Modern cloud applications, from interative web services to mobile and WoT workloads, generate highly dynamic multi-tenant network demands. Guaranteeing priority-aware bandwidth remains challenging: legacy shapers like Linux Traffic Control Hierarchical Token Bucket are static and unscalable, while cloud-native solutions such as Cilium offer only coarse-grained rate limiting. We present BeeQoS, a cloud-native QoS system that delivers low-latency, adaptive, and scalable multi-priority bandwidth guarantees. BeeQoS consists of an eBPF-powered data plane for high-performance, fine-grained per-packet shaping, a demand-aware control plane that senses real-time flow requirements and adaptively reallocates bandwidth, and seamless Kubernetes integration for expressive policy specification and cluster-wide scalable deployment. Evaluation shows that BeeQoS scales to 1K+ flows with stable performance, boosts high-/medium-priority throughput by 14.6%/36.4%, cuts median latency by 72.4%, reduces deployment overhead, and improves video QoE by 27.3% over state-of-practice baselines.

While existing far memory and disaggregated memory solutions provide a foundation for addressing limitations of single-node memory capacity and inefficient resource allocation in data centers, they predominantly focus on host memory, overlooking the critical demands of GPU-centric workloads. A key bottleneck in scaling GPU memory is the lack of connectivity and interoperability between GPUs, which is exacerbated by their heterogeneity. To bridge this gap, this paper proposes UHM, a unified data transferring and memory pooling scheme for heterogeneous GPU memories. UHM establishes the communication channels between heterogeneous GPU/host memories and leverages double data buffers for pipelined and reliable transfer. Furthermore, UHM unifies both local and remote memories to build a memory pool. The pooling scheme effectively integrates local and remote resources, performs efficient caching management in local memory, and optimizes memory block management for remote memory resources. Evaluation on a heterogeneous GPU cluster demonstrates that UHM significantly reduces the data transfer latency (up to 87.2%), improves the cache hit ratio, reduces runtime memory allocation latency (up to 94.7%), while enhancing the overall memory utilization (24.7%).

Modern cluster management platforms, such as Kubernetes, implement declarative interfaces based on state-centric principles and extend cluster capabilities through operators. Once the desired system state is declared, operators automatically reconcile the actual state to align with the desired state. Ensuring state correctness after operations is critical for system safety and stability, as incorrect or unhealthy states can lead to serious consequences. However, there is currently no systematic process in place to guarantee state correctness. The vast state space presents significant challenges in detecting and correcting abnormal states. To tackle this issue, we propose Arsc, an automated technique for test generation, detection, and correction aimed at maintaining state correctness. Arsc establishes the definition of state correctness, offers a state detection method, and presents three strategies for state correction. It was evaluated on five popular open-source operators, generating 11,581 tests and detecting 208 incorrect states, 71 unhealthy states, and 28 crashes, all of which were effectively corrected.