As application migration to the cloud becomes the mainstream approach for deployment, application runtime management requires large-scale workload prediction to ensure resource efficiency and system stability. However, existing forecasting models primarily focus on improving accuracy, often overlooking the impact of storage, training time, and inference time, leading to excessive computational overhead. Furthermore, cloud workloads exhibit high heterogeneity, with diverse patterns across containers, making it difficult for conventional models to generalize effectively. To address these challenges, this paper proposes FELT, a large-scale cloud workload prediction model through adaptive feature-enhanced and similarity-aware Transformer. FELT characterizes workload dynamics from both waveform and value perspectives, reducing model complexity while capturing macroscopic and detailed variations. We introduce a feature-enhanced workload similarity-aware algorithm that adaptively groups containers with similar workload patterns in real time by analyzing both historical and recent similarity, improving robustness in heterogeneous environments. Additionally, we leverage Transformer with customized position encoding and attention masks based on real-time workload similarity and employ multi-head self-attention for parallelized training, achieving a balance between accuracy and efficiency. Extensive experiments on public datasets demonstrate FELT’s superiority in both prediction accuracy and overhead, with ablation studies further validating the effectiveness of its components.

Process control systems typically comprise multiple variables that impact system parameters, interacting and constraining each other with the objective of maintaining parameters within a specified range to ensure dynamic equilibrium. The reliability and safety of these systems are also of paramount concern. The multi-factor Balanced Feedback Net (BFN) represents one of the significant models for simulating systems with balanced feedback mechanisms. This paper refines and expands the definition of BFN, creating BFN models that incorporate varying quantities of balance factors. Based on this, we analyze the structural properties of BFN and provide relevant proofs. Given the complexity of BFN modeling, this paper introduces an innovative approach to translating real-world process control systems into BFN and develops an algorithm to assist users in automatically constructing BFN. For extreme situations that may arise in process control systems, we present an early recognition algorithm for extreme states in BFN. Theoretical proofs and case analyses complement each other, as demonstrated by the example of the water level control system of a steam boiler. This illustrates the effectiveness of methods and algorithms in complex system control and optimization.

It is important to understand the process of cancer cell metastasis and some cancer characteristics that increase disease risk. Because the occurrence of the disease is caused by many factors, and the pathogenesis process is also complicated. It is necessary to use interpretable and visual modeling methods to characterize this complex process. Machine learning techniques have demonstrated extraordinary capabilities in identifying models and extracting patterns from data to improve medical prognostic decisions. However, in most cases, it is unexplainable. Using formal methods to model can ensure the correctness and understandability of prediction decisions in a certain extent, and can well visualize the analysis process. Coloured Petri Nets (CPN) is a powerful formal model. This paper presents a modeling approach with CPN and machine learning in breast cancer, which can visualize the process of cancer cell metastasis and the impact of cell characteristics on the risk of disease. By evaluating the performance of several common machine learning algorithms, we finally choose the logistic regression algorithm to analyze the data, and integrate the obtained prediction model into the CPN model. Our method allows us to understand the relations among the cancer cell metastasis and clearly see the quantitative prediction results.