Zhijie Jiang

Persisting tail latency in storage systems poses critical challenges to cloud computing and real-time applications. While existing software solutions predominantly target transient latency spikes through single-operation prediction, their effectiveness diminishes significantly when confronting persisting performance degradation in modern SSDs. We present LatVision , an operating system (OS) kernel-level monitoring framework that integrates dual-layer prediction and attention mechanisms for cross-storage latency analysis. Leveraging eBPF to capture I/O performance metrics at the OS kernel level, LatVision implements a dual-layer detection pipeline: a lightweight classifier initially filters normal operations, while suspicious data failing the Layer-1 screening are routed to Layer-2 prediction model for double check. This hierarchical architecture reduces average computational overhead by 75.3% compared with monolithic prediction models. An attention-enhanced neural network further improves persisting tail latency prediction precision by prioritizing critical temporal patterns. Experimental evaluations across six SSD and HDD configurations demonstrate LatVision’s generalization capability, achieving prediction accuracy exceeding 90% across all test cases. Heuristic algorithm integration with the prediction results enhances operational stability under diverse workload conditions. The framework achieves real-time, lightweight, and high-precision persisting tail latency prediction for low-latency SSDs while maintaining minor latency overhead.

For near-field synthetic aperture radar (SAR) imaging, frequency domain imaging algorithms typically require the array elements to be uniformly arranged to facilitate the application of fast Fourier transform (FFT) operations; this greatly restricts the array types that can be used for practical applications. For arrays with non-uniformly positioned antennas, no general and efficient imaging algorithm is available yet. To address this issue, an effective algorithm for efficient three-dimensional (3D) SAR imaging with arbitrary linear multi-input-multi-output (MIMO) array topologies is proposed. Specifically, non-uniform FFT (NUFFT) technology is utilized to tackle the non-uniform sampling issues in both the spatial and the wavenumber domain. Despite the intuitive idea, to achieve satisfied imaging, some issues are non-trivial and should be carefully addressed. Both simulations and experiments show the superiority of the proposed algorithm on computational complexity while maintain the same high imaging precision compared with the back-projection algorithm (BPA). Moreover, we suggest that the concept of NUFFT-based imaging can also be generalized to other imaging regimes with arbitrary array configurations.