Customer Adaptive Resource Provisioning for Long-Term Cloud Profit Maximization under Constrained Budget

As an efficient commercial information technology, cloud computing has attracted more and more users and enterprises to use it. Faced with such a large number and variety of customers, it is necessary for cloud providers (CPs) with limited budget to provide satisfactory customized pricing services, profitable customer and system investments, and flexible system resource provisioning strategies to improve both customer experience and long-term profit. Existing profit optimization research rarely considers customer diversity and dynamics, which may have a negative impact on long-term profit growth due to poor management of customer relations. In this article, we implement customer relationship management by considering both customer diversity and dynamics, and propose a customer adaptive resource provisioning scheme to maximize long-term profit under constrained budget. We consider four customer types (i.e., loyal, old, new, and lost) that can transition to each other during the customer's lifetime of interaction with the CP. The CP builds multiple cloud service sub-platforms, each of which contains multiple multiserver systems and serves the same type of customers. For the cloud service platform, we first analyze single multiserver system using an analytical method to obtain its optimal profit, invested funding, and system configuration. In particular, for systems serving new and lost customers, we develop a novel customer lifetime value (CLV)-based customer investment scheme that selects valuable customers for investment under limited marketing budget. Based on the above analysis, we then present a customer retention rate (CRR)-driven three-stage heuristic scheme that prioritizes investment in multiserver systems with endangered customers under limited infrastructure budget for reducing customer churn and promoting long-term profit growth. We conduct extensive simulation experiments to validate the effectiveness of our method. Simulation results show that compared with the benchmark algorithms, our method can improve the long-term profit and CRR by up to 3.4x and 7.8x, respectively.