## 6 Conclusions  
With the increasing popularity of cloud computing, there has been an influx of cloud service providers offering similar cloud services but differing in QoS attributes. Moreover, the number of clouds has increased, resulting in scenarios where the required services for a request are spread across multiple clouds, making cross-cloud scheduling more common. In such multi-cloud environments, cloud service composition faces complex situations, such as additional energy consumption due to data transmission and increased variability from multiple clouds. Therefore, this paper proposes the skyline-enhanced deep reinforcement learning approach to minimize the energy consumption of cloud service composition in a multi-cloud environment while optimizing QoS. We define an energy consumption model for cloud service composition in a multi-cloud environment and incorporate it into the reward function to guide the agent's choice of low-energy services. To reduce the algorithm execution time and simplify optimization difficulty, we integrate the branch and bound skyline algorithm to effectively narrow the search space. We improve the basic DQN algorithm by incorporating double DQN to address overestimation problems, utilizing Dueling Network and Prioritized Experience Replay to accelerate training speed and enhance stability. We conduct a series of experiments to validate the effectiveness of our proposed method in reducing energy consumption while performing well in terms of execution time, adaptability, computation time, and scalability in a multi-cloud environment. The experimental results demonstrate that SkyDRL effectively reduces energy consumption and finds relatively optimal cloud service composition solutions in multi-cloud environments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Subtasks** \\ \end{tabular} } & \begin{tabular}{c} **Number of** \\ **Candidate** \\ **Services** \\ \end{tabular} & \multirow{2}{*}{**QoS Value**} & \multirow{2}{*}{\
\begin{tabular}{c} **Number of** \\ **Subtasks** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Candidate** \\ **Services** \\ \end{tabular} } & \multirow{2}{*}{**QoS Value**} \\ \cline{1-1}  & & & & & \\ \hline
30 & 300 & 20.63 & 50 & 500 & 29.13 \\ \hline
30 & 400 & 19.79 & 60 & 500 & 36.10 \\ \hline
30 & 500 & 21.38 & 70 & 500 & 42.27 \\ \hline
30 & 600 & 21.51 & 80 & 500 & 49.69 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scalability test.  
Although our method has achieved good results, we are also aware that there are still some potential limitations. For example, since the QoS of services is usually dynamically changing, in the future, we plan to use QoS prediction techniques to further improve the flexibility and practicality of the method. In addition, we also hope to introduce multi-agent technology to improve learning efficiency through communication between multiple agents in order to further improve the performance of our method. We also intend to address the uncertainty factors of cost, response time, and reliability in service composition. To model these factors, we plan to employ the method of intuitionistic fuzzy sets.  
Writing--original draft, W.M.; Writing--review & editing, H.X. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Scientific Research Plan Projects of the Education Department of Jiangxi Province of China, grant No. GJJ160554, the Talent Plan Project of Fuzhou City of Jiangxi Province of China, grant No. 2021 ED008, and the Opening Project of Jiangxi Key Laboratory of Cybersecurity Intelligent Perception, grant No. JKLCIP202202.  
Not applicable.  
The original data used in this study can be downloaded from [[https://qwsdata.github.io](https://qwsdata.github.io)]([https://qwsdata.github.io](https://qwsdata.github.io)).  
The authors declare no conflict of interest.