Cloud computing provides blockchain a flexible and cost-effective service by on-demand resource sharing, which also introduces additional security risks. Adaptive Cyber Defense (ACD) provides a solution that continuously changes the attack surface according to the cloud environments. The dynamic characteristics of ACDs give defenders a tactical advantage against threats. However, when assessing the effectiveness of ACDs, the structure of traditional security evaluation methods becomes unstable, especially when combining multiple ACD techniques. Therefore, there is still a lack of standard methods to quantitatively evaluate the effectiveness of ACDs. In this paper, we conducted a thorough evaluation with a hierarchical model named SPM. The proposed model is made up of three layers integrating Stochastic Reward net (SRN), Poisson process, and Martingale theory incorporated in the Markov chain. SPM provides two main advantages: (1) it allows explicit quantification of the security with a straightforward computation; (2) it helps obtain the effectiveness metrics of interest. Moreover, the hierarchical architecture of SPM allows each layer to be used independently to evaluate the effectiveness of each adopted ACD method. The simulation results show that SPM is efficient in evaluating various ACDs and the synergy effect of their combination, which thus helps improve the system configuration accordingly.