LAUSR

The cloud resource provision policy of a content provider in the presence of competitors on globally distributed cloud platforms plays a significant role in maximizing its profit. However, developing an optimal resource provision policy is quite challenging, due to the difficulty to capture the competition relationship between two competitive CPs and to obtain the budget of the competitors which is usually kept private. To solve this problem, in this article, we propose a learning-driven cloud resource provision policy for a CP with competitors. We formulate the competition between the CPs as a lottery Colonel Blotto game in which the payoff of each region is positively related to the resource advantage achieved by the CP, formulate the budget allocation problem as a Markov decision process, and obtain the sub-optimal resource provision policy by reinforcement learning and deep reinforcement learning-based algorithms. We also prove the convergence of the sub-optimal solution. Finally, we validate our proposed method using real-world CPs statistics. The results show that the budget information is critical for a CP to make policy decisions, and it is better for CPs with smaller budget to focus their budget resources in regions with higher values.