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
COM/MON-based high-integrity switches are essential for fault-tolerant time synchronization in TTE and TSN. Although the principles of COM/MON are simple, the implementation details and challenges of realizing high-integrity switches have rarely been thoroughly discussed. Our FPGA-based experiments identify false alarms caused by inconsistent frame-output sequences from COM and MON as a key issue. Eliminating such false alarms requires strict consistency in the frame-input sequences of COM and MON, which is difficult to enforce due to the ultra-low-latency and low-intrusiveness requirements in time-sensitive switching contexts. We propose LingXi, a COM/MON-based high-integrity switch architecture. Its integrated consensus module enforces strict input-sequence synchronization between COM and MON without intrusive modifications to the switching pipeline. We theoretically prove that LingXi introduces only 1–2 additional clock cycles of delay. Experimental results show that LingXi completely eliminates false alarms while consuming only 2.35
In this paper, an enhanced version of interpretable optimization-inspired deep network for image compressive sensing(ISTA-Net) based on hybrid precision training, dubbed Mix-ISTA, ensures high compressed reconstruction and speed up training. ISTA-Net is a deep neural network architecture whose design is inspired by the Iterative Shrinkage Threshold Algorithm (ISTA), which has the advantages of both optimization and network. However, the training process for ISTA-Net can be quite time-consuming when trained on large-scale datasets. In order to overcome this problem, we introduce a hybrid precision training technology, which can effectively consume memory and compute costs by combining single-precision (FP32) and half-precision (FP16) operations. The experimental results show that the training speed is significantly improved after mixed precision training, while the reconstruction performance of ISTA-Net is not affected. While greatly speeding up the training, it can still achieve high reconstruction results, so that it has more practical value in practical applications.
Recent advancements have demonstrated the potential of reinforcement learning (RL) in enhancing cache management policies. However, existing approaches still face two critical limitations: (1) most methods treat prefetching and cache replacement as separate tasks, which restricts their adaptability to diverse and dynamic workloads; and (2) the high computational and storage overheads of typical RL techniques hinder their deployment in real-world hardware systems. To overcome these challenges, we propose LightCacheRL, a lightweight and practical RL-based cache management framework that unifies prefetching and replacement decisions within a unified formulation. Specifically, LightCacheRL models the cache management problem as a lightweight online Multi-Armed Bandit (MAB) process, enabling efficient and adaptive policy learning with minimal runtime overhead. The reward function in LightCacheRL integrates both instruction-per-cycle (IPC) and system-level bandwidth feedback, providing a hardware-aware optimization objective. We conduct comprehensive evaluations through simulation and hardware synthesis across single-core and multi-core configurations. Experimental results show that LightCacheRL achieves 11.5
Remote Direct Memory Access (RDMA) is widely deployed in Data Center Networks (DCNs), but it is sensitive to packet loss due to the Go-Back-N retransmission. Forward Error Correction (FEC) can mitigate this issue by introducing redundancy to recover lost packets. However, the fixed coding parameters of FEC hinder its adaptability to dynamic network conditions, leading to inefficiencies and unreliability. To address this issue, we propose DCTS-RDMA, a dynamic coding transmission system for RDMA. DCTS-RDMA incorporates a monitor to detect packet loss in real time and a Dynamic Coding Packet (DCP) algorithm to adaptively adjust the coding group size and redundancy based on the detected loss, thereby overcoming the inefficiency of fixed coding parameters. To support varying coding decisions, the selected parameters are transmitted to an encoder at the sender to generate redundant packets, while a decoder at the receiver reconstructs lost data using both the original and redundant packets. We implement DCTS-RDMA using C++/HLS on an FPGA-based simulation platform and conduct extensive experiments. Under low packet loss conditions (less than 1
Pedestrian motion prediction is of critical significance for intelligent and safe autonomous driving systems design. Human movement is by nature highly non-deterministic and multi-modal. Particularly, humans’ travel goals and their behavioral decisions interact with each other. In this work, we present Goal Interaction Conditioned Network GICNet for flexible and accurate human trajectory forecasting. Social influence, multi-modality, and goal constraints have been incorporated into GICNet to infer socially compliant human trajectories. The approach operates in three key stages: learning a probability distribution of motion intentions from historical data, grouping pedestrians and modeling goal-goal interactions using a masked Graph Attention Network (GAT), and integrating intention with motion history for prediction. Additionally, we present a novel iterative pooling method to adaptively fuse the impact of different pedestrian attributes during trajectory generation, enhancing robustness to neighbor misidentification. We demonstrate that GICNet generates realistic multi-modal trajectories, and improves the state-of-the-art performance on the Stanford Drone trajectory prediction benchmark by ∼ 6.7 ∼ 23.0
Cloud-edge collaboration scheduling has emerged as a solution to alleviate cloud workloads by pushing tasks to edge nodes. However, the existing deep-learning-based scheduling methods are challenging to scale to large-scale environments due to high memory overhead during model training. We propose a hierarchical cloud-edge collaborative task scheduling framework, HSC, to address the scalability issue. HSC organizes cloud servers and edge nodes into areas and employs a two-level hybrid scheduling workflow. The inter-area scheduling uses lightweight load-balanced rules to distribute tasks efficiently, while the intra-area scheduling applies a deep-learning-based model for high-quality decisions. HSC breaks down large-scale scheduling problems into smaller area-level scheduling problems, significantly reducing the amount of training memory required. Experiments demonstrate that HSC reduces task response time by 2.8
In recent years, unmanned aerial vehicles (UAVs) have been utilized as mobile edge computing (MEC) platforms to tackle computing resource limitations and communication coverage issues, particularly in areas without fixed infrastructure. However, the independent operation of UAV providers often leads to imbalanced service loads, inefficient resource usage, and limited coverage. To address these issues, this paper proposes a hierarchical cooperation approach for multiple UAV service providers, optimizing coalition formation and task offloading strategies to enhance overall system utility. We model the collaboration between UAV providers as a coalition formation game (CFG) and the joint order is employed to ensure stable coalitions, thus maximizing system performance. Task offloading and resource allocation within each coalition are formulated as a many-to-one matching problem to optimize resource utilization and computational efficiency. The Shapley value is applied for fair utility distribution, incentivizing UAV providers to maintain cooperation. Extensive experiments demonstrate the effectiveness of our approach, with the joint order improving system utility by 4.76
In the era of big data, the efficiency of the Hyperlink-Induced Topic Search (HITS) algorithm in processing large-scale web link data has become a critical issue. To address the inefficiency of the traditional serial HITS algorithm when dealing with massive web link graphs, this paper proposes a novel parallel HITS algorithm. The algorithm lies in the rational partitioning of the web link graph. By adopting a graph-based partitioning method, the large-scale web link graph is divided into multiple subgraphs. Each subgraph is assigned to a computing node, enabling each node to independently calculate the authority and hub values of the web pages within its assigned subgraph. In addition, to reduce communication overhead in the parallel computing process, we further design data compression and asynchronous communication strategies. The former is applied to web link data before transmission to effectively reduce the amount of data transferred, while the latter enables processing units to perform other tasks while waiting for data transmission, thereby improving resource utilization. Experimental results demonstrate that the proposed parallel HITS algorithm not only maintains the accuracy of the original HITS algorithm but also achieves a significant improvement in computing efficiency.
Telecom operators historically built extensive central offices for telephony, which now provide a unique physical substrate for edge computing. The emergence of model training, immersive media, and real-time analytics is transforming these facilities from voice switching hubs into distributed compute sites, demanding deterministic resource scheduling that simultaneously satisfies compute capacity and network SLAs. To this end, we propose CRANE, a coordinated scheduling framework that enables this evolution. CRANE integrates real-time compute and network awareness, multi-objective decision-making for selecting optimal compute nodes, and SRv6 traffic engineering for network SLA enforcement. Experiments demonstrate that CRANE achieves high SLA compliance, lowers end-to-end latency, and improves overall resource utilization, enabling the transformation of legacy telecom infrastructure into a distributed platform that supports deterministic workloads.
In the Industrial Internet of Things (IIoT), Time-Sensitive Networking (TSN) is a promising field network of implementing application functions across distributed devices. For a TSN-engaged IIoT system, co-scheduling task execution and TSN transmission is crucial to guarantee the chain execution of application tasks. However, the generalization ability of co-scheduling across varying scenarios is hindered in existing works, which lack characterization for resource conflicts arising from semantic relations among tasks, traffic, and the underlying topology. To address this, we propose a heterogeneous graph neural network (HGNN)-based co-scheduling method featuring explicit conflict characterization. We design a semantic-aware encoder within the HGNN, which aggregates heterogeneous component features through designated graph paths to capture their semantic relations. An agent then extracts conflict patterns from this encoding, and decodes conflict-free scheduling decisions on offloading, task priority assignment, and traffic offset design. To enhance generalization ability in unseen scenarios, the conflict extraction ability and the inductive encoding ability are refined through deep reinforcement learning feedback. Experiments demonstrate that our method achieves 12
Cellular vehicle-to-everything (V2X) networks leveraging device -to-device (D2D) communications face critical interference and reliability bottlenecks in safety-critical scenarios. This work introduces a dynamic resource orchestration framework that jointly optimizes transmission mode selection, spectrum sharing, and power allocation. By decomposing the mixed-integer non-convex optimization problem through block coordinate descent, our approach iteratively solves coupled subproblems. Non-convex constraints are transformed via successive convex approximation with first-order Taylor expansions, enabling efficient solution convergence. The proposed scheme maximizes vehicle-to-infrastructure (V2I) sum-rate while rigorously guaranteeing ultra-reliable low-latency requirements for vehicle-to-vehicle (V2V) links through adaptive mode switching between dedicated and reused spectrum access. Simulations confirm significant performance gains over conventional methods across diverse urban scenarios.
The exponential growth of deep neural network (DNN) model size and data volume makes distributed training indispensable in cloud environments. Today’s cloud datacenters typically operate as heterogeneous clusters, where computing nodes are equipped with diverse GPU generations. In such environments, cloud providers face the critical challenge of optimally matching user-submitted training jobs with suitable GPU resources to accelerate the job training process. Existing works either improve computational efficiency by leveraging affinity between heterogeneous GPUs and jobs, or arrange jobs on the same type of GPUs as much as possible to reduce cross-rack communication overhead. However, none of these methods simultaneously considers both computation and communication factors, despite their combined importance in determining job completion time (JCT) for distributed training. In this paper, we propose HetSpeed, a Heterogeneous-aware resource allocation method to jointly cluster efficient computational resources and communication overheads to Speedup the model training process. HetSpeed formulates a binary integer programming problem that incorporates real-world scenario constraints and proves its NP-hardness. To solve this problem, HetSpeed presents an effective submodular-based greedy algorithm with a tight approximation ratio (1-1/e) . We evaluate HetSpeed with real-world job traces, and HetSpeed is able to minimize the cluster’s total cross-rack traffic, decreasing the average JCT by 27.8
Machine translation (MT) systems have achieved remarkable progress in recent years, especially with the emergence of deep learning and Transformer-based architectures. Nonetheless, the application of a general neural machine translation approach to domain-specific scenarios remains limited. Firstly, the presence of domain-specific terminology may reduce the accuracy of general translation models, which typically lack fine-tuning for the target domain. Secondly, fine-tuning such models necessitates domain-specific training data, which is frequently limited in availability. Finally, general translation tools like Google Translate do not provide a unified pipeline for seamless integration into industry workflows. This paper introduces a unified translation workflow developed to address these limitations, offering adaptability and extensibility for domain-specific applications in industry. Our proposed approach delivers a complete pipeline for end-users, incorporating customizable domain-specific terminology and supporting translation across multiple document formats, thereby enhancing both usability and output quality in real-world applications. We evaluate the proposed approach in an industrial setting, focusing on the textile and garment domain. Using a dataset of textile and apparel domain-specific documents in various formats, along with human evaluation, the experiments demonstrate the effectiveness and practicality of our approach for domain-adaptive machine translation.
Federated learning (FL) is a privacy-preserving distributed learning paradigm in which clients perform localized training iterations and send model updates to a central server rather than sharing raw data. However, data heterogeneity among clients significantly impacts the performance of the global model. Existing FL methods tackle this issue by optimizing the loss function through client-side training, but fail to account for the distinct distribution differences of client data and rely on uniform client weighting during server-side aggregation. This limitation restricts the model’s effectiveness and weakens its generalization ability. To address this, we propose a novel FL optimization method, Clustering FL with Bhattacharyya Distance (CFLBD). This approach integrates the local similarity assessment based on client data heterogeneity and adaptive clustering to dynamically weigh the aggregation of local models in each communication round. Specifically, CFLBD quantifies the distribution similarity by calculating the Bhattacharyya distance between the local and global models. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is leveraged to adaptively cluster the local models on the server, minimizing the distribution variance within each cluster. The global aggregation relies on a dynamically weighted average of the clustered clients, enhancing its performance and robustness across heterogeneous data scenarios. Experimental results show that CFLBD consistently outperforms baseline methods in accuracy and robustness across various heterogeneity settings.
Face to DDoS attacks, an effective defense scheme based on SDN for DDoS attacks is proposed in the paper. The proposed scheme includes an attack module, a monitoring module, and a defense module. In the attack module, DDoS attack scenarios are simulated in Mininet by producing the attacking traffic. Then, in the monitoring module, sFlow technology is used to monitor and extract common attack traffic characteristics. Besides, in the defense module, DDoS attack defense is implemented with API in the ONOS controller. Furthermore, when the traffic exceeds the threshold, the speed is limited to prevent the network from being paralyzed, and the traffic type is categorized into attack, ordinary, or business traffic. Then, after DDoS defense is performed, the speed limit is restored, and the QoS rules are used to ensure the forwarding of business traffic. The simulation results demonstrated the excellent performance of the proposed SDN-based solution for defending against DDoS attacks.
