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Proceedings of the 19th iasted international conference on parallel and distributed computing and Systems

Proceedings of the 19th IASTED International Conference on P...

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19th iasted international conference on parallel and distributed computing and Systems

The proceedings contain 84 papers. The topics discussed include: impact of NUMA effects on high-speed networking with multi-opteron machines;an ensemble method using hybrid real-coded genetic algorithm with pruning (HRGA/P R);P2P video broadcast based on per-peer transcoding and its evaluation on planetlab;dynamic modification of wireless multihop transmission route in message-by-message manner for shorter transmission delay;an efficient partitioning and scheduling algorithm for streaming applications on FPGA with resource constraint;associativity-based adaptive weighted clustering for large-scale mobile ad hoc networks;directional node-disjoint multipath routing in wireless ad hoc network;toward more parallel frequent itemset mining algorithms;schedulability of aperiodic tasks in hybrid process model;GRID-enabled ensemble subsurface modeling;and parallel double divide and conquer and its evaluation on a super computer.

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Optimizing Production Component Scheduling in Multivariate Industrial networks with Dynamic Changes in Production Costs 25th

Optimizing Production Component Scheduling in Multivariate I...

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25th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Xing, Xiangxiang Chen, Fulin Zuo, Tianyu Li, Pan Di, Kai Wang, Xin Chen, Lifeng Jiang, Yichuan Chen, Dan School of Cyber Science and Engineering Southeast University Jiangsu Nanjing211189 China School of Computer Science and Engineering Southeast University Jiangsu Nanjing211189 China No.8511 Research Institute of CASIC Jiangsu Nanjing210007 China Sanjiang Aerospace Xianfeng Electronic Information Co. Ltd. Wuhan Xiaogan China College of Electronics and Information Engineering Beibu Gulf University Guangxi Qinzhou535000 China

ISBN: (纸本)9789819642069

With the development of industrial intelligence and information technology, the industrial network presents a new characteristic of multivariate network. In this paper, a production element scheduling optimisation algorithm for dynamic change of production cost in multivariate industrial network is designed in order to reduce the scheduling element falling into local optimum. First of all, the multi -time granularity prediction method based on stability selection predicts the production cost of components in multivariate industrial networks. Subsequently, multi -dimensional training is based on multi-dimensional training to learn scheduling optimization algorithm to solve the scheduling optimization of components. Finally, the experiment shows that the algorithm has better performance for optimization of task scheduling in multivariate industrial networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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Proceedings of the iasted international conference on parallel and distributed computing and Systems, PDCS 2011

Proceedings of the IASTED International Conference on Parall...

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iasted international conference on parallel and distributed computing and Systems, PDCS 2011

ISBN: (纸本)9780889869073

The proceedings contain 64 papers. The topics discussed include: the impact of inter-node latency versus intra-node latency on HPC applications;designing a secure epidemic based update protocol for P2P systems;simulating species interactions and complex emergence in multiple flocks of boids with GPUS;a security framework for service overlays: operating in the presence of compromised nodes;resource-constrained static scheduling of synchronous data-flow graphs;CTOR generations in neural network computations;a model based approach for computing speedup on parallel machines using static code analysis;dynamic selection of speculative paths in two-path limited speculation method;implementation of multiple-precision floating-point arithmetic library for GPU computing;multi-objective local instruction scheduling for GPGPU applications;and overlaying an opportunistic virtual storage system on the unagrid infrastructure.

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parallel computing Optimization of Tourism Big Data based on Crawler Programs

Parallel Computing Optimization of Tourism Big Data based on...

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Intelligent Systems and Computational networks (ICISCN), international conference on

作者： Xiaoyu Zhou School of Management Wuhan Donghu University Wuhan China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

With the rapid development of the tourism industry, traditional tourism methods are undergoing significant transformation, and online tourism is gradually becoming a new highlight in the market. However, faced with the challenges of big data, online tourism data experiences tremendous pressure in terms of storage, computation, and management, and the issue of information overload seriously affects the user experience. Therefore, there is an urgent need for innovative platforms and recommendation algorithms to optimize the screening and recommendation of tourism information. This paper proposes an optimization scheme based on the Spark cloud computing platform to improve the efficiency of tourism data processing. First, utilizing Spark's distributed computing and memory management capabilities, tourism data can be stored and processed in a distributed manner, thus accelerating the computation speed of recommendation algorithms. Secondly, a Python-written crawler program is used to obtain real tourism data, including user ratings and comments on attractions, to evaluate the effectiveness of the improved algorithm. Finally, a weighted algorithm combining the LDA topic model is proposed. This algorithm analyzes the topic distribution of comment texts and weights the comments based on user ratings, calculating the predicted score for unrated attractions for the target user, thereby achieving accurate attraction recommendations. The experimental results show that the algorithm proposed in this study demonstrates significant advantages in prediction accuracy.

关键词： Accuracy Tourism industry Crawlers Standardization Big Data Prediction algorithms Data processing User experience Sparks Optimization

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Anomaly Detection in Microservices Architecture Using Graph Neural networks

Anomaly Detection in Microservices Architecture Using Graph ...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Matthias Osswald Timothy Schönenberger Gokcan Cantali Wissem Soussi Gürkan Gür Zurich University of Applied Sciences (ZHAW) InIT Winterthur Switzerland Communication Systems Group CSG University of Zürich UZH Zürich Switzerland

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The rise of cloud computing has transformed application development and deployment, with Kubernetes emerging as a key platform for managing containerized applications. This paper explores the use of Graph Neural networks (GNNs) to detect anomalies in Kubernetes clusters and proposes GNN-based Anomaly Detection in Kubernetes (GATAKU), which is instrumental in maintaining security and performance. Our work involves integrating Cilium for detailed network monitoring and data collection, setting up a Kubernetes cluster with k3s and Traefik, and simulating attack scenarios to generate realistic data. Data preprocessing and feature engineering prepare this data for the GNN training. We present the GATAKU model’s performance, highlighting metrics such as accuracy, precision, recall, and F 1 -score and compare it to baseline ML models, namely Support Vector Machine (SVM) and Random Forest (RF). Moreover, we discuss these findings and emerging challenges, including handling high-dimensional data, and explore practical implications for cybersecurity.

关键词： Support vector machines Training Cloud computing Analytical models Accuracy Computer architecture Graph neural networks Computer security Anomaly detection Tuning

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Deep Time Series Anomaly Detection with Local Temporal Pattern Learning

Deep Time Series Anomaly Detection with Local Temporal Patte...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yizhou Wang, Yijie Xu, Hongzuo Zhou, Xiaohui National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Beijing100091 China

ISBN: (纸本)9798350368741

Self-supervised time series anomaly detection (TSAD) demonstrates remarkable performance improvement by extracting high-level data semantics through proxy tasks. Nonetheless, most existing self-supervised TSAD techniques rely on manual- or neural-based transformations when designing proxy tasks, overlooking the intrinsic temporal patterns of time series. This paper proposes a local temporal pattern learning-based time series anomaly detection (LTPAD). LTPAD first generates sub-sequences. Pairwise sub-sequences naturally manifest proximity relationships along the time axis, and such correlations can be used to construct supervision and train neural networks to facilitate the learning of temporal patterns. Time intervals between two sub-sequences serve as labels for sub-sequence pairs. By classifying these labeled data pairs, our model captures the local temporal patterns of time series, thereby modeling the temporal pattern-aware "normality". Abnormal scores of testing data are acquired by evaluating their conformity to these learned patterns shared in training data. Extensive experiments show that LTPAD significantly outperforms state-of-the-art competitors. © 2025 IEEE.

关键词： Local Temporal Pattern Self-supervised Learning Time Series Anomaly Detection

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A Tiered Native Digital Twin for Telco networks 3rd

A Tiered Native Digital Twin for Telco Networks

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Wang, Shoufeng Chen, Huamin Guo, Jianchao Ouyang, Ye Li, Fan Chen, Xuan Bian, Sen Zhang, Zhongke Zhang, Lianhua Li, Yun INC Beijing China Beijing University of Technology Beijing China China Unicom Beijing Branch Beijing China China Unicom Beijing China

ISBN: (纸本)9789819616930

As the importance of Digital Twin Network (DTN) has become a common sense in the study of 5G, 6G, and future network, the means to implement DTN and the performance challenges in DTN start to be discussed. However, the reproduction compression of DTN, i.e. the design and compression requirements of n:1 reproduction, is a technical gap. Additionally, there is no corresponding technical solution to the time delay of network intelligence calling DTN system. A Tiered Native Digital Twin for Telco (TNDT4Telco) is proposed for future communication systems in this paper, based on the multi-level distributed design philosophy. TNDT4Telco resolves two key issues in DTN design, i.e., n:1 reproduction against huge quantity of raw data, and DTN wake-up time. Numerical evaluation shows that TNDT4Telco can provide 36% total DTN wake-up time saving, and around 10 times gain with different number of parallel DTN tasks in DTN task aggregation performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G mobile communication systems

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LBoDSN: An In-Network Load Balancing Mechanism for Lossless Data Center networks Based on Direct Switch Notification 20th

LBoDSN: An In-Network Load Balancing Mechanism for Lossless...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Shi, Qingyu Jiang, Fangxue Huang, Huang Li, Xiaocui Li, Chuang Cao, Wenzhi Liu, Limei School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha China Xiangjiang Laboratory Changsha China

ISBN: (纸本)9789819628292

With the extensive deployment of Remote Direct Memory Access (RDMA) in lossless data center networks (DCNs), enhancing the RDMA load balancing performance for distributed AI training and HPC applications becomes particularly critical. However, existing load balancing schemes either suffer difficulty in responding to congestion in sub-RTT time, or cannot accurately detect and reroute flows that are on the verge of creating path congestion for Priority-based Flow Control (PFC) enabled lossless DCNs. In this paper, we propose LBoDSN, an in-network load balancing mechanism for lossless DCNs based on direct swtich notification, to address above challenges. LBoDSN monitors ingress queue length evolution at destination switches to predict the triggering time of PFC pause, and accurately identifies congested flows based on the congestion contribution level before PFC pause, then further proactively sends the flow congestion notification (FCN) to source switches for fast rerouting. The FCN contains a flow ID that identifies the congested flow, and a path ID that identifies the target path to which the congested flow will be rerouted. And after rerouting, the Congestion Notification Packet (CNP) of the old path is selectively discarded at source switches to improve transmission performance, while out-of-order packets are reordered at the destination switch. The experimental results show that under realistic workloads, LBoDSN achieves 9–64% and 20–80% better than CONGA for the average and tail Flow Completion Times (FCTs), respectively. Compared with ConWeave, LBoDSN achieves around 8% better performance for the average and tail FCTs while significantly reducing switch queue usage for reordering. © IFIP international Federation for Information Processing 2025.

关键词： Time switches

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On Scaling Neuronal Network Simulations Using distributed computing 24

On Scaling Neuronal Network Simulations Using Distributed Co...

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Proceedings of the SC '24 Workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Vladimir Omelyusik Khawar Shehzad Tyler Banks Praveen Rao Satish S. Nair University of Missouri Univeristy of Missouri

ISBN: (纸本)9798350355543

We investigated the computational capabilities of FABRIC, a nationwide research infrastructure with nearly 40 sites, for scaling neuroscience simulations. From the hardware standpoint, single-site characterization showed that FABRIC is a promising alternative to conventional neuroscience setups, particularly due to the availability of powerful graphics processing units (GPUs). While multi-site simulations are affected by network latency, it becomes less critical for larger networks. From the software perspective, we found that in the popular CoreNEURON library, cell distribution strategy (for parallel execution) does not affect the simulation time for biologically realistic networks, while other cases can be addressed with a minimum k-cut graph partitioning algorithm. Overall, scalability experiments revealed that FABRIC can be used to simulate networks of up to twenty-five thousand cells, with the limiting issue being GPU memory.

关键词： FABRIC

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The Application of Digital Empowerment Combined with Intelligent Algorithms of Computer Technology in Decision Support Systems

The Application of Digital Empowerment Combined with Intelli...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Wan Lu Li Chen Fei Gao Chang Wang Wuhan Windoor Information &Technology Co. Ltd Wuhan Hubei China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Traditional decision support systems (DSS) show obvious limitations in dealing with increasingly complex and dynamic decision-making scenarios. By integrating graph neural networks (GNNs) and expert systems (ESs), this paper constructs a new IDSS framework aimed at improving decision-making efficiency and accuracy. In terms of research methods, large-scale distributed computing is performed in the cloud based on parallel computing technology; the logical reasoning of expert systems and the self-learning ability of graph neural networks are combined to achieve dynamic updates and efficient processing of data by unstructured knowledge bases. Compared with traditional DSS, the accuracy of IDSS based on the fusion of GNNs and ES in financial forecasting and risk assessment scenarios is improved to more than 90%, and resource utilization is significantly optimized. This study shows that the integrated application of intelligent algorithms has broad prospects in improving the dynamic adaptability of decision support systems and coping with complex decision-making environments.

关键词： Decision support systems Accuracy Heuristic algorithms Systems architecture Graph neural networks Real-time systems Risk management Resource management Expert systems Optimization

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