In data center networks, the growth rate of switch buffer size is much smaller than that of network bandwidth capacity. This results in frequent network congestion, which requires congestion control. Currently, the classification of end-to-end congestion control protocols is based on single dimensions such as time or congestion arbitration subjects.

A Chinese research team has achieved a breakthrough in improving the training efficiency of Graph Neural Networks (GNNs). They introduced an innovative architecture named "Decentralized Hypercube Collaborative Framework," addressing long-standing challenges such as high memory overhead, low computational efficiency, and underutilized hardware resources in traditional Graph Convolutional Network (GCN) training.

HUST researchers introduce DMCGuard to mitigate security risks in multi-channel IoT management.

Serverless computing typically uses secure containers encapsulated within lightweight microVMs to isolate function invocations across different tenants. However, to achieve high security guarantees, the Single-Container-Per-VM (SCPV) model can lead to significant memory inefficiency, as each microVM includes a separate guest OS, even when the container instances are identical.