We confront two challenges in the management of a vast and diverse array of online web applications deployed on enterprise-grade auto-scaling infrastructure, primarily focused on ensuring Quality of Service (QoS) for large-scale applications and optimizing resource costs. Firstly, reacting to increased load with a response-based approach can temporarily degrade QoS because many web applications need a few minutes to warm up. Therefore, precise workload prediction is critical for predictive scaling. However, our analysis of real-world applications underscores the substantial challenges arising from the limited precision and robustness of existing single prediction algorithms in the context of predictive auto-scaling. Secondly, guaranteeing the QoS of online applications within a cost-effective structure is crucial, as it is inherently linked to corporate profitability. Nevertheless, our study shows that mainstream auto-scaling methods exhibit various limitations, either being unsuitable for online environments or inadequately ensuring QoS.

Data driven method based on big data and deep model is an effective tool to solve the problem of online power system transient stability assessment. Nevertheless, when the system suffers from some large disturbances such as the changes of topology, there is no proper way for a model to supplement the original knowledge base but to retrain it from scratch. Meanwhile, with the continuous emergence of new scenarios, data management has become a new dilemma reflected in the increase of data accumulation and retraining time. To address these issues, a novel TSA framework with continual learning ability is proposed. First, the improved convolutional neural network-based orthogonal weight modification algorithm is selected as a transient stability predictor. Then, large disturbance is regarded as a new recognition task which can be continuously learned by the predictor. Through the proposed updating process, the original knowledge base can be updated and supplemented only by the data corresponding to new scenario. The test results on two power systems show that the proposed scheme can realize the tradeoff between the integrity of knowledge base and the update speed without losing too much accuracy in a limited capacity.