Large organizations emit terabytes of logs every day in their cloud environment. Efficient data science on these logs via text search is crucial for gleaning operational insights and debugging production outages. Current log management systems either perform full-text indexing on a cluster of dedicated servers to provide efficient search at the expense of high storage cost, or store unindexed compressed logs on object storage at the expense of high search cost. We propose LogCloud, a new object-storage based log management system that supports both cheap compressed log storage and efficient search. LogCloud constructs inverted indices on compressed logs using a novel FM-index implementation that supports efficient querying from object storage directly, removing the need for dedicated indexing servers. Experiments on five public and five production log datasets show that LogCloud can achieve both cheap storage and search, scaling to TB-scale datasets.

Unmanned aerial vehicles (UAVs) with thermal imaging cameras are widely used for target tracking, reconnaissance, and search operations. However, rapid thermal camera rotations during field-of-view adjustments introduce significant motion blur, impairing real-time image detection and tracking. While deep learning has been a dominant approach for image deblurring, its application to infrared image motion deblurring (IRMD) remains limited owing to the lack of publicly available datasets and challenges in handling large motion blur or maintaining real-time performance. This study addresses these gaps by constructing a large-scale UAV infrared motion deblurring (U2IRD) benchmark dataset, incorporating gyroscopic steering rate information. Additionally, we propose a gyro-aided spatial frequency network (GSFNet) that uses spatial and frequency domain features for UAV IRMD. The input data converts the gimbal steering rate information into a pixel distribution intensity map as a priori information. Specifically, the designed spatial depth residual attention module captures critical spatial domain details, while the multiple frequency domain feature recovery module extracts frequency domain features for effective deblurring. Extensive evaluations on U2IRD and synthetic thermal blurred image datasets demonstrate that the proposed method achieves state-of-the-art deblurring performance. The new IRMD dataset, available at https://github.com/aurora-sea/U2IRD, is anticipated to facilitate advancements in UAV IRMD research and applications.

The Kalman filter is widely used in GNSS/INS integrated navigation systems to fuse information, resulting in high precision and robust positioning performance. In the Kalman filter, the accuracy of the process noise covariance matrix directly affects the precision of the positioning results. We propose a Soft Actor-Critic (SAC) algorithm based on Gated Recurrent Unit neural networks (GRU-SAC) to dynamically adjust the process noise covariance matrix online using sequential observation data to improve positioning accuracy. We model the decision-making process as a Partially Observable Markov Decision Process (POMDP) and incorporate multiple information sources as the system state. The GRU network is used to extract temporal features from the navigation data and to address memory consumption issues commonly associated with POMDPs. And the SAC algorithm continuously adjusts the process noise covariance based on observations from the Kalman filter, allowing the algorithm to perform better in complex, dynamic, and changing navigation environments. Additionally, we provide detailed design and deployment strategies for both loosely-coupled and tightly-coupled systems. Extensive experiments have been conducted to validate the effectiveness of our method. The results show that our approach generalizes well across a wide range of preset process noise covariance matrices and performs excellently in suppressing error drift during GNSS outages.

Silent Data Corruption (SDC) in processors can lead to various application-level issues, such as incorrect calculations and even data loss. Since traditional techniques are not effective in detecting these errors, it is very hard to address problems caused by SDCs in processors. For the same reason, knowledge about these SDCs in the wild is limited. In this article, we conduct an extensive study on CPU SDCs in a large production CPU population, encompassing over one million processors. In addition to collecting overall statistics, we perform a detailed study to understand (1) whether certain processor features are particularly vulnerable and their potential impacts on applications; (2) the reproducibility of CPU SDCs and the triggering conditions (e.g., temperature) of those less reproducible SDCs; and (3) the challenges to mitigate and handle CPU SDCs. We further investigate the implications that our observations obtained from the above researches have on the SDC fault models, SDC mitigation strategies, and the future research fields. In addition, we design an efficient SDC mitigation approach called Farron, which uses prioritized testing to detect highly reproducible SDCs and temperature control to mitigate less-reproducible SDCs. Our experimental results indicate that Farron can achieve better coverage of CPU SDCs with lower overall overhead, compared to the baseline used in Alibaba Cloud. This demonstrates that our observations are able to assist in SDC mitigation.

The detection of small infrared targets with a low signal-to-noise ratio and low contrast in high-noise backgrounds is challenging due to the lack of spatial features of the targets and the scarcity of real-world datasets. Most existing methods are based on single-frame images, which are prone to numerous false alarms and missed detections. This paper proposes ST-Trans that provides an efficient end-to-end solution for the detection of small infrared targets in the complex context of sequential images. First, the detection of small infrared targets in complex backgrounds relying only on a single image has been significantly difficult due to the lack of available spatial features. The temporal and motion information of the sequence image was found to effectively improve target detection performance. Therefore, we used the C2FDark backbone to learn the spatial features associated with small targets, and the spatial-temporal transformer module to learn the spatiotemporal dependencies between successive frames of small infrared targets. This improved the detection performance in challenging scenes. Second, due to the lack of publicly available infrared small target sequence datasets for training, we annotated a set of small infrared targets for challenging scenes and published them as the sequential infrared small target detection (SIRSTD) dataset. Finally, we performed extensive ablation experiments on the SIRSTD dataset and compared its performance with that of state-of-the-art methods to demonstrate the superiority of the proposed method. The results revealed that ST-Trans outperformed other models and can effectively improve the detection performance for small infrared targets. The SIRSTD dataset is available at https://github.com/aurora-sea/SIRSTD.

Cloud logs can be categorized into on-line, off-line, and near-line logs based on the access frequency. Among them, near-line logs are mainly used for debugging, which means they prefer a low query latency for better user experience. Besides, the storage system for near-line logs prefers a low overall cost including the storage cost to store compressed logs, and the computation cost to compress logs and execute queries. These requirements pose challenges to achieving fast and cheap cloud log storage. This article proposes LogGrep, the first log compression and query tool that exploits both static and runtime patterns to properly structurize and organize log data in fine-grained units. The key idea of LogGrep is “vertical partitioning”: it stores each log entry into multiple partitions by first parsing logs into variable vectors according to static patterns and then extracting runtime pattern(s) automatically within each variable vector. Based on such runtime patterns, LogGrep further decomposes the variable vectors into fine-grained units called “Capsules” and stamps each Capsule with a summary of its values. During the query process, LogGrep can avoid decompressing and scanning Capsules that cannot match the keywords, with the help of the extracted runtime patterns and the Capsule stamps. We further show that the interactive debugging can well utilize the advantages of the vertical-partitioning-based method and mitigate its weaknesses as well. To this end, LogGrep integrates incremental locating and partial reconstruction to mitigate the read amplification incurred by vertical-partitioning-based method. We evaluate LogGrep on 37 cloud logs from the production environment of Alibaba Cloud and the public datasets. The results show that LogGrep can reduce the query latency and the overall cost by an order of magnitude compared with state-of-the-art works. Such results have confirmed that it is worthwhile applying a more sophisticated vertical-partitioning-based method to accelerate queries on compressed cloud logs.

In cloud systems, near-line logs are mainly used for debugging, which means they prefer a low query latency for a better user experience, and like any other logs, they also prefer a low overall cost including storage cost to store compressed logs and computation cost to compress logs and execute queries.

Silent Data Corruption (SDC) in processors can lead to various application-level issues, such as incorrect calculations and even data loss. Since traditional techniques are not effective in detecting processor SDCs, it is very hard to address problems caused by SDCs. For the same reason, knowledge about SDCs in the wild is limited.

Compared with traditional solid-state drives (SSDs), open-channel SSDs (OCSSDs) expose their internal physical layout and provide a host-based flash translation layer (FTL) that allows host-side software to control the internal operations such as garbage collection (GC) and input/output (I/O) scheduling. In this paper, we comprehensively survey research works built on OCSSDs in recent years. We show how they leverage the features of OCSSDs to achieve high throughput, low latency, long lifetime, strong performance isolation, and high resource utilization. We categorize these efforts into five groups based on their optimization methods: adaptive interface customizing, rich FTL co-designing, internal parallelism exploiting, rational I/O scheduling, and efficient GC processing. We discuss the strengths and weaknesses of these efforts and find that almost all these efforts face a dilemma between performance effectiveness and management complexity. We hope that this survey can provide fundamental knowledge to researchers who want to enter this field and further inspire new ideas for the development of OCSSDs.

The detection of small infrared targets with a low signal-to-noise ratios and contrasts in noisy and cluttered backgrounds is challenging and therefore a domain of active research. Traditional methods result in a large number of false alarms and missed detections. In the case of convolutional neural network-based methods, it may not be possible to identify deep small targets, or the details of the target’s edge contours may not be appropriately considered. Therefore, this paper proposes MSAFFNet to perform infrared small target detection based on an encoder-decoder framework. In the encoder stage, small target features are extracted using a resnet-20 backbone network, and the global contextual features of small targets are extracted using an atrous spatial pyramid pooling module. In the decoding stage, a dual-attention module is used to selectively enhance the spatial details of the target at the shallow level and representative features of the semantic information at the deep level. Multi-scale feature maps are then concatenated to achieve superior feature fusion. Additionally, multi-scale labels are constructed to focus on the details of the target contour and internal features based on edge information and an internal feature aggregation module. Experiments conducted on the NUAA-SIRST, NUDT-SIRST and XDU-SIRST datasets revealed that the proposed approach outperforms the representative methods and achieves an improved detection performance.

Infrared (IR) ship tracking is becoming increasingly important in various applications. However, it remains a challenging task as the information that can be obtained from infrared images is limited. Aiming at enhancing IR ship tracking accuracy, we propose an innovative approach by presenting feature integration module (FIM) and backup matching module (BMM). FIM takes appearance feature, complete intersection over union (CIoU), and motion direction metrics into account. Regarding appearance feature extraction, an end-to-end characteristic learning strategy with a cross-guided multi-granularity fusion network is proposed to obtain more integral appearance features and enhance re-identification accuracy, which helps to distinguish individual IR ship targets better. Besides, a backup matching strategy is then used to match the unmatched tracks and detections after cascaded matching. Virtual trajectories are generated for the matched tracks to optimize parameters by parameter optimization module (POM). The accumulation of errors caused by the lack of observations in the Kalman filter is reduced. Thus, the position of IR ships can be estimated more accurately, and more robust IR ship tracking can be achieved. In addition, we present a sequential frame IR ship tracking dataset, providing the first public benchmark for testing IR ship tracking performance. Experimental results indicate that the MOTA, MOTP and IDs of the proposed method are 73.441, 80.826, and 32, respectively, outperforming other state-of-the-art methods. This demonstrates the superior robustness of the proposed method, particularly when the IR ships are occluded or the target texture information is lacking. Our dataset is available at https://github.com/echo-sky/SFIST.