威睿

Detection of anomalies in large-scale monitoring systems, especially in high-performance computing (HPC), is a significant issue because disruptions during the machine operation can cause operations to be interrupted and reduce its overall efficiency. In practice, traditional rule-based and statistical approaches are not likely to generalize, and deep learning approaches. The paper suggests a hybrid Graph Neural Network (GNN) architecture that employs graph convolutional learning to identify anomalies in the distributed systems. The dataset of Google 2019 Cluster is processed with feature engineering, label encoding, and standardization, and SMOTE is applied to deal with the class imbalance. The proposed Gradient Boosting (GB) model is worked out with a high accuracy (98.73%) and AUC-ROC (0.9990), which is better than the Random Forest and Gradient Boosting base models. The ensemble voting model has a better recall but a minimal reduction in AUC-ROC. The study shows that the graph-based deep learning is effective in capturing the relational dependencies that are not captured by the traditional models and that ensemble integration improves detection abilities of anomalies that can be resolved at a bigger scale.

Flow Matching has emerged as a leading framework for generative modeling, powering state-of-the-art systems such as FLUX and Stable Diffusion 3.5. However, the iterative nature of its ODE-based sampling process creates a fundamental efficiency bottleneck: the quality of generated samples is highly sensitive to the choice of step-sizes, and current models typically require 20 to 30 steps for good quality. In this work, we propose two lightweight, training-free algorithms, CAT-OV and CAT-OT that adapt step-sizes at inference time based on a novel connection between Flow Matching sampling and gradient flow. Our algorithms are computed efficiently by not requiring additional neural function evaluations. Specifically, CAT-OT estimates curvature over time via a finite-difference approximation of the time-derivative of the vector field, while CAT-OV approximates curvature over the state space via a gradient of the vector field. Under suitable conditions, both methods have truncation error bounds of constant order. Empirically, CAT-OV and CAT-OT outperform existing step-size heuristics in image quality metrics across four text- to-image Flow Matching models, reducing the number of generation steps required to reach comparable quality by up to 40

Enumerable filters are the gold-standard for full-featured filters: they support insertions, deletions, merging; they can support associated values, such as counts; and like traditional filters they support queries with a small probability of false positives. When designing a system that uses filters, high-performance enumerable filters are required to simplify system design and improve overall performance, compared to systems that must work around the limitations of traditional filters that are not full featured. The vector quotient filter (VQF) is the state of the art enumerable filter. For limited-functionality filters, blocked Bloom filters (BBF) and the prefix filter (PF) are state of the art and offer tradeoffs. Both support insertions and queries but not deletions or merging. BBFs are faster for insertions and queries but use more space than PFs and VQFs. For small-space filters, the state-of-the-art suggests a tradeoff: PFs are faster than VQFs but VQFs are enumerable, which makes them a favorable candidate to be integrated in applications. Given these tradeoffs, we are left with the following question: Do we need to give up features in order to achieve the highest performance? In this paper, we present the breadcrumb filter (BCF), a full-featured enumerable filter. For insertions, the BCF is up to 34% faster than VQF, 3.2 & times; faster than cuckoo filter, 19.6% faster than PF. For queries, the BCF achieves competitive performance, outperforming the VQF while matching the cuckoo filter. At the same time, it achieves higher space efficiency than VQF, prefix, cuckoo filter and BBF. We conclude that the choice of filter is now simplified: if additional features beyond insertions, such as deletions and counting, are required, or if minimizing space is crucial, the BCF offers the best performance. On the other hand, if only insertions are needed and space is not constrained, the BBF is the right choice.

Recent years have witnessed a widespread adoption of containers. While containers simplify and accelerate application development, existing container network technologies either incur significant overhead, which hurts performance for distributed applications, or lose flexibility or compatibility, which hinders the widespread deployment in production. We carefully analyze the kernel data path of an overlay network, quantifying the time consumed by each segment of the data path and identifying the extra overhead in an overlay network compared to bare metal. We observe that this extra overhead generates repetitive results among packets, which inspires us to introduce caches within an overlay network. We design and implement ONCache (Overlay Network Cache), a cache-based container overlay network, to eliminate the extra overhead while maintaining flexibility and compatibility. We implement ONCache using the extended Berkeley Packet Filter (eBPF) with only 524 lines of code, and integrate it as a plugin of Antrea. With ONCache, containers attain networking performance akin to that of bare metal. Compared to the standard overlay networks, ONCache improves throughput and request-response transaction rate by 12% and 36% for TCP (20% and 34% for UDP), respectively, while significantly reducing per-packet CPU overhead. Popular distributed applications also benefit from ONCache.

Memory disaggregation is an emerging data center architecture that improves resource utilization and scalability. Replication is key to ensure the fault tolerance of applications, but replicating shared data in disaggregated memory is hard. We propose SWARM (Swift WAit-free Replication in disaggregated Memory), the first replication scheme for in-disaggregated-memory shared objects to provide (1) single-roundtrip reads and writes in the common case, (2) strong consistency (linearizability), and (3) strong liveness (wait-freedom). SWARM makes two independent contributions. The first is Safe-Guess, a novel wait-free replication protocol with single-roundtrip operations. The second is In-n-Out, a novel technique to provide conditional atomic update and atomic retrieval of large buffers in disaggregated memory in one roundtrip. Using SWARM, we build SWARM-KV, a low-latency, strongly consistent and highly available disaggregated key-value store. We evaluate SWARM-KV and find that it has marginal latency overhead compared to an unreplicated key-value store, and that it offers much lower latency and better availability than FUSEE, a state-of-the-art replicated disaggregated key-value store.