In order to keep up with the ever-changing world of services, it is essential to fix a broken service composition in a cloud-edge environment when it fails or when users' requests change. However, current methods for replanning or repairing a service composition need to take a while to generate a new solution. To address this issue, we propose using a damaged planning graph repairing approach with graph embeddings to repair broken service compositions in the cloud-edge environment. To begin with, our approach employs graph embedding to acquire the low-dimensional vectors of service nodes within the service network graph. Secondly, we compute the candidate service set by utilizing the failed service, its parameters, and the user's initial input through graph embedding. Thirdly, we design an approach for repairing service compositions in a cloud-edge environment by relying on the candidate service set and the damaged planning graph. To verify the validity of the proposed approach, we analyze and compare the performances of the certain proposed method with referencing re-planning and repair methods, and the proposed method may repair a service composition within a shorter time.

Automated service composition fulfills complex tasks by combining different existing web services together. Unfortunately, optimizing service composition is still a challenging area that needs to be addressed. In this article, we propose a novel relational database approach for automated service composition. All possible service combinations are generated beforehand and stored in a relational database. When a user request comes, our system composes SQL queries to search in the database and return the best Quality of Service (QoS) solutions. We test the performance of the proposed system with a web service challenge data set. Our experimental results demonstrate that this system can always find top-K valid solutions to satisfy user's functional and non-functional requirements.

Web service composition enables the provision of existing resources on the web without investing in new infrastructure. However, searching an optimal composition solution with both functional and non-functional requirements is a computationally demanding problem: the time and space requirements may be insufferable due to the high number of available services. To alleviate this problem, we propose the application of a skyline operation to reduce the search space and improve the scalability. We design a system to solve the composition problem with two separate processes. The Graphplan approach finds a solution in a short time, the database approach may take longer time to find a solution, but the solution returned by this approach always has fewer redundant services with a better QoS value. Full Solution Indexing using Database (FSIDB) approach pre-computes all services combinations and store them as paths in a database. Partial pre-composing approach chooses "popular" paths generated by FSIDB approach and store them in a separate table. If the problem can be solved by these paths, there is no need to search the table with whole paths. We evaluate our approach with a web service challenge dataset.