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An Efficient network Flow Rules Compression Method Based On In-network Computation On Cloud computing 29

An Efficient Network Flow Rules Compression Method Based On ...

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29th IEEE international conference on parallel and Distributed Systems, ICPADS 2023

作者： Zhang, Penghao Zhang, Xinyi Shanxi University China Chinese Academy of Sciences Computer Network Information Center China

ISBN: (纸本)9798350330717

The development of cloud computing has led to the explosion of network traffic. The switches of cloud computing make it hard to process large-scale network traffic. Prior approaches proposed flow rules compression methods with splitting matching fields, expanding switch storage space, or reconstructing network architecture. The methods incurred little performance improvement in the network. The technology of innetwork computation(INC) has been widely used to accelerate the network of cloud computing and data-intensive distributed applications. The computing tasks performed on servers are offloaded to the network through INC. We implement a flow rules compression method(NetFR) based on INC to accelerate the network performance of cloud computing. NetFR adopts heuristic methods which include rule-deduplication and optimal coverage to compress flow rules, and offloads compressed rules into switches to accelerate the performance of the network on cloud computing. Finally, we evaluate NetFR and show that the performance of NetFR is 1.3x better than other methods of network packet matching. Furthermore, NetFR can achieve 98% compression ratio on average and 99.99% on highest. © 2023 IEEE.

关键词： Cloud computing In-network computing network Flow Rules Compression Programmable Switch

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Foundational Models for Malware Embeddings Using Spatio-Temporal parallel Convolutional networks

Foundational Models for Malware Embeddings Using Spatio-Temp...

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international conference on computing, networking and Communications (ICNC)

作者： Nandakumar, Dhruv Quinn, Devin Soba, Elijah Kim, Eunyoung Redino, Christopher Chan, Chris Choi, Kevin Rahman, Abdul Bowen, Edward Deloitte & Touche LLP London England

ISBN: (纸本)9798350371000;9798350370997

As the complexity of malicious tactics, techniques, and procedures (TTPs) continuously grows to evade detection, so does the need for advanced methods capable of capturing and characterizing malware behavior. Current methodologies for task-specific models in cyber threat detection are time-intensive to build and do not generalize well to other tasks. In this paper, the authors introduce a novel method that combines convolutional neural networks, standard graph embedding techniques, and a metric learning objective to extract meaningful information from network flow data and create strong embeddings characterizing malware behavior. These embeddings enable the development of highly accurate, efficient, and generalizable machine learning models for tasks such as malware strain classification, zero day threat detection, and closest attack type attribution as demonstrated in this paper. A shift from task specific objectives to strong embeddings will not only allow rapid iteration of cyber-threat detection models, but also allow different modalities to be introduced in the development of these models.

关键词： Embeddings

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High Altitude Platform Station (HAPS)-Enabled parallel computing for Handoff Control in Vehicular networks

High Altitude Platform Station (HAPS)-Enabled Parallel Compu...

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IEEE international conference on Communications (IEEE ICC)

作者： Ren, Qiqi Abbasi, Omid Kurt, Gunes Karabulut Yanikomeroglu, Halim Chen, Jian Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Polytech Montreal Dept Elect Engn Montreal PQ H3T 1J4 Canada

ISBN: (纸本)9781538674628

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks due to its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The proposed scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. We formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation-based methods are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner but also it improves the delay performance and maintains the delay stability.

关键词： Internet of vehicles (IoV) High altitude platform station (HAPS) distributed computing edge network handoff

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Multiobjective Hyperparameter Optimization for Deep Learning Interatomic Potential Training Using NSGA-II 23

Multiobjective Hyperparameter Optimization for Deep Learning...

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52nd international conference on parallel Processing (ICPP)

作者： Coletti, Mark Sedova, Ada Chahal, Rajni Gibson, Luke Roy, Santanu Bryantsev, Vyacheslav S. Oak Ridge Natl Lab Oak Ridge TN 37830 USA

ISBN: (纸本)9798400708428

Deep neural network (DNN) potentials are an emerging tool for simulation of dynamical atomistic systems, with the promise of quantum mechanical accuracy at speedups of 10000x. As with other DNN methods, hyperparameters used during training can make a substantial difference in model accuracy, and optimal settings vary with dataset. To enable rapid tuning of hyperparameters for DNN potential training, we developed a scalable multiobjective optimization evolutionary algorithm for supercomputers and tested it on the Summit system at the Oak Ridge Leadership computing Facility (OLCF). The multiobjective approach is required due to the coupling of two learned values defining the potential: the energy and force. Using a large-scale implementation of the NSGA-II algorithm adapted for training DNN potentials, we discovered several optimal multiobjective combinations, including best choices of activation functions, learning rate scaling scheme, and pairing of the two radial cutoffs used in the three dimensional descriptor function.

关键词： evolutionary computation hyperparameter optimization high performance computing machine learning neural networks multiobjective optimization neural network potentials molecular simulation

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Multi-scale parallel Fusion Convolution network for Crop Disease Identification 8

Multi-scale Parallel Fusion Convolution Network for Crop Dis...

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8th international conference on Cloud computing and Big Data Analytics (ICCCBDA)

作者： Ren, Huyue Chen, Wanhui Liu, Peishun Tang, Ruichun Wang, Jinyu Ge, Chang Ocean Univ China Sch Comp Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9781665455336

In order to identify crop disease accurately and efficiently, we propose a multi-scale parallel fusion convolution network called MSPFNet for the automatic crop disease identification. It combines multi-scale convolution and attention mechanism. By mixing convolution kernels of different sizes in the same convolution unit, multi-scale convolution can identify crop disease features at different scales better. In this paper, Convolutional Block Attention Module is introduced to improve the representation ability of the model. By analyzing the information of spatial attention and channel attention, model can pay more attention to important features. In addition, we design a feature fusion module to realize the fusion of features at different depths. And feature fusion module can solve the problem of the disappearance of disease features in the process of image feature extraction. We validate the proposed model on the publicly available apple leaf pest data set. Experimental results show that the proposed MSPFNet can achieve higher recognition accuracy.

关键词： crop disease automatic identification multiscale convolution feature fusion

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An ANN-Guided Multi-Objective Framework for Power-Performance Balancing in HPC Systems 21

An ANN-Guided Multi-Objective Framework for Power-Performanc...

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21st ACM international conference on computing Frontiers (CF)

作者： Maas, William de Souza, Paulo S. S. Luizelli, Marcelo C. Rossi, Fabio D. Navaux, Philippe O. A. Lorenzon, Arthur F. Univ Fed Rio Grande do Sul Informat Inst Porto Alegre RS Brazil UNIPAMPA Alegrete RS Brazil IFFar Alegrete RS Brazil

ISBN: (纸本)9798400705977

Power-performance efficiency has become one of the most critical issues in evolving High-Performance computing systems (HPC) towards Exaflops. Thread-level parallelism (TLP) exploitation, dynamic voltage and frequency scaling (DVFS), and uncore frequency scaling (UFS) are methods widely applied to better balance the power consumption and performance improvements of parallel applications. However, selecting ideal combinations of these knobs for every application is challenging due to the massive number of possible solutions, as there is no unique combination that delivers at the same time the best performance and the lowest power consumption. Given that, we propose HPC-PPO (power-performance optimizer), a multi-objective optimization strategy driven by an artificial neural network that leverages hardware and software features of parallel applications to predict Pareto-efficient configurations of TLP degree, DVFS, and UFS that optimize the balance between power and performance. When validating HPC-PPO on three multicore processors with twenty-five applications, we show that HPC-PPO can predict combinations very close to the best ones found by an exhaustive search. We also show that the Pareto-efficient configurations predicted by HPC-PPO improve parallel applications' performance by 30.7% while spending 23.9% less power when compared to state-of-the-art strategies.

关键词： High-Performance computing Power-Performance Efficiency Artificial Neural network

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GraNNDis: Fast Distributed Graph Neural network Training Framework for Multi-Server Clusters 24

GraNNDis: Fast Distributed Graph Neural Network Training Fra...

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international conference on parallel Architectures and Compilation Techniques (PACT)

作者： Song, Jaeyong Jang, Hongsun Lim, Hunseong Jung, Jaewon Kim, Youngsok Lee, Jinho Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (数字)9798400706318

ISBN: (纸本)9798400706318

Graph neural networks (GNNs) are one of the rapidly growing fields within deep learning. While many distributed GNN training frameworks have been proposed to increase the training throughput, they face three limitations when applied to multi-server clusters. 1) They suffer from an inter-server communication bottleneck because they do not consider the inter-/intra-server bandwidth gap, a representative characteristic of multi-server clusters. 2) Redundant memory usage and computation hinder the scalability of the distributed frameworks. 3) Sampling methods, de facto standard in mini-batch training, incur unnecessary errors in multi-server clusters. We found that these limitations can be addressed by exploiting the characteristics of multi-server clusters. Here, we propose GraNNDis, a fast distributed GNN training framework for multi-server clusters. Firstly, we present Flexible Preloading, which preloads the essential vertex dependencies server-wise to reduce the low-bandwidth inter-server communications. Secondly, we introduce Cooperative Batching, which enables memory-efficient, less redundant mini-batch training by utilizing high-bandwidth intra-server communications. Thirdly, we propose Expansion-aware Sampling, a cluster-aware sampling method, which samples the edges that affect the system speedup. As sampling the intra-server dependencies does not contribute much to the speedup as they are communicated through fast intra-server links, it only targets a server boundary to be sampled. Lastly, we introduce One-Hop Graph Masking, a computation and communication structure to realize the above methods in multi-server environments. We evaluated GraNNDis on multi-server clusters, and it provided significant speedup over the state-of-the-art distributed GNN training frameworks. GraNNDis is open-sourced at https://***/AISSNU/GraNNDis_Artifact to facilitate its use.

关键词： Distributed Systems Systems for Deep Learning Graph Neural network Training Distributed Graph Neural network Training

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Research and simulation of adaptive routing mechanism in InfiniBand network 3

Research and simulation of adaptive routing mechanism in Inf...

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3rd international conference on Advanced Manufacturing Technology and Manufacturing Systems, ICAMTMS 2024

作者： Wu, Ruolan Wan, Wei Li, Junhong School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China Co. Ltd. Beijing100193 China School of Software Tsinghua University Beijing100084 China

ISBN: (纸本)9781510681804

InfiniBand interconnect network is widely used in parallel computing. network adapter (HCA) is one of the necessary hardware components for architecture deployment. With the expansion of nodes in parallel computing system, InfiniBand network may have higher performance requirements. However, the current InfiniBand network uses deterministic routing, and adaptive routing can be introduced to meet higher requirements. Because the original InfiniBand protocol is strictly sequence-preserving, adding adaptive routes to the network improves performance but requires out-of-order rearrangement of network cards, which requires a certain cache capability. In this paper, the function extension of InfiniBand protocol is designed to realize the adaptive function and complete the modeling and simulation. Two indexes related to out-of-order are also designed in this paper, and the direct influence of adaptive routing on packet out-of-order and network adapter buffer depth is studied from many aspects. Through the simulation of the above system, the experimental result of All-to-All communication between model nodes shows that under the Greedy and RoundRobin adaptive port selection algorithms, the buffer depth required by the network card is 61 and 62 2KB of packet space, respectively. This provides an estimate of the buffer that the network card needs to handle out-of-order packets. © 2024 SPIE.

关键词： parallel processing systems

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Quantum Cloud with Communication, computing, Caching, and Cipher (4C) Resource Coordination

Quantum Cloud with Communication, Computing, Caching, and Ci...

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27th international conference on Optical network Design and Modeling (ONDM)

作者： Zhu, Qingcheng Zhao, Yongli Yu, Xiaosong Zhang, Jie Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing Peoples R China

ISBN: (纸本)9783903176546

The cloud services requiring communication, computing and caching (3C) resources are supported by datacenter networks (DCNs). The service security is threatened due to the development of quantum computers. Quantum key distribution (QKD) technology promises to share secret keys as cipher resources between remote users with information-theoretic security. The quantum cloud is illustrated which is realized by the QKD-secured cloud datacenter network. In the quantum cloud, the communication, computing, caching and cipher (4C) resources are provided for data transmission, processing, storing and encryption. To solve the newly emerged 4C resource coordination problem, a 4C-oriented control architecture is designed and the heuristic algorithms for securing service function chains are innovatively proposed. The simulation results show that the proposed algorithms can achieve the great service success ratio performance compared with the baseline.

关键词： quantum cloud communication computing caching cipher quantum key distribution

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Quantum Information Technology for network Security: An Application-Focused Overview 21

Quantum Information Technology for Network Security: An Appl...

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21st IEEE international Symposium on parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Huan Lai, XiaoYang China Electronics Technology Group Corporation Beijing China China Innovation Fund Management Co. Ltd. Beijing China

ISBN: (纸本)9798350329223

In recent years, Quantum information technology (QIT) has accelerated its development in academic research with continuously highlights and achievements that have attracted the attention of the entire society. network security is currently the main application field of QIT and two types of quantum security products have been released: Quantum Key Distribution systems and Quantum Random Number Generators. This article introduces the two quantum security products and reveals that the application of QIT in network security is currently in its initial stage, consisting of a total of six stages. Both product and market sides need continuous innovation and development, and it may take time for QIT to truly manifest significant commercial value. It also offers prospects for the technological evolution of the two quantum products in the further process of industrialization, mainly involving the multiplexing of QKD with classical optical communication and the chip based QRNG. The aim of this study is to consolidate consensus and contribute to the industrial application of QIT in network security. © 2023 IEEE.

关键词： network security

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