# A New Dynamic Game-Based Pricing Model for Cloud Environment
## 6 Conclusions  
Since the advent of clouds, choosing the right price for the services offered by this large-scale computing system has been a challenge. Considering all the various factors such as supply and demand, operating costs such as electricity bills, the possibility of a resource failure and the resulting financial and operational losses, and heterogeneity of resources in terms of the ability to execute different programs in a comprehensive pricing model is an issue that is less common in the past related work.  
Figure 9: Comparison of the proposed method with [16; 39] under different system loads from the viewpoint of the provider’s total profit.  
In this paper, we propose an approach based on game theory that offers a formal framework for examining various factors that are effective in resolving resource allocation and pricing issues. The proposed approach models these problems as a dynamic game between cloud user and provider. Since it is ultimately the user who decides whether or not to rent a resource from the cloud, we consider the user to be the leader of the game and the player who has priority in the decision. Various factors have been considered in the players' profit functions to help bring the model closer to the real situation. The resource broker, who is responsible for resource allocation and management processes, constructs and solves the game tree. The simulation results demonstrate that the proposed method is superior in reducing the operating costs for the cloud provider, even though it offers a reasonable price for renting resources to users compared to previous related methods. Paying attention to a more comprehensive set of factors affecting the execution costs of running a job by a cloud has helped the proposed method perform better than related works. However, a more precise assessment of the impact of these factors can enhance the effectiveness of the proposed approach. Extensive research has been conducted to ascertain the level of mutual influence between jobs in their execution, anticipate the energy consumption of the jobs, and assess the likelihood of successful execution. Nevertheless, further investigation in this area is still necessary.  
Conceptualization, H.S. and J.H.J.; methodology, [PERSON] and J.H.J.; software, H.S.; validation, H.S., J.H.J. and H.G.A.; formal analysis, H.S.; investigation, J.H.J.; resources, H.G.A.; data curation, H.S.; writing--original draft preparation, H.S., J.H.J. and H.G.A.; writing--review and editing, H.S. and J.H.J.; visualization, H.S.; supervision, H.S.; project administration, J.H.J.; funding acquisition, J.H.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data are simulated and generated during the experiment.  
[PERSON] is grateful for the financial support of MDPI Publisher, which was provided for refereeing in MDPI journals by receiving 20 vouchers.  
The authors declare no conflict of interest.  
Abbreviations explains the abbreviations utilized in this study. Abbreviations List of abbreviations used in this study. Abbreviations Meaning CC Cloud Computing SLA Service Level Agreement PaaS Platform as a service SaaS Software as a service Amazon Ec2 Amazon Elastic Compute Cloud IaaS Infrastructure as a Service PUE Power Usage Effectiveness CDARA Combinatorial Double Auction Resource Allocation QoS Quality of Service CSPs Cloud service providers CIPs Cloudlet Infrastructure Providers CLF Cloudlet Federation CP Cloud Provider \(VM_{j}\) Virtual machine \(j\)