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CustTest: An Effective Testbed for Testing Customized Routing in Integrated Heterogeneous networks 29

CustTest: An Effective Testbed for Testing Customized Routin...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Zhang, Jialei Xie, Xiaoxuan Yan, Zheng Wang, Haiguang Li, Tieyan Xidian University School of Cyber Engineering Xi'an China Singapore Research Center Huawei Technologies Shield Lab. Singapore

ISBN: (纸本)9798350330717

With the rapid development of the internet and the increasing demand for communication, customized routing has become one of the hot research topics in future network. In order to better meet various communication requirements, it is necessary to test and evaluate existing customized routing algorithms to optimize and explore better customized algorithms. However, existing research on customized routing simulation testing cannot provide a customized routing testbed that supports universality, stability, efficiency, and reliability to test and evaluate different customized routing algorithms. To address this issue, we proposes a testbed called CustTest. CustTest is designed based on Software Defined network (SDN) and network Simulator 3 (NS3), and consists of two sub platforms: a customized routing calculation platform and a customized routing simulation platform. The customized routing calculation platform is used to calculate customized routes and distribute routing tables. This platform can not only flexibly deploy different customized routing algorithms, but also calculate customized routes based on user needs and convert these routes into customized routing tables. The customized routing simulation platform can effectively evaluate the performance of different customized routing algorithms using statistical analysis tools in NS3. We ensure the stability, efficiency, and reliability of CustTest by adding a pool structure and daemons. In addition, we conduct functional and performance tests on CustTest to demonstrate its ability to effectively execute customized routing calculations and its excellent performance in high concurrency environments, as well as the effectiveness of testing the performance of different customized routing algorithms. © 2023 IEEE.

关键词： Customized Routing Computing Platform Customized Routing Simulation Platform Customized Routing Testbed Integrated Heterogeneous network

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Gradient and self-attention enabled convolutional neural network for crack detection in smart cities 29

Gradient and self-attention enabled convolutional neural net...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Xie, Renping Chen, Mengyao Tao, Ming Ding, Kai Chen, HaoHan Dongguan University of Technology Dongguan China

ISBN: (纸本)9798350330717

Intelligent transportation is an important guarantee for the safety and efficiency of urban transportation in smart cities, and regular road pavement inspection is the focus of road and bridge maintenance in intelligent transportation. Cracks in concrete pavement are the most common type of pavement damage and the earliest sign of pavement deterioration. However, existing crack detection algorithms suffer from incomplete crack detection and are easily disturbed by pseudo-cracks such as water spots and leaves. To address the above problems, this paper proposes a convolutional neural network (CNN) method that introduces a gradient module and an attention mechanism. The method adopts a CNN model based on the VGG-16 structure as the main body of the network structure, and optimally adjusts the network structure by incorporating a gradient layer and a self-attention mechanism, accelerating the convergence speed of network training and the global information learning ability. A negative sample dataset with pseudo-cracks, such as leaves, water spots and branches was constructed, and comparative experimental analysis was conducted in terms of both visual judgment and objective indicators. The experimental results show that after the introduction of the gradient layer and the self-attentive mechanism, not only the convergence speed of the network training is faster, but also the cracks in the concrete pavement images can be segmented more completely and accurately. © 2023 IEEE.

关键词： convolution neural network crack detection image segmentation

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Job2Vec: A Self-Supervised Contrastive Learning based HPC Job Power Consumption Prediction Framework 29

Job2Vec: A Self-Supervised Contrastive Learning Based HPC Jo...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Zhang, Jie Song, Jian Li, Xiang Tian, Xuesen Zhao, Zhigang Wu, Lu Wang, Chunxiao Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In the context of the rapid development of big data and artificial intelligence, the field of HPC is facing significant challenges in energy consumption. To address this challenge, this paper proposes a power consumption prediction framework for HPC jobs based on self-supervised contrastive learning, named Job2Vec. Firstly, HPC jobs are clustered into different clusters based on job logs and power consumption curve shapes. Next, high-quality features of power consumption data at different granularities within each cluster are captured through self-supervised contrastive learning. Lastly, an incremental update strategy is introduced to handle the dynamic changes in HPC data. The experimental results demonstrate that, on the Jinan Supercomputing dataset comprising 1170 HPC jobs, the Job2Vec framework outperforms the baseline models overall in terms of prediction performance. Additionally, we also investigated the model's performance in single-step and multi-step forecasting, the impact of different indicators on prediction accuracy, and conducted ablation experiments to confirm the indispensability of each module. This is the first time that self-supervised contrastive learning has been introduced into the HPC field, aiming to provide support for energy scheduling in data centers. © 2023 IEEE.

关键词： Contrastive Learning HPC Power Consumption Prediction

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TPTO: A Transformer-PPO based Task Offloading Solution for Edge Computing Environments 29

TPTO: A Transformer-PPO based Task Offloading Solution for E...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Gholipour, Niloofar De Assuncao, Marcos Dias Agarwal, Pranav Gascon-Samson, Julien Buyya, Rajkumar Univ. of Quebec Dept. of Software Engineering and IT École de Technologie Supérieure Montreal Canada The Univ. of Melbourne CLOUDS Lab School of Computing and Information Systems Australia

ISBN: (纸本)9798350330717

Emerging applications in healthcare, autonomous vehicles, and wearable assistance require interactive and low-latency data analysis services. Unfortunately, cloud-centric architectures cannot fulfill the low-latency demands of these applications, as user devices are often distant from cloud data centers. Edge computing aims to reduce the latency by enabling processing tasks to be offloaded to resources located at the network's edge. However, determining which tasks must be offloaded to edge servers to reduce the latency of application requests is not trivial, especially if the tasks present dependencies. This paper proposes a Deep Reinforcement Learning (DRL) approach called TPTO, which leverages Transformer networks and Proximal Policy Optimization (PPO) to offload dependent tasks of IoT applications in edge computing. We consider users with various preferences, where devices can offload computation to an edge server via wireless channels. Performance evaluation results demonstrate that under fat application graphs, TPTO is more effective than state-of-the-art methods, such as Greedy, HEFT, and MRLCO, by reducing latency by 30.24%, 29.61%, and 12.41%, respectively. In addition, TPTO presents a training time approximately 2.5 times faster than an existing DRL approach. © 2023 IEEE.

关键词： Edge computing reinforcement learning task offloading Transformers

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Design of distributed drive electric vehicle control architecture based on module division 6

Design of distributed drive electric vehicle control archite...

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2023 6th International conference on Advanced Algorithms and Control Engineering, ICAACE 2023

作者： Liu, Ning Wang, Bin Wang, Yi Li, Zhengbo Ou, Yang China Automotive Engineering Research Institute Co. Ltd China

At present, with the deepening of vehicle intelligence and electrification, the logic chaos of the whole vehicle control architecture leads to complex procedures, which cannot adapt to the large-scale, team, and parallel development work, and is not conducive to the later maintenance, upgrading, and platform of the controller. This paper analyzes the types and functions of the vehicle electrical appliances, compiles the vehicle electrical control circuit and CAN (Controller Area network, CAN) communication network, decouples the control program design from the hardware input signal by reasonably dividing the signal processing flow of the control program, and then divides the vehicle control program into modules to meet the platform-based requirements of the control strategy. © Published under licence by IOP Publishing Ltd.

关键词： Control system synthesis

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High-performance network Traffic Classification based on Graph Neural network 6

High-performance Network Traffic Classification Based on Gra...

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6th IEEE Information Technology, networking, Electronic and Automation Control conference, ITNEC 2023

作者： Pang, Bo Fu, Yongquan Ren, Siyuan Jia, Yan Harbin Institute of Technology College of Computer Science and Technology Shenzhen China National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing College of Computer Changsha China Peng Cheng Laboratory Shen Zhen China

ISBN: (纸本)9781665460033

network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to automatically extract features from packet streams. Unfortunately, current approaches fail to effectively combine the structural information of traffic packets with the content features of the packets, resulting in limited classification accuracy. In this paper, we propose a graph neural network model for network traffic classification, which can well perceive the interaction feature of packets in traffic. Firstly, we design a graph structure for packets' flows to hold the interaction information between packets, which embeds both packet contents and sequence relationships into a unified graph. Secondly, we propose a graph neural network framework for graph classification to automatically learn the structural features of the packets' flows together with the packets' features. Extensive evaluation results on real-world traffic data show that the proposed model improves the prediction accuracy of improves the prediction accuracy by 2% to 37% for malicious traffic classification. © 2023 IEEE.

关键词： Graph neural networks

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DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme 28

DSPG: Decentralized Simultaneous Perturbations Gradient Desc...

引用

28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Ramaswamy, Arunselvan Paderborn Univ Dept Elect & Comp Sci Engn D-33098 Paderborn Germany

ISBN: (纸本)9781728165820

distributed descent-based methods are an essential toolset to solving optimization problems in multi-agent system scenarios. Here the agents seek to optimize a global objective function through mutual cooperation. Oftentimes, cooperation is achieved over a wireless communication network that is prone to delays and errors. There are many scenarios wherein the objective function is either non-differentiable or merely observable. In this paper, we present a cross-entropy based distributed stochastic approximation algorithm (SA) that finds a minimum of the objective, using only samples. We call this algorithm Decentralized Simultaneous Perturbation Stochastic Gradient, with Constant Sensitivity Parameters (DSPG). This algorithm is a two fold improvement over the classic Simultaneous Perturbation Stochastic Approximations (SPSA) algorithm. Specifically, DSPG allows for (i) the use of old information from other agents and (ii) easy implementation through the use simple hyper-parameter choices. We analyze the biases and variances that arise due to these two allowances. We show that the biases due to communication delays can be countered by a careful choice of algorithm hyper-parameters. The variance of the gradient estimator and its effect on the rate of convergence is studied. We present numerical results supporting our theory. Finally, we discuss an application to the stochastic consensus problem.

关键词： distributed optimization distributed approximate gradient method cross-entropy based gradient approximation optimization over networks with unbounded delays

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Partition Pruning: parallelization-Aware Pruning for Dense Neural networks 28

Partition Pruning: Parallelization-Aware Pruning for Dense N...

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28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Shahhosseini, Sina Albaqsami, Ahmad Jasemi, Masoomeh Bagherzadeh, Nader Univ Calif Irvine Elect Engn & Comp Sci Dept Irvine CA 92697 USA Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781728165820

As recent neural networks are being improved to be more accurate, their model's size is exponentially growing. Thus, a huge number of parameters requires to be loaded and stored from/in memory hierarchy and computed in processors to perform training or inference phase of neural network processing. Increasing the number of parameters causes a big challenge for real-time deployment since the memory bandwidth improvement's trend cannot keep up with models' complexity growing trend. Although some operations in neural networks processing are computational intensive such as convolutional layer computing, computing dense layers face with memory bandwidth bottleneck. To address the issue, the paper has proposed Partition Pruning for dense layers to reduce the required parameters while taking into consideration parallelization. We evaluated the performance and energy consumption of parallel inference of partitioned models, which showed a 7.72x speedup of performance and a 2.73x reduction in the energy used for computing pruned fully connected layers in TinyVGG16 model in comparison to running the unpruned model on a single accelerator. Besides, our method showed a limited reduction in accuracy while partitioning fully connected layers.

关键词： parallelization Deep Neural network Pruning Partitioning Hardware Accelerator

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A Federated Learning Scheme based on Reputation Evaluation and Blockchain 29

A Federated Learning Scheme Based on Reputation Evaluation a...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Tang, Siyu An, Jian Sun, Xiangyan Hu, Xiancheng Wang, Feifei Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Henan Jiaoyuan Engineering Technology Group Co. Ltd Zhengzhou China Shaanxi Province Key Laboratory of Computer Network Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350330717

Federated learning is an emerging distributed machine learning paradigm, where multiple data sources collaborate to train a model without sharing the original data, safeguarding user data privacy. Nevertheless, users, whether intentionally or unintentionally, may contribute low-quality data that can impact model quality or infer user information from the model parameters, potentially leading to privacy breaches. Selecting appropriate participants for training is still a challenge needing resolution. This paper introduces a federated learning scheme called FREB, which leverages a reputation evaluation mechanism and blockchain technology. The foundation of this scheme is blockchain, and it incorporates reputation as an indicator of participant reliability. Reputation is computed using a multi-weight subjective logic model. Before the training process begins, participants are chosen based on their reputation. Furthermore, the paper presents a novel model aggregation approach. By injecting noise into the uploaded model parameters, the scheme ensures that potential attackers cannot glean sensitive data information from the parameters. To validate the efficacy of FREB, the authors conducted simulation experiments using real datasets. The results of these experiments demonstrate that FREB is capable of thwarting malicious node attacks and promoting high-quality collaborative learning. It achieves this while preserving data privacy and maintaining model accuracy. © 2023 IEEE.

关键词： Blockchain Differential privacy Federated learning Reputation evaluation

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MLN-Subdue: Substructure Discovery In Homogeneous Multilayer networks 16

MLN-Subdue: Substructure Discovery In Homogeneous Multilayer...

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16th International conference on Knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th International Joint conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Rai, Anish Roy, Anamitra Santra, Abhishek Chakravarthy, Sharma The University of Texas at Arlington ArlingtonTX76019 United States

ISBN: (纸本)9789897587160

Substructure discovery is a well-researched problem for graphs (both simple and attributed) for knowledge discovery. Recently, multilayer networks (or MLNs) have been shown to be better suited for modeling complex datasets that have multiple entity and relationship types. However, the MLN representation brings new challenges in finding substructures due to the presence of layers, and substructure discovery methods for MLNs are currently not available. This paper proposes a substructure discovery algorithm for homogeneous MLNs using the decoupling approach. In HoMLNs, each layer has same or a common subset of nodes but different intralayer connectivity. This algorithm has been implemented using the Map/Reduce framework to handle arbitrarily large layers and to improve the response time through distributed and parallel processing. In the decoupled approach, each layer is processed independently (without using any information from other layers) and in parallel and the substructures generated from each layer are combined after each iteration to generate substructures that span layers. The focus is on the correctness of the algorithm and resource utilization based on the number of layers. The proposed algorithm is validated through extensive experimental analysis on large real-world and synthetic graphs with diverse graph characteristics. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： network architecture

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