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

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.

Toward an efficient healthcare CloudIoT architecture by using a game theory approach

“…The proposed CloudIoT-based architecture for healthcare environments. Ghanavati et al (2017) Yes IoT service-oriented framework Wireless body area network Yang et al (2016) No Monitoring based on IoT Healthcare (electrocardiography) Park and Kim (2015) No Data-offloading technique Traffic control Dinh et al (2017) Yes CloudIoT model for MCC Mobility and power consumption Shah-Mansouri and Wong 2017No Offloading game Fog computing resources Nezarat and Dastghaibifard (2015) No Game theory mechanism Auction-based method Yuan et al 2017Yes Dynamic resource allocation Geo-distributed CloudIoT Hayat et al 2017Yes Game-based approach Data as a service (DaaS) Zhang et al 2017Yes Fog computing Fog nodes optimization Sood and Mahajan (2017) No Fog and cloud based Healthcare (diagnose and prevent)…”

Massive Data Balance Scheduling in Cloud Computing Environment

“…Aiming at the problem of that load balancing is not fully taken into account and scheduling error is large when using the current algorithm to establish the massive data balance scheduling model in the cloud computing environment [14,15], a balance scheduling method for massive data in cloud computing environment based on the improved clonal selection algorithm is proposed. According to the principle of clonal selection, the attributes of the massive data in the cloud computing environment are defined as antigens, and the antibody encoding mode of the massive data balance scheduling is designed.…”

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

“…In a study, Nezarat and Dastghaibifard [69] propose a method based on an auction, which applies game theory mechanism to determines the auction winner and holding a repetitive game with inadequate information. At the last point of the game theory approach is the Nash equilibrium.…”