The field of distributed systems and networking is witnessing significant advancements, driven by the need for efficient resource allocation, improved performance, and enhanced sustainability. Researchers are exploring innovative approaches to construct accurate service dependency graphs, optimize resource allocation in heterogeneous clusters, and develop adaptive telemetry systems for performance diagnosis. Notably, the integration of machine learning and artificial intelligence is becoming increasingly prevalent, enabling the development of predictive models for auto-scaling, performance prediction, and load balancing. These advancements have the potential to significantly improve the efficiency, scalability, and reliability of distributed systems and networks. Noteworthy papers include: GOGH, which proposes a learning-based architecture for managing machine learning workloads in heterogeneous clusters. Host-Side Telemetry for Performance Diagnosis in Cloud and HPC GPU Infrastructure, which introduces an eBPF-based telemetry system for unified host-side monitoring of GPU workloads. Morpheus, which develops lightweight and accurate RTT predictors for performance-aware load balancing.