The safe operation of metro systems, particularly in densely populated cities, relies on the effective management of crowd gatherings within stations. Existing research primarily focuses on camera-based crowd counting but fails to consider crowd movement across multiple interconnected spaces, limiting its effectiveness in complex metro environments. This paper proposes a real-time Edge-Cloud collaborative video analytics system for Crowd Gathering Event Detection (EC-CGED), integrating an edge-cloud collaboration mechanism, spatial knowledge model, and an event-driven adaptive dynamic scheduling strategy. The system enables fine-grained, real-time monitoring of crowd dynamics and provides early warnings of potential crowd gatherings. At the edge nodes, real-time video streams are decoded and analyzed to extract crowd counting indicators for various station areas and transit channels. These indicators, along with spatial knowledge of the metro station layout, are aggregated at the central cloud server to enhance the accuracy of crowd gathering event detection. Additionally, we introduce an event-driven adaptive dynamic task scheduling strategy to optimize computational resource allocation, improving system efficiency. By enabling timely detection and proactive alerts for crowd gatherings, the EC-CGED system enhances metro station safety and operational efficiency, addressing critical challenges in urban public transportation management.

Workload characterization is critical for resource management and scheduling. Recently, with the fast development of container technique, more and more cloud service providers like Google and Alibaba adopt containers to provide cloud services, due to the low overheads. However, the characteristics of co-located diverse services (e.g., interactive on-line services, off-line computing services) running in containers are still not clear. In this paper, we present a comprehensive analysis of the characteristics of co-located workloads running in containers on the same server from the perspective of hardware events. Our study quantifies and reveals the system behavior from the micro-architecture level when workloads are running in different co-location patterns. Through the analysis of typical hardware events, we provide recommended/unrecommended co-location workload patterns which provide valuable deployment suggestions for datacenter administrators.