Abstract
In the cloud market, there exist multiple cloud providers adopting auction-based mechanisms to offer cloud services to users. These auction-based cloud providers need to compete against each other to maximize their profits by setting cloud resource prices based on their pricing strategies. In this paper, we analyze how an auction-based cloud provider sets the auction price effectively when competing against other cloud providers in the evolutionary market where the amount of participated cloud users is changing. The pricing strategy is affected by many factors such as the auction prices of its opponents, the price set in the previous round, the bidding behavior of cloud users, and so on. Therefore, we model this problem as a Partially Observable Markov Game and adopt a gradient-based Multi-agent deep reinforcement learning algorithm to generate the pricing strategy. Furthermore, we run extensive experiments to evaluate our pricing strategy against the other four benchmark pricing strategies in the auction-based cloud market. The experimental results show that our generated pricing strategy can beat other pricing strategies in terms of long-term profits and the amount of participated users, and it can also learn cloud users’ marginal values and users’ choices of cloud providers effectively.
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Acknowledgement
This paper was funded by the Humanity and Social Science Youth Research Foundation of Ministry of Education (Grant No. 19YJC790111), the Philosophy and Social Science Post-Foundation of Ministry of Education (Grant No. 18JHQ060) and Shenzhen Fundamental Research Program (Grant No. JCYJ20190809175613332).
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Shi, B., Huang, L., Shi, R. (2020). Pricing in the Competing Auction-Based Cloud Market: A Multi-agent Deep Deterministic Policy Gradient Approach. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds) Service-Oriented Computing. ICSOC 2020. Lecture Notes in Computer Science(), vol 12571. Springer, Cham. https://doi.org/10.1007/978-3-030-65310-1_14
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DOI: https://doi.org/10.1007/978-3-030-65310-1_14
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