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.

Transmission schemes in data centers are supposed to accurately distinguish flow types for different scheduling. However, prior efforts failed to meet the needs at all levels in a cost-effectively way. Nor the existing schemes proved applicable to all the diverse scenarios or dynamic traffic patterns. Therefore, we proposedDifferentiatedTraffic schEduling in dAta cenTers (DiffTREAT) based the Recurrent Neural Network (RNN), aiming to simplify the transmission in the dynamic and diverse network scenarios. First, DiffTREAT utilizes deep learning methods for traffic classification and flow size prediction. Second, according to the classified results of flows, DiffTREAT adopts multilevel priority queues to ensure the preferential transmission of latency-sensitive flows while optimizing the overall average flow completion time (FCT). Third, DiffTREAT employs the network cache to increase the capacity of data center networks (DCN), which effectively fights against the traffic burst and improves the throughput of latency-insensitive flows. DiffTREAT has been tested in different topologies in the contexts of diverse network loads and real-world workloads. Experiment results showed that compared with state-of-the-art schemes, DiffTREAT yielded both the lower average flow completion time for latency-sensitive flows and the higher throughput for latency-insensitive flow.