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international conference on parallel distributed and Grid computing (PDGC)

作者： S. Saravanan B. Uma Maheswari Kanagasundaram K Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Nokia Networks Bengaluru India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The continuous adoption of Internet of Things (IoT) devices has established notable security risks. In recent times, IoT devices are often targeted by malicious actors as IoT devices are limited with processing and memory resources. The detection of malicious network flows in real-time is necessary to maintain the security of IoT networks. This paper addresses the challenge by presenting a scalable stream processing pipeline that identifies malicious and non-malicious flows from IoT devices in real-time. The proposed system utilizes NFStream to generate network flows from device packets. The generated network flows are then published to a Kafka topic and consumed by a Kafka consumer. A distributed random forest-based machine learning model from Apache Spark MLlib is trained to classify these flows. The classification results are stored alongside the flows in a MongoDB database. Real-time visualization of the count of malicious and non-malicious flows for each IoT device is provided via Streamlit. Experimental results demonstrate the system's capability to accurately classify network traffic in real time while maintaining scalability for large-scale IoT environments. The proposed pipeline offers a robust solution for real-time malicious flow detection in large-scale IoT networks.

关键词： Accuracy Pipelines distributed databases Data visualization Cluster computing Telecommunication traffic Real-time systems Internet of Things Object recognition Monitoring

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parallel Construction of Module networks 21

Parallel Construction of Module Networks

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international conference for High Performance computing, Networking, Storage and Analysis (SC21)

作者： Srivastava, Ankit Chockalingam, Sriram P. Aluru, Maneesha Aluru, Srinivas Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781450384421

Module networks (MoNets) are a parameter -sharing specialization of Bayesian networks that are used for reasoning about multidimensional entities with concerted interactions between groups of variables. Construction of MoNets is compute -intensive, with sequential methods requiring months,for learning networks with a few thousand variables. In this paper, we present the first scalable distributed-memory parallel solution for constructing MoNets by parallelizing Lemon-Tree, a widely used sequential software. We demonstrate the, scalability of our parallel method on a key application of MoNets the construction of genome-scale gene regulatory networks. Using 4096 cores, our parallel implementation constructs regulatory networks for 5,716 and 18, 373 genes of two model organisms in 24 minutes and 4.2 hours, compared to an estimated 49 and 1561 days using Lemon -Tree for generating exactly the same networks, respectively. Our method is application-agnostic and broadly applicable to the learning of high-dimensional MoNets for any of its wide array of applications.

关键词： Bayesian networks module networks score-based learning parallel machine learning gene networks

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Hierarchical Communication Optimization for distributed DNN Training

Hierarchical Communication Optimization for Distributed DNN ...

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IEEE international conference on High Performance computing Workshops (HiPCW)

作者： Om Shivam Verma Preeti Malakar Indian Institute of Technology Kanpur

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

Motivation: Large scale deep neural network models use a lot of training data, evident from the large datasets curated for their training, such as ImageNet. Thus it is necessary to use high performance computing (HPC) systems for training of these large-scale models. Various techniques have been developed for parallelizing model training - data-parallelism, model-parallelism, and the more recent FSDP (Fully Sharded Data parallel).

关键词： Training Computational modeling High performance computing conferences Training data distributed databases Artificial neural networks Data models Optimization

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PipePrune: Pipeline parallel Based on Convolutional Layer Pruning for distributed Deep Learning 23

PipePrune: Pipeline Parallel Based on Convolutional Layer Pr...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Tan, Daojun Jiang, Wenbin Qin, Shang Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology Natl. Eng. Res. Ctr. for Big Data Technol. and Syst. Serv. Comp. Technol. and Syst. Lab Cluster and Grid Comp. Lab Wuhan430074 China

ISBN: (纸本)9781665494571

Benefitting from the combination of the idea of pipeline with model parallelism and data parallelism, pipeline parallelism improves the efficiency of distributed deep learning systems significantly. However, suffering from the bubbles and gaps caused by the imbalance of different stages in pipeline, it can not output ideal performance yet. To explore the potential of pipeline parallelism further, we propose a novel approach called PipePrune, which adds a convolutional layer pruning strategy to pipeline creatively to reduce the bubbles and gaps. For the convolutional layers with heavy overheads, some unimportant kernels are pruned by the measurement of the L1-norm. This approach makes the processing overheads of different pipeline stages more balanced. The experimental results show that, compared with state-of-the-art pipeline methods, PipePrune can improve the training speeds obviously (e.g. for ResNet50 on ImageNet, about 30% speed improvement is realized with only 1.1% loss of training accuracies). © 2021 IEEE.

关键词： Training Deep learning Smart cities Pipelines Neural networks distributed databases parallel processing

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UAV Aided Post Disaster Relief networks using Blockchain Technology: Challenges and Solutions

UAV Aided Post Disaster Relief Networks using Blockchain Tec...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Balaram Pal Soumyajit Paul Ashok Kumar Turuk Munshi Mustafijur Rahaman Sanjeev Patel Dept. of Computer Science Engineering Bengal Institute of Technology Kolkata India Dept. of Computer Science Engineering GCECT Kolkata India Dept. of Computer Science Engineering National Institute of Technology Rourkela India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

This paper investigates integrating blockchain technology into UAV-aided disaster relief networks (UDRNs). It addresses challenges such as inefficient resource coordination, security vulnerabilities in smart contracts, and transaction malleability. Solutions include coordinated smart contracts for efficient resource allocation, dynamic vulnerability auditing for enhanced security, and augmented forensic systems to ensure transaction integrity. The proposed approach enhances trust, transparency, and effectiveness in disaster relief operations by leveraging UAVs and blockchain.

关键词： Disasters Scalability Smart contracts Supply chains Standards organizations Autonomous aerial vehicles Blockchains Security Resource management Vehicle dynamics

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Queuing parallel computing CAD Tasks in the Design and Optimization of IC Topography 28

Queuing Parallel Computing CAD Tasks in the Design and Optim...

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28th international conference on Mixed Design of Integrated Circuits and System (MIXDES)

作者： Wojtasik, Adam Warsaw Univ Technol Inst Microelect & Optoelect Warsaw Poland

ISBN: (纸本)9788363578190

In recent years, the development of personal computers hardware has been aimed at increasing the number of processor cores. At the same time, the efficiency and reliability of computer interconnecting networks is increased. This enables the introduction and development of parallel and distributed processing methods also in CAD systems. The paper presents the problems related to the parallelization of the computational process and the methods of solving them, based on the example of typical computational experiments used in the design and optimization of integrated circuits topography.

关键词： parallel computing distributed computing CAD integrated circuit design

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A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data

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Data Intelligence 2023年第3期5卷 663-684页

作者： Irene Kilanioti George A.Papadopoulos School of Electrical and Computer Engineering National Technical University of Athens9 Heroon Polytechneiou St.Zografou Campus Athens15780Greece Department of Computer Science University of Cyprus1 University AvenueAglantziaCY-2109NicosiaCyprus

Sustainable development denotes the enhancement ofliving standards in the present without compromising future generations'*** Development Goals(SDGs)quantify the accomplishment of sustainable development and pave the way for a world worth living in for future *** can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data,as intended by this *** propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves(HSFCs)in order to semantically cluster new uncategorised SDG data and novel indicators,and efficiently place them in the environment of a distributed knowledge graph ***,a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment,for retrieval of indicators and loadbalancing along with an approach for data classification of entrant-indicators is ***,a thorough case study in a distributed knowledge graph environment experimentally evaluates our *** results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data,including intergovernmental organizations,government agencies and social welfare *** approach empowers SDG knowledge graphs for causal analysis,inference,and manifold interpretations of the societal implications of SDG-related actions,as data are accessed in reduced retrieval *** facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching,as semantic cohesion of data is preserved.

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Gold and Silver Price Prediction using Hybrid Machine Learning Models 7

Gold and Silver Price Prediction using Hybrid Machine Learni...

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7th international conference on parallel, distributed and Grid computing, PDGC 2022

作者： Goel, Sakshi Saxena, Merry Sarangi, Pradeepta Kumar Rani, Lekha Chitkara University Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781665454018

Gold and silver are two of the most precious metals in great demand due to their industrial, electrical, and decorative uses. They have gained high popularity in terms of investments and trading. The prediction of their price with good accuracy can be beneficial for people and investors. This paper attempts to evaluate the potency of machine learning models in anticipating the future price of two precious metals (gold and silver) in the Indian market. In particular, the implementation of hybrid machine learning models like CNN (Convolutional Neural Network) and CNN-RNN (CNN with Recurrent Neural Network) have been analyzed through this work. The experiments have been done using the daily traded data from January 2021 to August 2022. The accuracy of the models has been measured using Mean Absolute Percentage Error (MAPE). From the results, it is found that the RNN model has performed well only in the case of gold price prediction, whereas other models have achieved nearly acceptable accuracy. © 2022 IEEE.

关键词： Recurrent neural networks

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Joint Task Partition and Computation Offloading for Latency-Sensitive Services in Mobile Edge networks 18

Joint Task Partition and Computation Offloading for Latency-...

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18th IEEE international conference on Mobility, Sensing and Networking (MSN)

作者： Peng, Yujie Song, Xiaoqin Liu, Fang Xing, Guoliang Song, Tiecheng Southeast Univ Natl Mobile Commun Res Lab Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing Peoples R China Chinese Univ Hong Kong Informat Engn Hong Kong Peoples R China Southeast Univ Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9781665464574

With the development of Internet of Things (IoT), wireless communication networks and Artificial Intelligence (AI), more and more real-time applications such as online games and autonomous driving have emerged. However, due to limited computing power and battery capacity, it has become increasingly difficult for local user devices to take on the full range of computing tasks under tight timing constraints. The emerging Mobile Edge computing (MEC) technology is widely considered to be an important technology for achieving ultra-low latency. However, most of the existing work is focused on non-splittable computation tasks. In fact, data partitioning-oriented applications can be split into multiple subtasks for parallel processing. In this paper, we study the partial computation offloading of multiple detachable tasks in MEC networks, focusing on minimizing the total user device latency in the multi-MEC multiuser scenarios. Considering the dynamic partitioning of tasks, we adopt the barrel theory to construct a linear system of equations to find the optimal solutions and propose an approach for distributed computation offloading based on numerical methods. The simulation results show that the proposed algorithm can reduce the average user device latency by 31% compared with the binary offloading method.

关键词： MEC low-latency partial offloading numerical calculation method

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MicroDACP: Microservice Fault Diagnosis Method Based on Dual Attention Contrastive Learning and Graph Attention networks 20th

MicroDACP: Microservice Fault Diagnosis Method Based on Dual...

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20th international conference on Intelligent computing (ICIC)

作者： Xu, Dongqi Wu, Xu Chen, Ningjiang Liu, Changjian Guangxi Univ Sch Comp & Elect Informat Nanning 530004 Peoples R China Hainan Normal Univ Sch Informat Sci & Technol Haikou 571158 Hainan Peoples R China Guangxi Intelligent Digital Serv Res Ctr Engn Tec Nanning 530004 Peoples R China Guangxi Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Parallel Distributed & Intelligent Comp Nanning 530004 Peoples R China

ISBN: (纸本)9789819756711;9789819756728

With the wide application of microservice architecture, the fault diagnosis of microservice software system becomes difficult due to the complex dependencies between microservices. In order to diagnose the faults of microservices quickly and accurately, this paper proposes MicroDACP. First, a contrastive representation of the dual attention mechanism is used to learn to determine whether the microservice system is anomalous. Second, a graph attention network is used to learn the microservice invocation dependency graph, and the fault root cause scores are ranked using an improved PageRank algorithm. Then, a Sock-shop microservice system is built on a Kubernetes cluster to evaluate the performance of MicroDACP. We conduct extensive experiments on Sock-shop dataset, SMD dataset and AIOps 2020 dataset to compare and analyze our method with the baseline, and the results show that MicroDACP achieves improvements of up to about 0.13 in F1 score for anomaly detection and 0.32 in mean average precision for root cause localization.

关键词： Microservice systems Metrics Anomaly detection Dual attention network Contrastive representation learning Graph attention network Fault root cause localization

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