## 5 Experiments
### Adaptability  
The multi-cloud environment is a complex and dynamic system where many factors can influence the outcome of service composition. For instance, the QoS attributes of services may change due to a variety of reasons, such as seasonal factors or internal issues specific to the service provider. Additionally, certain services may become temporarily unavailable, further complicating service composition. In such scenarios, it is crucial to have a method that can identify the optimal solution within a given timeframe without requiring manual parameter adjustments or human intervention. This ability is known as adaptability.  
Figure 8: QoS for PD-DQN, Q-learning with skyline, SFGA, MAACS, and SkyDRL, with 60 subtasks but different numbers of candidate services for each subtask.  
Figure 9: QoS for PD-DQN, Q-learning with skyline, SFGA, MAACS, and SkyDRL, with 200 candidate services but different numbers of subtasks.  
To evaluate the adaptability of our proposed approach, we conducted an experiment using 60 subtasks and 200 candidate services for each subtask. To simulate the dynamic changes in the cloud environment, we followed the methodology outlined in the references [39; 43] by randomly disabling a certain proportion of cloud services. Figure 10 shows the experimental results obtained. After the algorithm reached a stable state, we randomly disabled a certain percentage of cloud services in four different scenarios: no disabled services and 1%, 5%, and 10% of randomly disabled services. This approach forced SkyDRL to readjust and learn, finding a new service composition. As seen from Figure 10, the higher the percentage of disabled cloud services, the greater the fluctuations in the algorithm and the longer the adjustment time. Moreover, when a high percentage of services was disabled, such as 10%, the QoS value of the newly found service composition decreased. This was due to the loss of high-QoS services as a result of disabling some services, which affected the final QoS value of the selected service composition. Conversely, when a small percentage of services was disabled, such as 1%, the impact on the algorithm's performance was minimal and had little influence on the final outcome. Nevertheless, SkyDRL demonstrated its adaptability by readjusting its strategy and finding a new service composition, regardless of the percentage of disabled services.  
Figure 11 displays the QoS values of the newly found service composition after disabling different percentages of cloud services. Unlike heuristic algorithms designed for specific problems that require modifications and parameter adjustments when the environment changes, DRL can learn the optimal policy through training and update weights adaptively based on the environment. As shown in Figure 11, as the percentage of disabled services increased, the QoS values of heuristic algorithms dropped significantly due to environmental changes. In contrast, algorithms based on reinforcement learning were less affected by environmental changes, with the QoS values decreasing only slightly.  
Figure 10: Adaptability of SkyDRL in solving MCECSC when certain percentages of services are made unavailable.