Dealer: An Efficient Pricing Strategy for Deep-Learning-as-a-Service

Deep learning combining with cloud computing is a surging technology recently which is a new paradigm called DLAS (deep learning as a service). To supply good services, resource utilization and user performance must be considered and satisfied. In this paper, we formulate a competitive market between a provider and users in cloud computing. Then, we schedule multi-types computational virtualized resources including CPU, GPU and TPU to maximize the revenue of users and the provider. To find the optimal solution, we propose two efficient decision and pricing strategies called Dealer strategies for users and the provider, respectively. Finally, We evaluate our method compared with Elastic pricing strategy and Amazon EC2, and Dealer strategies can bring better revenue than others.

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