility of the fog resource pool. We also design a heuristic algorithm to approximate the solution of the model. We apply a dominant-resource-based strategy for resource allocation to improve resource utilization as well as critical value theory for resource pricing to enhance the utility of the fog resource pool. We also prove that the algorithm satisfies truthful and individual rationality. Finally, we give some numerical examples to demonstrate the performance of the algorithm. Compared with existing studies, our approach can improve resource utilization and maximize the utility of the fog resource pool.</p>
</abstract>

It is a hot topic to investigate resource allocation in fog computing. However, currently resource allocation in fog computing mostly supports only fixed resources, that is, the resource requirements of users are satisfied with a fixed amount of resources during the usage time, which may result in low utility of resource providers and even cause a waste of resources. Therefore, we establish an integer programming model for the time-varying multidimensional resource allocation problem in fog computing to maximize the utility of the fog resource pool. We also design a heuristic algorithm to approximate the solution of the model. We apply a dominant-resource-based strategy for resource allocation to improve resource utilization as well as critical value theory for resource pricing to enhance the utility of the fog resource pool. We also prove that the algorithm satisfies truthful and individual rationality. Finally, we give some numerical examples to demonstrate the performance of the algorithm. Compared with existing studies, our approach can improve resource utilization and maximize the utility of the fog resource pool.

A Combinatorial Auction Mechanism for Time–Varying Multidimensional Resource Allocation and Pricing in Fog Computing

School of Economics and Management Yanshan University No. 438

State Key Laboratory of Media Convergence and Communication Communication University of China No. 1

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

It is a hot topic to investigate resource allocation in fog computing. However, currently resource allocation in fog computing mostly supports only fixed...

A Combinatorial Auction Mechanism for Time–Varying Multidimensional Resource Allocation and Pricing in Fog Computing

Publié en ligne: 23 juin 2023

Pages: 327 - 339

Reçu: 23 déc. 2021

Accepté: 19 août 2022

DOI: https://doi.org/10.34768/amcs-2023-0024

Mots clés
fog computing, combinatorial auction, time-varying multidimensional resource allocation, resource pricing

© 2023 Shiyong Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Combinatorial Auction Mechanism for Time–Varying Multidimensional Resource Allocation and Pricing in Fog Computing

Publié en ligne: 23 juin 2023

Pages: 327 - 339

Reçu: 23 déc. 2021

Accepté: 19 août 2022

DOI: https://doi.org/10.34768/amcs-2023-0024

Mots clésfog computing, combinatorial auction, time-varying multidimensional resource allocation, resource pricing

© 2023 Shiyong Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
fog computing, combinatorial auction, time-varying multidimensional resource allocation, resource pricing