Bilevel Fee-Setting Optimization for Cloud Monitoring Service Under Uncertainty

We introduce a new paradigm for the cooperation of cloud monitoring and study the annual fee-setting problem under uncertainty. First, a value network of cloud monitoring and different monitoring service modes are studied. Second, we express the fee-setting problem for cloud monitoring as a chanceconstrained bilevel optimization model, where the fee-setter acts as a leader, and all the clients act as followers who make decisions based on the price they receive. Third, optimal fee-setting and the interaction between the platform and clients are analyzed. Finally, numerical experiments are designed and implemented on a monitoring platform, and pricing policy and decision-making under uncertainty on unplanned downtime rate are analyzed. The proposed paradigm and model do not only target a specific cloud service, and thus they can be applied to other similar public platforms provided as services.