As microservice architectures become increasingly complex and system events become more frequent, Root Cause Analysis (RCA) has emerged as a critical task to ensure system reliability. However, existing deep learning-based methods often struggle with limited flexibility and a lack of interpretability when addressing complex system failures. Recent efforts to integrate large language models (LLMs) have shown promise in enhancing diagnostic transparency and reasoning capability. However, expansive search spaces, intricate workflows, and entangled constraints constrain practical adoption. We propose RCAFlow, a multi-agent framework that integrates structured workflow knowledge with hierarchical planning to address these challenges. RCAFlow transforms semi-structured documents into behavior tree-style workflows to support interpretable plan generation, employs a Git-inspired branching mechanism for modular and hierarchical task execution with path isolation, and leverages state-aware task execution with semantic analysis to improve result understanding and feedback. We evaluate RCAFlow on three benchmark datasets provided by OpenRCA. Experimental results demonstrate that RCAFlow consistently outperforms existing methods across all datasets. Further ablation studies confirm the effectiveness of each core module, highlighting the reliability, extensibility, and interpretability of RCAFlow to support complex RCA tasks within intelligent IT operations.

With the widespread deployment of large language models (LLMs) across diverse applications, optimizing their inference processes to achieve high throughput and low latency has become increasingly critical. While vLLM emerges as a high-performance inference engine that significantly accelerates processing speed, it continues to face challenges with elevated tail latency in high-concurrency scenarios. Sarathi-Serve addresses this through chunked-prefills and stall-free scheduling, achieving a balance between throughput and latency. However, our analysis reveals fundamental limitations in its static chunking strategy. This paper presents AdaptoServe, a system employing a dynamic adaptive chunk-based prefetching strategy to overcome the resource management and latency optimization limitations inherent in static chunking approaches. Our solution features intelligent chunk size adaptation through real-time monitoring of GPU utilization and work-load characteristics, ensuring optimal performance across varying resource conditions. Experimental evaluations demonstrate that AdaptoServe delivers substantial performance gains in diverse hardware configurations and work-load scenarios, effectively balancing throughput-latency tradeoffs. Compared to vLLM, AdaptoServe achieves up to 59.4 % higher request throughput, while outperforming Sarathi-Serve by$\mathbf{2 4. 4 \%}$through its dynamic chunking mechanism and enhanced multilevel feedback queue scheduling algorithm. We further investigate the strategy's applicability across different application scenarios, revealing significant optimization potential for next-generation LLM serving systems.

Time series forecasting for multiscale periodic data is critical in domains such as finance, meteorology, and energy, yet existing models struggle to effectively capture complex interactions across scales. Traditional approaches often fail to separate macro-scale periodic trends from micro-scale fluctuations, leading to limited forecast accuracy. To address these challenges, this paper introduces TimeShaper, a novel framework built upon the TimesNet architecture. TimeShaper employs sliding average techniques to decompose raw time series into macro-scale and micro-scale periodic components, thereby isolating key features and reducing noise. It further incorporates an innovative Causal Dilated Convolution module to extract intra-period and inter-period features from these decomposed components, enabling the model to utilize extensive historical periodic data while minimizing noise interference. Extensive experiments on multiple benchmark datasets demonstrate that TimeShaper consistently outperforms state-of-the-art methods in forecast accuracy, showcasing its robustness and practical applicability. By introducing this temporal decoupling and feature extraction methodology, TimeShaper provides a new perspective for tackling multiscale periodic time series forecasting tasks.

Technologies such as microservices, containerization and Kubernetes in cloud-native environments make large-scale application delivery easier and easier, but problem troubleshooting and fault location in the face of massive applications is becoming more and more complex. Currently, the data collected by the mainstream monitoring technologies based on sampling is difficult to cover all anomalies, and the kernel's lack of observability also makes it difficult to monitor more detailed data in container environments such as the Kuber-netes platform. In addition, most of the current technology solutions use tracing and application performance monitoring tools (APMs), but these technologies limit the language used by the application and need to be invasive into the application code, many scenarios require more general network performance detection diagnostic methods that do not invade the user application. In this paper, we propose to introduce network monitoring at the kernel level below the application for the Kubernetes cluster in Alibaba container service. By nonintrusive collection of user application L7/L4 layer network protocol interaction information based on eBPF, data collection of more than 10M throughputs per second can be achieved without modifying any kernel and application code, while the impact on the system application is less than 1%. It also uses machine learning methods to analyze and diagnose application network performance and problems, analyze network performance bottlenecks and locate specific instance information for different applications, and realize protocol-independent network performance problem location and analysis.

To support the migration of software applications to the cloud environment, cloud venders have proposed different migration methodologies and guidelines. Yet, most of them require human intervention, involving manually performing repetitive tasks. This paper proposes a pattern-based transformation approach for cloud application migration. The approach automatically modifies the source code of an application before the migration, to make it cloud-ready, and then transforms the source code to the target code in the cloud environment. The approach is supported by three key elements (patterns, rules and templates) and a process that systematically applies these elements. First, a pattern matching engine based on a regular expression processing technique is used to identify the parts of the source code that require modification and to extract the essential tokens from the source code for code transformation. Next, transformation rules are invoked to change the source code into the target code using a template, designed according to the target cloud environment. The proposed approach has been demonstrated on 19 open-source projects, by migrating them to Amazon Web Services.

Identifying business components is the basis of component-based software engineering. Many approaches, including cluster analysis and concept analysis, have been proposed to identify components from business models. These approaches classify business elements into a set of components by analyzing their properties. However, most of them do not consider the difference in their properties for the business elements, which may decrease the accuracy of the identification results. Furthermore, component identification by partitioning business elements cannot reflect which features are responsible for the generation of certain results. This paper deals with a new approach for component identification from business models using fuzzy formal concept analysis. First, the membership between business elements and their properties is quantified and transformed into a fuzzy formal context, from which the concept lattice is built using a refined incremental algorithm. Then the components are selected from the concepts according to the concept dispersion and distance. Finally, the effectiveness and efficiency are validated by applying our approach in the real-life cases and experiments.

Understanding business processes is an important step for software maintenance. The approaches for recovering business processes are mostly based on source code analysis, including static analysis and dynamic analysis. All these methods are proved to be effective in some specific situations. However, they are challenged when facing enterprise legacy systems which implement complex business processes triggered by the external actors. In this paper, we introduce a new business process recovery approach that combines the requirement reacquisition with dynamic and static program analysis methods. The approach has been applied to the maintenance of an equity trading system to prove its efficiency.