A new resource allocation method in fog computing via non-cooperative game theory

2021 ◽  
pp. 1-12
Author(s):  
Houshyar Mohammady Talvar ◽  
Hamid Haj Seyyed Javadi ◽  
Hamidreza Navidi ◽  
Afshin Rezakhani
Keyword(s):  
Resource Allocation ◽  
Stackelberg Game ◽  
Fog Computing ◽  
Modern Architecture ◽  
Data Service ◽  
Network Systems ◽  
Computing Systems ◽  
Data Services ◽  
Auction Equilibrium ◽  
Combined Algorithm

IoT-based network systems use a modern architecture called fog computing, In which data providing data services is economical with low latency. This paper tends to solve the challenge of resource allocation in fog computing. Solving the resource allocation challenge leads to increased profits, economic savings, and optimal computing systems use. Here resource allocation is improved by making use of the combined algorithm Nash equilibrium and auction. In the proposed method, each player is assigned a matrix. Each player matrix includes fog nodes (FNs), data service subscribers (DSSs), and data service operators (DSOs). Each player generates the best strategy based on the other players strategy in all stages of the algorithm. The simulation results show that FNs profit in the combined Nash and Auction equilibrium algorithms is superior to the Stackelberg game algorithm.

scholarly journals Blockchain-Enabled Task Offloading and Resource Allocation in Fog Computing Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiaoge Huang ◽  
Xuesong Deng ◽  
Chengchao Liang ◽  
Weiwei Fan
Keyword(s):  
Resource Allocation ◽  
Stackelberg Game ◽  
Fog Computing ◽  
User Privacy ◽  
Total Energy Consumption ◽  
Total Delay ◽  
Resource Allocation Algorithm ◽  
Network Utility ◽  
Optimal Resource ◽  
Task Offloading

To address the data security and user privacy issues in the task offloading process and resource allocation of the fog computing network, a blockchain-enabled fog computing network task offloading model is proposed in this paper. Furthermore, to reduce the network utility which is defined as the total energy consumption of the fog computing network and the total delay of the blockchain network, a blockchain-enabled fog computing network task offloading and resource allocation algorithm (TR-BFCN) is proposed to jointly optimize the task offloading decision and resource allocation. Finally, the original nonconvex optimization problem is converted into two suboptimization problems, namely, task offloading decisions and computational resource allocations. Moreover, a two-stage Stackelberg game model is designed to obtain the optimal amount of purchased resource and the optimal resource pricing. Simulation results show that the proposed TR-BFCN algorithm can effectively reduce the network utility compared with other algorithms.

scholarly journals PORA: Predictive Offloading and Resource Allocation in Dynamic Fog Computing Systems

2020 ◽  
Vol 7 (1) ◽  
pp. 72-87 ◽  
Author(s):  
Xin Gao ◽  
Xi Huang ◽  
Simeng Bian ◽  
Ziyu Shao ◽  
Yang Yang
Keyword(s):  
Resource Allocation ◽  
Fog Computing ◽  
Computing Systems

Game-Based Resource Allocation Mechanism in B5G HetNets with Incomplete Information

2020 ◽  
Vol 10 (5) ◽  
pp. 1557
Author(s):  
Weijia Feng ◽  
Xiaohui Li
Keyword(s):  
Resource Allocation ◽  
Incomplete Information ◽  
Stackelberg Game ◽  
Superior Performance ◽  
Channel Gain ◽  
Mobile Users ◽  
Pricing Strategies ◽  
Game Equilibrium ◽  
Allocation Process ◽  
Realistic Situation

Ultra-dense and highly heterogeneous network (HetNet) deployments make the allocation of limited wireless resources among ubiquitous Internet of Things (IoT) devices an unprecedented challenge in 5G and beyond (B5G) networks. The interactions among mobile users and HetNets remain to be analyzed, where mobile users choose optimal networks to access and the HetNets adopt proper methods for allocating their own network resource. Existing works always need complete information among mobile users and HetNets. However, it is not practical in a realistic situation where important individual information is protected and will not be public to others. This paper proposes a distributed pricing and resource allocation scheme based on a Stackelberg game with incomplete information. The proposed model proves to be more practical by solving the problem that important information of either mobile users or HetNets is difficult to acquire during the resource allocation process. Considering the unknowability of channel gain information, the follower game among users is modeled as an incomplete information game, and channel gain is regarded as the type of each player. Given the pricing strategies of networks, users will adjust their bandwidth requesting strategies to maximize their expected utility. While based on the sub-equilibrium obtained in the follower game, networks will correspondingly update their pricing strategies to be optimal. The existence and uniqueness of Bayesian Nash equilibrium is proved. A probabilistic prediction method realizes the feasibility of the incomplete information game, and a reverse deduction method is utilized to obtain the game equilibrium. Simulation results show the superior performance of the proposed method.

An evolutionary fuzzy scheduler for multi-objective resource allocation in fog computing

2021 ◽  
Vol 117 ◽  
pp. 498-509
Author(s):  
Chu-ge Wu ◽  
Wei Li ◽  
Ling Wang ◽  
Albert Y. Zomaya
Keyword(s):  
Resource Allocation ◽  
Fog Computing ◽  
Multi Objective
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