Efficient Nash equilibrium resource allocation based on game theory mechanism in cloud computing by using auction

Efficient resource allocation in cognitive radio network (CRN) remains a challenge due to dynamic nature of available spectrum in working band and implementation in nano-computing environment. Adoption of game theory in power allocation based on pricing model requires the formulation of strategy of the players as profit/loss function which may lead to Nash equilibrium. Earlier research focuses on the impact of game models such as Cournot, and Bertrand in formulating the utility function under the constraint power and service quality. In Cournot and Bertrand game the users play simultaneously that may not be acquainted with the other user’s action. It suits for common regimes such as all user are to be unlicensed (SU) and they may simultaneously try to access a set of available channels. In this paper, we formulate the condition for optimal power allocation in CRN using Stackelberg game where the previous user decides its output and then the erstwhile user does so, knowing the output characteristics by the former user. Based on the profit of PU (as a leader) and SU (as a follower), the optimized solution is formulated for power and interference price and it is modelled as a convex function of transmission power achieved by Nash equilibrium which involves backward induction. By means of a uniform pricing scheme every PU aims to maximize its profit under channel data rate and interference power constraint. The proposed Resource Allocation using Stackelberg Game (RASG) algorithm tries to optimize uniform pricing and power allocation among SUs such that maximizing throughput and fairness. The simulation results show a significant enhancement in throughput and fairness compared to power optimization based on Bertrand and Cournot game theory.

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Resource Sharing and Allocation in P2P Social Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.610.588 ◽

2014 ◽

Vol 610 ◽

pp. 588-594

Author(s):

Qing Feng Zhang ◽

Sheng Wang ◽

Dan Liao

Keyword(s):

Game Theory ◽

Social Networks ◽

Resource Allocation ◽

Nash Equilibrium ◽

Public Goods ◽

Transaction Costs ◽

Resource Sharing ◽

Fixed Costs ◽

Average Price ◽

Resource Pricing

this paper investigated resource sharing and allocation in P2P social networks which based on game theory. Firstly, resources are divided into two categories: Public goods (PG) and Club goods (CG). The PG has the following characteristics: self-less, Non-exclusive and un-competitive; but the CG has some self-ish, exclusive and competitive. The PG only to get the sharing fixed costs and transaction costs, but the CG needs to obtain more benefits over than costs. We demonstrated that when providers sharing resource is CG within sharing capacities can achieve the maximum benefits and Nash equilibrium. Secondly, peers are divided into two sets: friends set (FS) and strangers set (SS), providers allocate the CG in different sets within different pricing by the average price. Finally, simulations analyzed benefits of peers sharing the PG or the CG, and then discussed resource allocation in different sets within different payment strategies and resource pricing in the same set.

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Researches on Data Privacy in Cloud Computing Based on Game Theory

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.571-572.22 ◽

2014 ◽

Vol 571-572 ◽

pp. 22-25

Author(s):

Yong Hong Yu ◽

Li Wu

Keyword(s):

Game Theory ◽

Cloud Computing ◽

Nash Equilibrium ◽

Data Privacy ◽

Mixed Strategy ◽

Service Providers ◽

Influence Factors ◽

Complete Information ◽

Trusted Data ◽

Database Service

Existing proposals for data privacy in cloud computing have typically been founded on encryption and are not well-balanced on dealing with the contradiction between data privacy and efficient queries. This paper discusses the data privacy in cloud computing based on the perspective of game theory. It built a complete information static game theory frame between a trusted data center and many un-trusted database service providers to avoid the coalition among un-trusted database service providers, and gave Nash equilibrium of mixed strategy. Some influence factors of Nash equilibrium are also analyzed.

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IMPLEMENTING TASK AND RESOURCE ALLOCATION ALGORITHM BASED ON NON-COOPERATIVE GAME THEORY IN CLOUD COMPUTING

International Journal of Advanced Research in Computer Science ◽

10.26483/ijarcs.v9i1.5389 ◽

2018 ◽

Vol 9 (1) ◽

pp. 660-666 ◽

Cited By ~ 1

Author(s):

G. Prasadu ◽

Keyword(s):

Game Theory ◽

Resource Allocation ◽

Cloud Computing ◽

Cooperative Game ◽

Cooperative Game Theory ◽

Resource Allocation Algorithm ◽

Allocation Algorithm

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A Game-Theoretic Based Resource Allocation Strategy for Cloud Computing Services

Scientific Programming ◽

10.1155/2016/1629893 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13

Author(s):

Wang Yan ◽

Wang Jinkuan ◽

Sun Jinghao

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Nash Equilibrium ◽

System Performance ◽

Nash Bargaining ◽

Noncooperative Game ◽

Allocation Strategy ◽

Resource Allocation Algorithm ◽

Allocation Algorithm ◽

Computing Services

We propose an economics-oriented cloud computing resources allocation strategy with the use of game theory. Then we develop a resource allocation algorithm named NCGRAA (noncooperative game resource allocation algorithm) to search the Nash equilibrium solution that makes the utility of various resource providers achieve optimum. We also propose an algorithm named BGRAA (bargaining game resource allocation algorithm) to further increase the overall revenue with the constraints of efficiency and fairness. Based on numerical results, we discuss the influence of NCGRAA and BGRAA for the utility of resource on the system performance. It shows that the choice of parameters of the two algorithms is significant in improving the system performance and converging to the Nash equilibrium and Nash bargaining.

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