## 7 Conclusions and Future Work  
This research aimed to identify the key differences in system features, usability, and performance between Google Cloud Run, AWS App Runner, and Microsoft Azure Container Apps serverless cloud computing systems with respect to a containerized mobile web application. After thoroughly examining the features of serverless services offered by GCP, AWS, and Azure, it was observed that the Google Cloud Run service stands out with its extensive range of memory and CPU options. It also supports a greater number of containers, instances per service, and maximum concurrency per instance compared to its competitors. The AWS App Runner service and Azure Container Apps were found to be the next-best options. When it comes to the ease of use and usability of services, Google Cloud Run provides the easiest-to-understand documentation and educational materials for beginners. Azure and AWS also offer helpful resources, but Google Cloud Run stands out as the most beginner-friendly option. From the performance analysis of the services, we found that the performance of the Google Cloud Run service was better in regards to container request latency and distance matrix API response time. Cloud Run exhibited the lowest latency at 50 ms, while AWS App Runner had a higher latency, with an average request latency of 91 ms. Azure Container Apps monitoring services cannot capture the container latency for the applications. Cloud Run also showed a faster response time of 63.7 ms for distance matrix queries while App Runner and Container Apps have an average response time of 71.38 and 66.8 ms respectively. Meanwhile, Azure Container Apps exhibited the lowest CPU memory utilization with an average utilization of 0.000773% compared to the other two options. Cloud Run uses an average of 23% and AWS App Runner uses the highest amount of CPU, with an average utilization of 47%. From these findings, our suggestion is to opt for GCP as the primary choice, followed by Azure, for those developers who are new to the concept of serverless computing. On the other hand, AWS is a suitable option for developers already well-acquainted with serverless computing. These key contributions will help future academic researchers and developers when deciding to choose a serverless cloud platform for deploying similar containerized applications. The analysis of performance results in this comparative study was focused solely on the particular mobile web application. However, there is potential for conducting further research on other application domains and exploring a broader range of cloud providers beyond those examined in this study. While the findings of the study are practical, the results have implications for future research and development practices in serverless cloud computing. First, to support the study's findings more applicably and objectively to other types of applications or broader contexts in serverless computing, a more thorough examination of factors affecting performance differences such as platform architecture, underlying infrastructure, and/or specific optimization in future studies could enhance the depth of analysis and generalization. A detailed discussion on network effects and the impact of scaling on performance measures along with a more rigorous statistical analysis could further ensure the reliability of the results. Second, including a balanced discussion with challenges and disadvantages such as cost implications, security concerns, and limitations in specific use cases would offer a more comprehensive perspective. Additionally, a subjective analysis of each platform's documentation and learning curve with surveys and experiences from a broader range of users could strengthen the usability study. All authors contributed equally to this project: writing--original draft, software, investigation, A.A.; conceptualization, methodology, writing--review and editing, supervision, funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.