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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

ISBN: (纸本)9789819708581

The proceedings contain 165 papers. The special focus in this conference is on Algorithms and Architectures for Parallel processing. The topics include: MDCF: Multiple Dynamic Cuckoo Filters for LSM-Tree;a Game Theory Based Task Offloading Scheme for Maximizing Social Welfare in Edge Computing;research on Dos Attack Simulation and Detection in Low-Orbit Satellite network;malware Detection Method Based on Visualization;TOC: Joint Task Offloading and Computation Reuse in Vehicular Edge Computing;distributed Task Offloading for IoAV Using DDP-DQN;a Task Offloading and Resource Allocation Optimization Method in End-Edge-Cloud Orchestrated Computing;multi-agent Cooperative Intrusion Detection Based on Generative Data Augmentation;an Uncertainty-Aware Auction Mechanism for Federated Learning;Long Short-Term Deterministic Policy Gradient for Joint Optimization of Computational Offloading and Resource Allocation in MEC;query Optimization Mechanism for Blockchain-Based Efficient Data Traceability;research on the Evolution Path of network Hotspot Events Based on the Event Evolutionary Graph;Task Offloading in UAV-Assisted Vehicular Edge Computing networks;path Planning of Coastal Ships Based on Improved Hybrid A-Star;data Augmentation Method Based on Partial Noise Diffusion Strategy for One-Class Defect Detection Task;K Asynchronous Federated Learning with Cosine Similarity Based Aggregation on Non-IID Data;MPQUIC Transmission Control Strategy for SDN-Based Satellite network;an Energy Prediction Method for Energy Harvesting Wireless Sensor with Dynamically Adjusting Weight Factor;Bayesian Optimization for Auto-tuning Convolution neural network on GPU;persistent Sketch: A Memory-Efficient and Robust Algorithm for Finding Top-k Persistent Flows;faCa: Fast Aware and Competition-Avoided Balancing for Data Center network;Optimizing GNN Inference processing on Very Long Vector Processor;GDTM: Gaussian Differential Trust Mechanism for Optimal Recommender System;privacy-Enhanced Dyna

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network Traffic Anomaly Detection Method Based on Improved Clustering Analysis

Network Traffic Anomaly Detection Method Based on Improved C...

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2022 International conference on Signal processing, Computer networks, and Communications, SPCNC 2022

作者： Xu, Chao Liu, Ying Zhang, Wenjie Meng, Meng Teng, Ziyi Tian, Yunjia State Grid Liaoning Electric Power Supply Co. LTD. Information & Telecommunication Branch Shenyang110000 China

ISBN: (纸本)9781510664616

With the surge in the amount of data transmitted on the network, intelligent learning and other technologies have emerged to solve the problem of anomaly detection of streaming data in large data. For network security issues, based on the extraction of network traffic characteristics, network traffic classification or clustering is an important technical means to discover network faults and network attacks. In this paper, a distributed detection framework for detecting anomalous behaviors of encrypted network traffic is proposed. The intelligent router is adopted to obtain the encrypted network traffic monitoring stub, and then the neural network codec is used to adaptively learn the characteristics of the encrypted traffic and identify the abnormal behavior of the encrypted protocol traffic. A wide coverage traffic pattern extraction algorithm based on the network state sequence is designed to obtain the traffic patterns that represent the network conditions of the data center. Finally, the simulation test verifies that the model has frequent traffic patterns with priority. The performance of the network anomaly detection model is better than other detection methods, which improves the accuracy of detection and has a better recognition effect. © 2023 SPIE.

关键词： network security

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STEAM: Observability-Preserving Trace Sampling 2023

STEAM: Observability-Preserving Trace Sampling

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31st ACM Joint Meeting of the European Software Engineering conference / Symposium on the Foundations-of-Software-Engineering (ESEC/FSE)

作者： He, Shilin Feng, Botao Li, Liqun Zhang, Xu Kang, Yu Lin, Qingwei Rajmohan, Saravan Zhang, Dongmei Microsoft Beijing Peoples R China Microsoft Redmond WA USA

ISBN: (纸本)9798400703270

In distributed systems and microservice applications, tracing is a crucial observability signal employed for comprehending their internal states. To mitigate the overhead associated with distributed tracing, most tracing frameworks utilize a uniform sampling strategy, which retains only a subset of traces. However, this approach is insufficient for preserving system observability. This is primarily attributed to the long-tail distribution of traces in practice, which results in the omission or rarity of minority yet critical traces after sampling. In this study, we introduce an observability-preserving trace sampling method, denoted as STEAM, which aims to retain as much information as possible in the sampled traces. We employ Graph neural networks (GNN) for trace representation, while incorporating domain knowledge of trace comparison through logical clauses. Subsequently, we employ a scalable approach to sample traces, emphasizing mutually dissimilar traces. STEAM has been implemented on top of OpenTelemetry, comprising approximately 1.6K lines of Golang code and 2K lines of Python code. Evaluation on four benchmark microservice applications and a production system demonstrates the superior performance of our approach compared to baseline methods. Furthermore, STEAM is capable of processing 15,000 traces in approximately 4 seconds.

关键词： distributed tracing trace sampling graph neural network

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distributed Detection of Anomalies in the network Flow Using Generative Adversarial networks

Distributed Detection of Anomalies in the Network Flow Using...

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2022 International Russian Automation conference, RusAutoCon 2022

作者： Grekov, Mikhail Sychugov, Alexey Tula State University Department of Information Security Tula Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

Ensuring information security in distributed information systems is a time-consuming task associated with processing large amounts of network data. Intrusion detection systems based on anomalies and machine learning algorithms are a promising tool for protecting distributed computing networks. The complexity of using such a means of protection at real industrial facilities lies in the high level of false positives. Another problem is the need to process a large number of network packets, which requires high computational costs. In this paper, a model of distributed processing and anomaly detection in network flows is proposed. To minimize the processed traffic, an attacker's behavior model is used. Improving the efficiency of anomaly detection is achieved through the use of generative-adversarial neural networks. Creating synthetic traffic based on the characteristics of a real network will allow us to balance data sets for training the anomaly detection core. Thus, the efficiency of detecting anomalies in network traffic and the resistance to adversarial attacks will increase. The distributed model allows to reduce the load on the communication lines and minimize the transmission of confidential data. The multilevel structure of this model will allow detecting multi-stage attacks by processing notification streams received from nodes of a distributed system. © 2022 IEEE.

关键词： Generative adversarial networks

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Stash: A comprehensive stall-centric characterization of public cloud VMs for distributed deep learning 43

Stash: A comprehensive stall-centric characterization of pub...

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43rd IEEE International conference on distributed Computing Systems (ICDCS)

作者： Sharma, Aakash Bhasi, Vivek M. Singh, Sonali Jain, Rishabh Gunasekaran, Jashwant Raj Mitra, Subrata Kandemir, Mahmut Taylan Kesidis, George Das, Chita R. Penn State Univ Comp Sci & Engn University Pk PA 16802 USA Adobe Res San Francisco CA USA

ISBN: (纸本)9798350339864

Deep neural networks (DNNs) are increasingly popular owing to their ability to solve complex problems such as image recognition, autonomous driving, and natural language processing. Their growing complexity coupled with the use of larger volumes of training data (to achieve acceptable accuracy) has warranted the use of GPUs and other accelerators. Such accelerators are typically expensive, with users having to pay a high upfront cost to acquire them. For infrequent use, users can, instead, leverage the public cloud to mitigate the high acquisition cost. However, with the wide diversity of hardware instances (particularly GPU instances) available in public cloud, it becomes challenging for a user to make an appropriate choice from a cost/performance standpoint. In this work, we try to address this problem by (i) introducing a comprehensive distributed deep learning (DDL) profiler Stash, which determines the various execution stalls that DDL suffers from, and (ii) using Stash to extensively characterize various public cloud GPU instances by running popular DNN models on them. Specifically, it estimates two types of communication stalls, namely, interconnect and network stalls, that play a dominant role in DDL execution time. Stash is implemented on top of prior work, DS-analyzer, that computes only the CPU and disk stalls. Using our detailed stall characterization, we list the advantages and shortcomings of public cloud GPU instances for users to help them make an informed decision(s). Our characterization results indicate that the more expensive GPU instances may not be the most performant for all DNN models and that AWS can sometimes sub-optimally allocate hardware interconnect resources. Specifically, the intra-machine interconnect can introduce communication overheads of up to 90% of DNN training time and the network-connected instances can suffer from up to 5x slowdown compared to training on a single instance. Furthermore, (iii) we also model the impact of DNN m

关键词： Characterization Cloud computing distributed deep learning Public cloud GPU

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STB-GraCapsNet: A Novel Capsule network Structure with Swin Transformer Block 25th

STB-GraCapsNet: A Novel Capsule Network Structure with Swin...

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25th International conference on Parallel and distributed Computing, Applications and Technologies, PDCAT 2024

作者： Zhang, Chunying Dong, Ziao Wang, Liya Liu, Lu Ren, Jing Ma, Jiang Liu, Bin College of Science North China University of Science and Technology 21 Bohai Road Caofeidian Xincheng Hebei Tangshan063210 China Big data and Social Computing Research Center Hebei University of Science and Technology Hebei Shijiazhuang0500198 China

ISBN: (纸本)9789819642069

Capsule network is a new type of neural network encoding features into capsules and constructing the part-whole relationships, which demonstrated good performance in image classification. However, it has some issues such as weak feature extraction ability and large number of training parameters, leading to performance decline when processing images with complex background. To address these issues, this article proposes a novel capsule network structure called STB-GraCapsNet, with key improvements as follows: 1) It uses Swin Transformer Block with the shift window attention mechanism for data preprocessing to obtain richer features;2) the features are then processed through Linear Embedding and passed into the Primary Capsule Layer to generate high-dimension feature capsules, which reduces primary capsules number and improves feature extraction ability of the network. Ablation and comparative experiments based on multiple datasets show that STB-GraCapsNet performs better on complex datasets: 77.8% and 36.2% reduction in parameters number of capsule structure, and 12.07% and 5.22% improvement in model accuracy, as compared to CapsNet and GraCapsNet, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Signal encoding

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Efficient distributed Parallel Inference Strategies via Block-based DNN Structure in Edge-to-IoT Continuum 24

Efficient Distributed Parallel Inference Strategies via Bloc...

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9th International conference on Intelligent Information Technology (ICIIT)

作者： Choi, Inhun Akhter, Sharmen Jeong, Hong-Ju Huh, Eui-Nam Kyung Hee Univ Dept Comp Sci & Engn Gyeonggi Do 17104 South Korea Kyung Hee Univ Dept Artificial Intelligence Yeonggi Do 17104 South Korea

ISBN: (纸本)9798400716713

Recently, AI and deep neural networks have found extensive applications in mobile devices, drones, carts, and more. To meet the demands of processing large-scale data and providing DNN inference services with minimal latency, there is a need. However, IoT devices, with their limited computing capabilities, are not well-suited for AI inference. Moreover, considering the diverse requirements of different services, it is necessary to provide inference services that address these variations. To address these challenges, many previous studies have explored collaborative approaches between edge servers and cloud servers by partitioning DNN models. However, these methods face difficulties in finding optimal partitioning points for splitting DNN models and are heavily influenced by network bandwidth since intermediate computation results need to be transmitted to other devices. In this paper, we propose the Adaptive block-based DNN network inference framework. This involves breaking down a large DNN model into block-level networks, training them using knowledge distillation techniques to enable inference only through each block network. Subsequently, dynamic block-level inference calculations are offloaded based on the computing capabilities of edge clusters, providing inference results. Even when using multiple devices, our method is not affected by network bandwidth since only input images need to be transmitted. Experimental results demonstrate that our approach consistently reduces inference latency as the number of devices increases. Additionally, by controlling the trade-off between latency and accuracy, we can provide inference services tailored to various latency requirements.

关键词： DNN inference Block-based DNN network trade-off between latency and accuracy

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FPGA Accelerated Convolutional neural network for Detection of Cardiac Arrhythmia 4

FPGA Accelerated Convolutional Neural Network for Detection ...

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4th IEEE International conference on VLSI Systems, Architecture, Technology and Applications, VLSI SATA 2024

作者： Mangaraj, Soumyashree Oraon, Pawan Ari, Samit Swain, Ayas Kanta Mahapatra, Kamalakanta National Institute Of Technology Department Of Electronics And Communication Engineering Rourkela India

ISBN: (纸本)9798350362268

Electrocardiogram signals (ECG) are vital features to identify a healthy body;diagnosing cardiovascular diseases (CVDs) automatically with resource constraint devices at the edge has caught a significant attention in the rapid growth of smart health-care system. The rapidly developing smart world of IoT demands for low latency, less complex, and energy efficient architectures, and their deployment in real time scenarios. Field Programmable Gate Arrays (FPGAs) is a trading distributed embedded platform proving parallel processing, and flexibility in re-design with its re-configurable advancements. This work demonstrates a spilt 2D convolutional neural network (CNN) performing ECG beat classification on Xilinx's ZCU 104 FPGA board. The convolution has been carried out depth-wise, and performing a channel-wise addition of the half-split feature maps, so as to minimize the resource utilization by reducing the trainable parameters. A customized FPGA IP has been generated using Vitis High Level Synthesis (HLS) tool, and is implemented on ZCU 104 FPGA for the proposed less complex light-weight model. The design has achieved an accuracy of 98.64% in software, and consumed a total power of 4.177W with a latency of 2.19E8ns in classifying arrhythmia reference to Association for the Advancement of Medical Instrumentation-AAMI EC57 standard. © 2024 IEEE.

关键词： Electrocardiograms

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ANN based Faulty Phase Identification and Fault Resistance Estimation in Inverter Interfaced Grid Connected Microgrids 3

ANN based Faulty Phase Identification and Fault Resistance E...

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3rd IEEE Applied Signal processing conference, ASPCON 2023

作者： Debnath, Anushka Roy, Subhamita National Institute of Technology Department of Computer Science and Engineering Durgapur India Jadavpur University Department of Electrical Engineering Kolkata India

ISBN: (纸本)9798350325881

Fault detection is a challenging task in inverter interfaced microgrids as fault current in inverter interfaced distributed generator (IIDG) is limited. The different control strategies adopted by IIDGs make the fault detection more complex. For quick restoration of normal supply, faulty phase identification is essential. The root cause of the fault can be assessed from the estimation of fault resistance. This paper utilizes artificial neural network (ANN) to identify the faulty phase in feeders and sub-feeders in the microgrid acquiring current signals from the substation. Further, fault resistance has been estimated utilizing ANN. Power spectral density (PSD) of the three phase current signals at the substation calculated utilizing discrete wavelet transform (DWT) has been fed to ANN model to classify faults and estimate fault resistance. Only half cycle of the current signal after fault is required and the simulation results establishes the efficacy of the proposed technique. © 2023 IEEE.

关键词： neural networks

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Training Large-Vocabulary neural Language Models by Private Federated Learning for Resource-Constrained Devices 48

Training Large-Vocabulary Neural Language Models by Private ...

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48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Xu, Mingbin Song, Congzheng Tian, Ye Agrawal, Neha Granqvist, Filip Van Dalen, Rogier Zhang, Xiao Argueta, Arturo Han, Shiyi Deng, Yaqiao Liu, Leo Walia, Anmol Jin, Alex Apple

ISBN: (纸本)9781728163277

Federated Learning (FL) is a technique to train models on distributed edge devices with local data samples. Differential Privacy (DP) can be applied with FL to provide a formal privacy guarantee for sensitive data on device. Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP. However, the noise required to guarantee differential privacy increases as the model size grows, which often prevents convergence. We propose Partial Embedding Updates (PEU), a novel technique to reduce the impact of DP-noise by decreasing payload size. Furthermore, we adopt Low Rank Adaptation (LoRA) and Noise Contrastive Estimation (NCE) to reduce the memory demands of large models on compute-constrained devices. We demonstrate in simulation and with real devices that this combination of techniques makes it possible to train large-vocabulary language models while preserving accuracy and privacy. © 2023 IEEE.

关键词： Sensitive data

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