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Efficient Nash Equilibrium Resource Allocation Based on Game Theory Mechanism in Cloud Computing by Using Auction.

Nezarat A, Dastghaibifard GH - PLoS ONE (2015)

Bottom Line: One of the most complex issues in the cloud computing environment is the problem of resource allocation so that, on one hand, the cloud provider expects the most profitability and, on the other hand, users also expect to have the best resources at their disposal considering the budget constraints and time.In most previous work conducted, heuristic and evolutionary approaches have been used to solve this problem.To prove the response space convexity, the Lagrange method is used and the proposed model is simulated in the cloudsim and the results are compared with previous work.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Payame Noor University, Yazd, Iran; Department of Computer Engineering, Shiraz University, Shiraz, Iran.

ABSTRACT
One of the most complex issues in the cloud computing environment is the problem of resource allocation so that, on one hand, the cloud provider expects the most profitability and, on the other hand, users also expect to have the best resources at their disposal considering the budget constraints and time. In most previous work conducted, heuristic and evolutionary approaches have been used to solve this problem. Nevertheless, since the nature of this environment is based on economic methods, using such methods can decrease response time and reducing the complexity of the problem. In this paper, an auction-based method is proposed which determines the auction winner by applying game theory mechanism and holding a repetitive game with incomplete information in a non-cooperative environment. In this method, users calculate suitable price bid with their objective function during several round and repetitions and send it to the auctioneer; and the auctioneer chooses the winning player based the suggested utility function. In the proposed method, the end point of the game is the Nash equilibrium point where players are no longer inclined to alter their bid for that resource and the final bid also satisfies the auctioneer's utility function. To prove the response space convexity, the Lagrange method is used and the proposed model is simulated in the cloudsim and the results are compared with previous work. At the end, it is concluded that this method converges to a response in a shorter time, provides the lowest service level agreement violations and the most utility to the provider.

No MeSH data available.


Resource price forecasting.
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getmorefigures.php?uid=PMC4592273&req=5

pone.0138424.g012: Resource price forecasting.

Mentions: Next, the accuracy of forecasting Bayesian learning is evaluated in a mode where the cloud competitive environment is replete with uncertainty such as insufficient knowledge of the environment and posting tasks as real time. In Fig 12, price forecasting of resources in a dynamic game with incomplete information has been demonstrated.


Efficient Nash Equilibrium Resource Allocation Based on Game Theory Mechanism in Cloud Computing by Using Auction.

Nezarat A, Dastghaibifard GH - PLoS ONE (2015)

Resource price forecasting.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4592273&req=5

pone.0138424.g012: Resource price forecasting.
Mentions: Next, the accuracy of forecasting Bayesian learning is evaluated in a mode where the cloud competitive environment is replete with uncertainty such as insufficient knowledge of the environment and posting tasks as real time. In Fig 12, price forecasting of resources in a dynamic game with incomplete information has been demonstrated.

Bottom Line: One of the most complex issues in the cloud computing environment is the problem of resource allocation so that, on one hand, the cloud provider expects the most profitability and, on the other hand, users also expect to have the best resources at their disposal considering the budget constraints and time.In most previous work conducted, heuristic and evolutionary approaches have been used to solve this problem.To prove the response space convexity, the Lagrange method is used and the proposed model is simulated in the cloudsim and the results are compared with previous work.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Engineering, Payame Noor University, Yazd, Iran; Department of Computer Engineering, Shiraz University, Shiraz, Iran.

ABSTRACT
One of the most complex issues in the cloud computing environment is the problem of resource allocation so that, on one hand, the cloud provider expects the most profitability and, on the other hand, users also expect to have the best resources at their disposal considering the budget constraints and time. In most previous work conducted, heuristic and evolutionary approaches have been used to solve this problem. Nevertheless, since the nature of this environment is based on economic methods, using such methods can decrease response time and reducing the complexity of the problem. In this paper, an auction-based method is proposed which determines the auction winner by applying game theory mechanism and holding a repetitive game with incomplete information in a non-cooperative environment. In this method, users calculate suitable price bid with their objective function during several round and repetitions and send it to the auctioneer; and the auctioneer chooses the winning player based the suggested utility function. In the proposed method, the end point of the game is the Nash equilibrium point where players are no longer inclined to alter their bid for that resource and the final bid also satisfies the auctioneer's utility function. To prove the response space convexity, the Lagrange method is used and the proposed model is simulated in the cloudsim and the results are compared with previous work. At the end, it is concluded that this method converges to a response in a shorter time, provides the lowest service level agreement violations and the most utility to the provider.

No MeSH data available.