Video-on-demand (VoD) is one of the core CDN services, yet current VoD services commonly face video stuttering and long first-frame latency due to poor transmission path conditions. Existing solutions using Multi-path QUIC (MPQUIC) to address these issues cannot activate cellular paths on demand, causing massive traffic waste; additionally, some solutions depend on dedicated clients for application-layer information, precluding large-scale deployment. To address these limitations, we propose EMVOD, an elastic multi-path QUIC scheme deployed only on CDN edge nodes. It parses client-requested videos, dynamically activates cellular networks on demand, and prioritizes smooth video playback with minimal cellular traffic, while significantly reducing stuttering duration and first-frame latency.

Internet-wide scanning is a commonly used research technique in various network surveys, such as measuring service deployment and security vulnerabilities. However, these network surveys are limited to the given port set, not comprehensively obtaining the real network landscape, and even misleading survey conclusions. In this work, we introduce PMap, a port scanning tool that efficiently discovers the majority of open ports from all 65K ports in the whole network. PMap uses the correlation of ports to build an open port correlation graph of each network, using a reinforcement learning framework to update the correlation graph based on feedback results and dynamically adjust the order of port scanning. Compared to current port scanning methods, PMap achieves better performance on hit rate, coverage, and intrusiveness. Our experiments over real-world networks show that PMap can find 90% open ports by only scanning 125 ports (90% @125) to each active address with 136× less than the state-of-the-art port probing methods. PMap reduces the number of scanned ports to decrease the intrusive nature of port scanning. PMap is the first effective practice for scanning open ports using reinforcement learning. It bridges the gap of existing scanning tools and effectively supports subsequent service discovery and security research.