Continuous integration, deployment and delivery (CI/CD) have become cornerstones of DevOps practices. In recent years, GitHub Action (GHA) has rapidly replaced the traditional CI/CD tools on GitHub, providing efficiently automated workflows for developers. With the widespread use and influence of GHA, it is critical to understand the existing problems that GHA developers face in their practices as well as the potential solutions to these problems. Unfortunately, we currently have relatively little knowledge in this area. To fill this gap, we conduct a large-scale empirical study of 6,590 Stack Overflow (SO) questions and 315 GitHub issues. Our study leads to the first comprehensive taxonomy of problems related to GHA, covering 4 categories and 16 sub-categories. Then, we analyze the popularity and difficulty of problem categories and their correlations. Further, we summarize 56 solution strategies for different GHA problems. We also distill practical implications of our findings from the perspective of different audiences. We believe that our study contributes to the research of emerging GHA practices and guides the future support of tools and technologies.

Anomaly detection based on deep learning has been widely used in IT infrastructure management. Driven by large amount of data, deep learning (DL) based algorithms can achieve higher accuracy compared to rule-based algorithms. However, the computational complexity of these algorithms is much higher than traditional rule-based ones, which will cost lots of time and computing resources. This limits the application of such algorithms in some actual systems. Therefore, it is necessary to improve the detection efficiency (e.g., execution time, resource occupation, etc.) of existing DL-based algorithms to make them more practical. In this work, we propose an efficient detection approach to solve this problem by combining DL-based algorithms and rule-based algorithms. Specificly, we apply rule-based algorithms to filter the original data roughly and then exploit the DL-based algorithms to make the final decision. We evaluate our approach using two state-of-the-art deep learning anomaly detection approaches with three real-world datasets. The results show that the proposed approach can significantly improve detection efficiency, saving about 80%~95% of execution time under the premise that the accuracy is nearly unchanged.

There have been dramatic increases in industrial investment and growing research interests in Internet of Things over the past few years. Driven by the application demand from Internet of everything in the future, the collaboration between traditional cloud and edge cloud is already on the way to provide better service to diverse users. However, due to the high marginal operation and maintenance costs, there remains a need for collaboration between traditional clouds. JointCloud computing is a new computing paradigm that supports collaboration within heterogeneous clouds, which is a complex problem that is difficult to manage using the traditional methodology of “top-down, decomposition, and reduction” in the benign construction of the JointCloud ecosystem. In this article, we first describe the inherent mechanism behind the constructional evolution of the JointCloud ecosystem, which details the inner workings of its elements and the general principals of how these various elements influence each other. We then propose a series of forward-looking, feasible operational methods to support the constructional evolution of the JointCloud ecosystem that incorporate efficient development methods, data-driven operations, AI-assisted decision making and so on. Finally, a case study of a cross-cloud migration deployment is used to illustrate the scientific basis of the systems inherent mechanism, as well as the efficacy of the supporting methods.