Competition for the last-level cache (LLC) is a long-standing issue in multi-tenant cloud environments, often leading to severe performance interference among co-located virtual machines. LLC management in the cloud faces unique challenges, including unpredictable tenant workloads, misaligned performance metrics, and the need to ensure fairness under service level agreements (SLAs). Existing LLC allocation methods fall short in addressing these challenges. We present Cacheman, a comprehensive LLC management system designed from real-world cloud deployment experience. Cacheman introduces a novel gradient-based sharing mechanism for LLC ways, enabling smooth LLC allocation adjustments that simultaneously improve fairness and utilization efficiency. Its real-time allocation algorithm promptly detects and mitigates unfair LLC allocation, adapting to dynamic workloads with second-scale responsiveness. Additionally, Cacheman supports performance consistency for tenants running distributed applications by enforcing negotiated upper bounds on cache usage. Extensive experiments demonstrate that Cacheman effectively achieves its multi-dimensional goals, and long-term production deployment further shows that it significantly reduces SLA violations caused by LLC contention.

Dataset distillation techniques have revolutionized the way of utilizing large datasets by compressing them into smaller, yet highly effective subsets that preserve the original datasets' accuracy. However, while these methods have proven effective in reducing data size and training times, the robustness of these distilled datasets against adversarial attacks remains underexplored. This vulnerability poses significant risks, particularly in security-sensitive applications. To address this critical gap, we introduce DD-RobustBench, a novel and comprehensive benchmark specifically designed to evaluate the adversarial robustness of distilled datasets. Our benchmark is the most extensive of its kind and integrates a variety of dataset distillation techniques, including recent advancements such as TESLA, DREAM, SRe2L, and D4M, which have shown promise in enhancing model performance. DD-RobustBench also rigorously tests these datasets against a diverse array of adversarial attack methods to ensure broad applicability. Our evaluations cover a wide spectrum of datasets, including but not limited to, the widely used ImageNet-1K. This allows us to assess the robustness of distilled datasets in scenarios mirroring real-world applications. Furthermore, our detailed quantitative analysis investigates how different components involved in the distillation process, such as data augmentation, downsampling, and clustering, affect dataset robustness. Our findings provide critical insights into which techniques enhance or weaken the resilience of distilled datasets against adversarial threats, offering valuable guidelines for developing more robust distillation methods in the future. Through DD-RobustBench, we aim not only to benchmark but also to push the boundaries of dataset distillation research by highlighting areas for improvement and suggesting pathways for future innovations in creating datasets that are not only compact and efficient but also secure and resilient to adversarial challenges. The implementation details and essential instructions are available on DD-RobustBench.