Current GPU Collective Communication Libraries (CCLs) employ predefined schedules optimized for stable environments. Their supported schedules and selection logic are fixed at communicator initialization, which fails to account for evolving runtime conditions, such as workload characteristics and hardware health status. Consequently, long-running GPU jobs experience suboptimal performance after hours or days of execution, which translates into longer job completion times and wasted GPU cluster resources. To address this problem, we present Theseus, a novel CCL backend that provides schedule-level runtime adaptivity. It admits user-defined schedules and selection policies. As runtime conditions change, Theseus selects suitable schedules using cluster-wide runtime attributes beyond CCL-internal metrics. Moreover, it hot-swaps from the previous schedule consistently across GPUs with low overhead. Theseus acts as a drop-in replacement to facilitate integration. We evaluate Theseus extensively on various GPU workloads with intuitive policies. Compared with NCCL, Theseus achieves up to 1.61X speedup of communication time in stable environments and 2.46X in dynamic environments. It improves end-to-end job completion time by up to 1.84X while incurring comparable or lower overhead.

Ensuring service availability in large-scale datacenters hinges on network monitoring. For monitoring quality, it is essential to attain sufficient coverage of all physical components. However, given the ever-evolving complexity of industrial environments, even measuring coverage metrics becomes challenging, let alone attaining sufficient coverage. In fact, insufficient coverage widely existed in our production datacenters and caused many missed failures.

Sketch algorithms have been widely deployed for network measurement as they achieve high accuracy with restricted resource usage. They store measurement results compactly in fixed-size counters. However, as sketch counters are skewed towards low values, higher bits in most counters remain zero. Such massive unused bits impair the space efficiency valued by sketch algorithms. Unfortunately, efforts to mitigate the issue either apply to specific algorithms or compromise accuracy. In this paper, we design BitSense, a novel optimization framework that integrates with existing sketch algorithms. The key idea is to regard higher bits in sketch counters as a sparse vector and leverage compressive sensing techniques to compress and restore counters. Further, BitSense provides a programming model to help developers easily realize sketch algorithms without dealing with the details of compression and recovery. Bit-Sense proposes an automatic approach for parameter configuration. It theoretically guarantees nearly zero error under the configuration. We have built a BitSense prototype in P4 and a software platform and integrated it with fourteen sketch solutions. Extensive experiments show that BitSense significantly reduces the memory usage of existing sketch solutions by 25%-80% while incurring little overhead and almost zero accuracy drop, outperforming five state-of-the-art optimization frameworks.