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.

A reference geospatial information portal (GIP) architecture is designed for emergency management, which will bridge the gaps between different organizations and communities that have heretofore shared geospatial information only with great difficulties. The mode and its workflow are examined as well. In its implementation, general metadata fields are designated to describe the emergency information resources falling into two stages respectively. The portal provides the basis for an open, vendor-neutral facility that is intended to be an entry point of discovery for geospatial content in the context of designing and implementing the spatial data infrastructure for emergency management.