The cement clinker compressive strength (CCCS) at the specified age is a significant indicator of the clinker quality. However, as for the existing CCCS measurement process, the test block must undergo standard curing for a specified age (long waiting time), which is of poor timeliness. Cement production enterprises have long yearned for a new approach to quickly obtain the CCCS values. In this article, a new method for predicting the CCCS with high accuracy immediately using only process information that can be quickly detected is proposed. First, a more efficient feature selection technique is designed to screen out the key input variable group for the CCCS modeling. Then, considering that the traditional single kernel support vector regression (SVR) is difficult to have both good learning and generalization abilities at the same time, the advantages of polynomial kernel function and K-type kernel function are combined to construct hybrid kernel SVR, which serves as the model structures of the 3 days’ CCCS (R3) and 28 days’ CCCS (R28). Finally, an improved grey wolf optimizer algorithm is proposed for the global optimal estimation of CCCS model parameters to further improve the prediction accuracy of the constructed models. The effectiveness of the overall work is verified through various comparative experiments. The experimental results prove that our proposed method can quickly establish accurate estimation models. The optimized SVR can predict the CCCS at specified ages very accurately and timely, which is beneficial for the production of various types of cement in industry.

WebAssembly (Wasm) is a low-level bytecode format that can run in modern browsers. With the development of standalone runtimes and the improvement of the WebAssembly System Interface (WASI), Wasm has further provided a more complete sandboxed runtime experience for server-side applications, effectively expanding its application scenarios. However, the implementation of WASI varies across different runtimes, and suboptimal interface implementations can lead to performance degradation during interactions between the runtime and the operating system. Existing research mainly focuses on overall performance evaluation of runtimes, while studies on WASI implementations are relatively scarce. To tackle this problem, we propose an eBPF-based WASI performance analysis framework. It collects key performance metrics of the runtime under different I/O load conditions, such as total execution time, startup time, WASI execution time, and syscall time. We can comprehensively analyze the performance of the runtime's I/O interactions with the operating system. Additionally, we provide a detailed analysis of the causes behind two specific WASI performance anomalies. These analytical results will guide the optimization of standalone runtimes and WASI implementations, enhancing their efficiency.

Configurable software systems are prone to configuration errors, resulting in significant losses to companies. However, diagnosing these errors is challenging due to the vast and complex configuration space. These errors pose significant challenges for both experienced maintainers and new end-users, particularly those without access to the source code of the software systems. Given that logs are easily accessible to most end-users, we conduct a preliminary study to outline the challenges and opportunities of utilizing logs in localizing configuration errors. Based on the insights gained from the preliminary study, we propose an LLM-based two-stage strategy for end-users to localize the root-cause configuration properties based on logs. We further implement a tool, LogConfigLocalizer, aligned with the design of the aforementioned strategy, hoping to assist end-users in coping with configuration errors through log analysis.

Software logs record system activities, aiding maintainers in identifying the underlying causes for failures and enabling prompt mitigation actions. However, maintainers need to inspect a large volume of daily logs to identify the anomalous logs that reveal failure details for further diagnosis. Thus, how to automatically distinguish these anomalous logs from normal logs becomes a critical problem. Existing approaches alleviate the burden on software maintainers, but they are built upon an improper yet critical assumption: logging statements in the software remain unchanged. While software keeps evolving, our empirical study finds that evolving software brings three challenges: log parsing errors, evolving log events, and unstable log sequences. In this paper, we propose a novel unsupervised approach named Evolving Log analyzer (EvLog) to mitigate these challenges. We first build a multi-level representation extractor to process logs without parsing to prevent errors from the parser. The multi-level representations preserve the essential semantics of logs while leaving out insignificant changes in evolving events. EvLog then implements an anomaly discriminator with an attention mechanism to identify the anomalous logs and avoid the issue brought by the unstable sequence. EvLog has shown effectiveness in two real-world system evolution log datasets with an average F1 score of 0.955 and 0.847 in the intra-version setting and inter-version setting, respectively, which outperforms other state-of-the-art approaches by a wide margin. To our best knowledge, this is the first study on localizing anomalous logs over software evolution. We believe our work sheds new light on the impact of software evolution with the corresponding solutions for the log analysis community.

Working set size estimation (WSS) is of great significance to improve the efficiency of program executing and memory arrangement in modern operating systems. Previous work proposed several methods to estimate WSS, including self-balloning, Zballoning and so on. However, these methods which are based on virtual machine usually cause a large overhead. Thus, using those methods to estimate WSS is impractical. In this paper, we propose a novel framework to efficiently estimate WSS with eBPF (extended Berkeley Packet Filter), a cutting-edge technology which monitors and filters data by being attached to the kernel. With an eBPF program pinned into the kernel, we get the times of page fault and other information of memory allocation. Moreover, we collect WSS via vanilla tool to train a predictive model to complete estimation work with LightGBM, a useful tool which performs well on generating decision trees over continuous value. The experimental results illustrate that our framework can estimate WSS precisely with 98.5% reduction in overhead compared to traditional methods.