Industrial Control Systems (ICS) often lack device-level authentication, making them vulnerable to unauthorized access to Programmable Logic Controllers (PLCs). To address this challenge, we propose a lightweight hybrid fingerprinting method, Timing-based Device Fingerprinting (TIDF), for detecting unauthorized PLCs based on communication processing time and clock pulse period. TIDF leverages stable ICS network conditions and inherent PLC hardware characteristics, and integrates these features into a unified system consisting of filtering, training, and anomaly detection modules. By employing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and One-Class Support Vector Machine (OCSVM), TIDF achieves accurate and efficient classification with low overhead. We evaluate TIDF on real-world data from 13 PLCs, including Siemens and Xinje, and further test its robustness against basic forgery attempts. The results show an anomaly detection rate of 96
For some distributed applications, e.g. the distributed actor model, distributed multi-producer, single-consumer (MPSC) queues play a vital role. For these applications to be fault tolerant and performant, a highly efficient non-blocking distributed MPSC queue is desired. Currently, in the literature, there is no non-blocking distributed MPSC queue. Therefore, a question naturally arises: Does there exist a performant non-blocking distributed MPSC queue? We answer this question by proposing Slotqueue, a wait-free distributed MPSC queue with only a constant amount of remote operations per enqueue and dequeue call. This is achieved by the use of timestamps to order the items in the queue and the idea of using a flat array structure to maintain the timestamps. To demonstrate how well Slotqueue performs in practice, we develop a microbenchmark to measure its throughput as compared to other distributed MPSC queues we have surveyed in the literature. We discover that Slotqueue is fault tolerant while performing comparable to other distributed MPSC queues.
Graph Neural Network (GNN) has emerged in graph learning tasks in recent years. As the real-world graphs become larger, sampled GNN is widely used in both academia and industry instead of training on the whole graph. As the training stage consumes less execution time due to the small size of sampled subgraphs, the data preparation stage becomes the performance bottleneck, especially the graph sampling stage. Many sampling methods have been proposed to improve the efficiency, but they still suffer from the significant overhead of the high-degree node selection collision in bias sampling. In this paper, we present HSampler , a multi-GPU GNN sampling system. It selects sampled subgraphs with reordering and sliding windows to reduce the repeated trials in biased sampling, thus improving the performance of the graph sampling stage. Evaluation on a node with 4 GPUs shows that HSampler significantly outperforms state-of-the-art systems like DGL and P^3 by 2.1 × to 6.2 × .
With the increasing deployment of complex deep learning models on edge devices, addressing the high inference latency caused by their inherent computational bottlenecks is paramount for enabling real-telligent applications. To mitigate the resulting high inference latency, we propose EdgeInferFlow, a novel framework designed to accelerate the distributed inference of chain-structured models across edge-device clusters. The framework is predicated on a two-level optimization methodology: model partitioning and distributed inference orchestration. At the partitioning level, EdgeInferFlow employs a dynamic load-balancing algorithm to mitigate computational load imbalance among partitioned sub-models. This algorithm is integrated with a distributed computation scheme for linear layers and a multi-stage strategy to minimize inter-node data dependencies and communication overhead. At the inference level, EdgeInferFlow constructs a distributed computational graph to orchestrate the data flow and parallel execution of tensors throughout the cluster. Experimental results demonstrate that our proposed method achieves a reduction in end-to-end inference latency of up to 70
Nowadays, big data query systems are often deployed on cloud native platforms for advantages like automated deployment and elastic scalability. Nonetheless, traditional optimization approaches often fail to fully exploit the potential of cloud native platforms and cache acceleration. It results in cache related QoS drawbacks for running query on cloud native environment, including inadequate space for caching, static cache configurations that are unsuitable for dynamic workloads, and query task scheduling that lacks consideration for cache reuse. To address these issues, we propose Fluid-DataTable, an elastic and efficient table cache management and query task scheduling service designed specifically for query systems on cloud native platforms. It prioritizes loading tables with high acceleration gain into cache cluster, dynamically adjusts the number of cache replicas and cache nodes based on data access frequency, and plans the execution order of queries with consideration of the cache state. Experimental results show that Fluid-DataTable achieves significant improvement in cache efficiency and query performance due to the proposed cloud native cache management and adaptation mechanisms. Also, the cache-aware scheduling strategy reduces cache replacement frequency, bringing about around 30
