The IoT Resource Allocation Improvement in Fog Computing Using Non-Cooperative Game Theory

Document Type : Original Article

Authors

1 Associate Professor, Department of Computer, Islamic Azad University, Boroujerd Branch, Boroujerd, Iran

2 Assistant Professor, Computer Department, Ayatollah Borujerdi University, Borujerd, Iran

Abstract

A modern architecture called fog computing is used in the IoT-based network systems. Providing data services is economical and low latent in the fog computing architecture. This paper addresses the main challenge of allocating computing resources in fog computing. Solving the resource allocation challenge leads to increased profits, economic savings, and optimal use of the computing systems. In this survey, resource allocation has been improved by using the combined Nash equilibrium algorithm and the auction algorithm. In the proposed method, each player is assigned a specific matrix. Each player’s matrix includes fog nodes, data service subscribers, and data service operators. At each stage of the algorithm, each player generates the best strategy based on the strategy of the other players. The results show the superiority of fog node utility and data service operator utility in the proposed method compared with the Stackelberg game algorithm. The first comparison is based on the changes of subscribers in which the productivity of the node with 240 used subscribers in the proposed method is 6852.8 whilst it is 5510.2 in the Stackelberg method with the same conditions. The second comparison is based on the service rate of the resource control blocks (μ) in which the productivity of the data service operator with μ=4 in the proposed method is 1.35E + 07 whilst it is 1E + 7 in the Stackelberg method with the same conditions .

Keywords

References

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Electronic and Cyber Defense
Volume 9, Issue 4 - Serial Number 36
Serial No. 36, Winter Quarterly
February 2022
Pages 147-158

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History

  • Receive Date: 11 September 2021
  • Revise Date: 18 December 2021
  • Accept Date: 16 October 2021
  • Publish Date: 20 February 2022

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