This paper presents stable reinforcement learning through adversarial imitation learning (SRAIL), a two-phase learning framework for automatic proportional integral derivative (PID) tuning in the absence of high-quality demonstration data. First, adversarial imitation learning (AIL) is used to learn an initial control policy using imperfect demonstration data. Subsequently, reinforcement learning (RL) refines this initial policy to derive the optimal PID control policy. SRAIL is designed to ensure stable and safe exploration of the RL agent, even with imperfect demonstration data, thereby enabling efficient and automatic tuning in industrial environments. Conventional PID tuning methods are time-consuming owing to system complexity and lack of prior knowledge. Furthermore, the random exploratory nature of RL presents significant risks during the early training phase, limiting its practical application. SRAIL addresses these challenges by ensuring stable control performance despite the absence of high-quality demonstration data and by enhancing the convergence speed of RL. The experimental results demonstrate that SRAIL minimizes random exploration in the initial learning phase, ensuring safety, and highlighting its potential for implementation of RL in real-world industrial environments.

Forecasting workloads and responding promptly with resource scaling and migration is critical to optimizing operations and enhancing resource management in cloud environments. However, the diverse and dynamic nature of devices within cloud environments complicates workload forecasting. These challenges often lead to service level agreement violations or inefficient resource usage. Hence, this paper proposes an Enhanced Long-Term Cloud Workload Forecasting (E-LCWF) framework designed specifically for efficient resource management in these heterogeneous and dynamic environments. The E-LCWF framework processes individual resource workloads as multivariate time series and enhances model performance through anomaly detection and handling. Additionally, the E-LCWF framework employs an error-based ensemble approach, using transformer-based models and Long-Term Time Series Forecasting (LTSF) linear models, each of which has demonstrated exceptional performance in LTSF. Experimental results obtained using virtual machine data from real-world management information systems and manufacturing execution systems show that the E-LCWF framework outperforms state-of-the-art models in forecasting accuracy.

In the manufacturing process, process monitoring is very important. Real-time contrast (RTC) control chart outperforms existing monitoring methods. However, the performance of RTC control chart depends on the classifier. The existing RTC charts use random forest (RF), support vector machine (SVM), or kernel linear discriminant analysis (KLDA) as a classifier. RF classifier can find cause of faults but the performance is lower than others. Therefore, we suggest the data representation method to improve the RF based RTC control chart. Symbolic aggregate approximation (SAX) is famous method to improve the performance of classification and clustering. We convert the input data by using SAX. We change the parameters of SAX such as alphabet size and breakpoints to improve the performance. Experiment shows that represented data is efficient method to improve the performance of RTC control chart.