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distributed Explicit State Space Exploration with State Reconstruction for RDMA networks 26

Distributed Explicit State Space Exploration with State Reco...

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26th international conference on Engineering of Complex Computer Systems (ICECCS)

作者： Evangelista, Sami Petrucci, Laure Kristensen, Lars Michael Univ Sorbonne Paris Nord LIPN CNRS UMR 7030 99 Av JB Clement F-93430 Villetaneuse France Western Norway Univ Appl Sci Fac Engn & Sci Inndalsveien 28 N-5020 Bergen Norway

ISBN: (纸本)9781665401623

The inherent computational complexity of validating and verifying concurrent systems implies a need to be able to exploit parallel and distributed computing architectures. We present a new distributed algorithm for state space exploration of concurrent systems on computing clusters. Our algorithm relies on Remote Direct Memory Access (RDMA) for low-latency transfer of states between computing elements, and on state reconstruction trees for compact representation of states on the computing elements themselves. For the distribution of states between computing elements, we propose a concept of state stealing. We have implemented our proposed algorithm using the OpenSHMEM API for RDMA and experimentally evaluated it on the Grid'5000 testbed with a set of benchmark models. The experimental results show that our algorithm scales well with the number of available computing elements, and that our state stealing mechanism generally provides a balanced workload distribution.

关键词： Computational modeling Clustering algorithms Computer architecture Benchmark testing Explosions Space exploration Low latency communication

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distributed Task Offloading for IoAV Using DDP-DQN 1

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23rd international conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Peng, Xiting Li, Wenjie Zhang, Xiaoyu Dong, Mianxiong Ota, Kaoru Song, Shun Shenyang Univ Technol Sch Informat Sci & Engn Shenyang 110870 Peoples R China Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Peoples R China Shenyang Key Lab Informat Percept & Edge Comp Shenyang Peoples R China Muroran Inst Technol Dept Informat & Elect Engn Muroran Hokkaido 0508585 Japan

ISBN: (数字)9789819708116

ISBN: (纸本)9789819708109;9789819708116

The swift progress of the Internet of Vehicles (IoV) and autonomous driving technology has facilitated the emergence of the Internet of Autonomous Vehicles (IoAV). If delay-sensitive vehicle tasks are not completed on time, it will lead to bad consequences for IoAV. Task offloading technology can solve the problem that the vehicle cannot meet the task requirements. However, highly dynamic vehicle networks and diverse vehicle applications require more intelligent task offloading strategies. Therefore, this paper addresses the distributed task offloading problem in the IoAV to meet diverse vehicle task demands. First, we model the vehicle task offloading problem as a decision problem, and a deep reinforcement learning (DRL) algorithm named DDP-DQN (double-dueling-prioritize-DQN) is applied to complete vehicle tasks more efficiently. Then, we design a reward function to complete the task within the acceptable maximum delay of the task while reducing the consumption of resources. Simulations demonstrate the outperforming of the DDP-DQN compared with other three reinforcement learning algorithms.

关键词： Internet of autonomous vehicles Task offloading Vehicle edge computing Deep reinforcement learning

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Aware: Adaptive distributed Training with Computation, Communication and Position Awareness for Deep Learning Model 24

Aware: Adaptive Distributed Training with Computation, Commu...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zeng, Yan Yi, Guangzheng Yin, Yuyu Wu, Jiyang Xue, Meiting Zhang, Jilin Wan, Jian Zhang, Yunquan School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing China

ISBN: (纸本)9798350319934

The accuracy of the neural networks can usually be improved by increasing the size of the dataset and the layers or operators of the network, as it has strong composability. But, it makes a challenge to train these models efficiently, due to the limited resources of a single device, such as memory, computational resource and so on. For this challenge, it has become an inevitable trend to automatically implement model parallelism across multiple devices. This paper proposes Aware, an adaptive distributed parallel training method, to search distributed parallel strategy automatically for deep learning model. It firstly proposes an operator fusion strategy with computation and communication awareness to simplify the computational graph of deep learning model. And then, it introduces position-aware graph embedding algorithm to extract the structural features of models, which can make the searched parallel strategies to transplant to other networks with similar structures. On this basis, we use reinforcement learning algorithm to search distributed parallel strategy automatically. This paper makes experiments with neural networks such as Inception V3, NMT, GNM, NasNet and ResNet, and uses CIFAR10 and PTB datasets to compare the two methods of Hierarchical and Placeto. The results show that compared with Placeto, Aware achieves up to 5% reductions in runtime. Aware achieves more than 8% reductions in runtime compared with Hierarchical. Moreover, it has better transplantation and generalization capabilities, and supports pre-training for large-scale network parallel strategy search and accelerates convergence. © 2022 IEEE.

关键词： Deep learning

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REVENUE FORECASTING USING distributed MACHINE LEARNING IN ONLINE ADVERTISING SYSTEMS

REVENUE FORECASTING USING DISTRIBUTED MACHINE LEARNING IN ON...

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2023 IEEE international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Murugan, P. Esakkiammal, S. Kanagavalli, N. Priya, P. Krishna Martis, Jason Elroy Kavitha, P. Pallavaram Tamilnadu Chennai India Department of Library Central University of Tamil Nadu Neelakudi Tamilnadu Thiruvarur India Department of CSE Rajalakshmi Institute of Technology Kuthambakkam Tamilnadu Chennai India KL Business School KL Education Foundation Guntur Andrha Pradesh Vaddeswaram India Department of Information Science and Engineering NMAM Institute of Technology Nitte Deemed to be University Karnataka Karkala India Department of Computer Science and Engineering R.M.K. Engineering College Tamilnadu India

ISBN: (纸本)9798350305708

This research delves into the utilization of distributed machine learning methods for revenue forecasting in the realm of online advertising systems. As the domain of digital advertising continues to expand rapidly, the accurate prediction of revenue becomes indispensable for optimizing resource allocation and making well-informed decisions. Conventional approaches frequently encounter difficulties in managing the sheer volume and intricacy of advertising data. In contrast, distributed machine learning harnesses the capabilities of parallel processing and distributed computing to efficiently process and analyze large datasets. This investigation explores diverse distributed machine learning algorithms, including gradient boosting, random forests, and neural networks, within the context of revenue forecasting. The research assesses their performance, scalability, and precision in comparison to traditional techniques. Through empirical experiments and case studies, we showcase the potential of distributed machine learning to elevate the accuracy of revenue forecasting in online advertising systems, ultimately contributing to more informed business strategies and enhanced revenue generation. © 2023 IEEE.

关键词： algorithm scalability distributed machine learning online advertising systems parallel processing revenue forecasting

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MonkeyGPT: Generative AI in Network Anomaly Detection of Video conference Applications 22

MonkeyGPT: Generative AI in Network Anomaly Detection of Vid...

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22nd IEEE international Symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Xu, Junhao He, Dongbiao Zhang, Chen Zhou, Xu Ming, Zhongxing Cui, Laizhong Sangfor Technologies Inc China Shenzhen University College of Computer Science and Software Engineering China China

ISBN: (纸本)9798331509712

The rapid advancement of generative artificial intelligence (GAI) has led to the creation of transformative applications such as ChatGPT, which significantly boosts text processing efficiency and diversifies audio, image, and video content. Beyond digital content creation, GAI's capability to analyze complex data distributions holds immense potential for next-generation networks and communications, especially given the swift rise of video conferencing applications (VCAs). This paper presents a dynamic, real-time method for detecting anomalous network links in video conferencing applications. The proposed tool, MonkeyGPT, generates tracing representations of network activity and trains a large language model from scratch to serve as a detection system based on network traffic data. Unlike traditional methods, MonkeyGPT provides an unrestricted search space and does not rely on predefined rules or patterns, enabling it to detect a wider range of anomalies. We demonstrate the effectiveness of MonkeyGPT as an anomaly detection tool in real-world VCAs. The results indicate that the model possesses strong detection capabilities, achieving an accuracy rate of over 97%. It is applicable to various platforms, including Zoom, Microsoft Teams, Tencent Meeting, and Feishu, showcasing its robust adaptability. © 2024 IEEE.

关键词： Video conferencing

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Small Reservoirs Make a Mickle: distributed Reservoir-computing based Equalization for 100 Gb/s VCSEL-enabled Optical Interconnects

Small Reservoirs Make a Mickle: Distributed Reservoir-Comput...

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Asia Communications and Photonics conference (ACP) / international conference on Information Photonics and Optical Communications (IPOC)

作者： Zhang, Songte Zhang, Wenjia He, Zuyuan Shanghai Jiao Tong Univ State Key Lab Adv Opt Commun Syst & Networks Shanghai 200240 Peoples R China

ISBN: (纸本)9781665481557

In this paper, we propose a distributed reservoir-computing based parallel nonlinear equalization for 100 Gb/s vertical cavity surface emitting laser (VCSEL) enabled optical interconnects. Equalization performance of proposed equalizer is compared with neural network and Volterra series based equalizers and similar performance can be achieved but with very neat and low computational complexity training process. Moreover, this approach, explained as small reservoirs make a mickle, is a scalable network generation solution that is promising for parallel hardware implementation.

关键词： Short reach optical interconnects reservoir computing equalization algorithm

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Regression and Classification-Based Model Predictive Maintenance of Aircrafts Using Machine Learning 7

Regression and Classification-Based Model Predictive Mainten...

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

作者： Banday, Aamir Shafi Chohan, Jasgurpreet Singh Kumar, Raman Chandigarh University Department of Mechanical Engineering Punjab Mohali India

ISBN: (纸本)9781665454018

One of the main hurdles of Industry 4.0 is to predict Aircraft failures Since downtime costs money and reduces productivity, the ability to prevent failures is essential. Knowing how many cycles or RULs remain before a breakdown occurs is crucial for Aircraftry. Where possible under the same circumstances, the RUL estimations should be based on prior observations. The development of technologies that track equipment's present state is at the heart of Evaluate the influence research.. Despite the enormous research on this subject, no one universal strategy exists. The lack of a universal method is what drives this notion, which uses recurrent neural networks (RNN) for the monitoring of the proposed approach. © 2022 IEEE.

关键词： Recurrent neural networks

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Clustering Techniques in Vehicular Adhoc networks- A Survey 7

Clustering Techniques in Vehicular Adhoc Networks- A Survey

引用

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

作者： Kaur, Gurtej Khurana, Meenu Kaur, Amandeep Chitkara University Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781665454018

The vehicular adhoc networks (VANETs) is an integral part of the intelligent transportation system required for delivering safety and non-safety messages to vehicles in real-time via vehicle to vehicle and vehicle to everything communication modes. The purpose of VANETs is to provide efficient routing, message security, congestion-free traffic, and other services related to entertainment and comfortable journey. All these VANETs services, at the core, are related to efficient and secure data dissemination. In this paper, we have focused on clustering in VANETs for efficient data dissemination and resource sharing tasks. Considering the importance of fog computing for fast computing and processing, we have focused on clustering techniques in Fog-based VANET as well that can enhance the range of transmission and expand the number of available resources. This survey paper discusses the existing clustering techniques in VANETs scenario, cluster head selection parameters, the context-based techniques, and cluster performance parameters in VANETs. © 2022 IEEE.

关键词： Cluster analysis

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ICDCN 2022 - Proceedings of the 2022 international conference on distributed computing and Networking

ICDCN 2022 - Proceedings of the 2022 International Conferenc...

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23rd international conference on distributed computing and Networking, ICDCN 2022

ISBN: (纸本)9781450395601

The proceedings contain 46 papers. The topics discussed include: undervolting on wireless sensor nodes: a critical perspective;a scalable causal broadcast that tolerates dynamics of mobile networks;robotic sorting on the grid;greedy algorithms for scheduling package delivery with multiple drones;a study on migration scheduling in distributed stream processing engines;distributed matrix tiling using a hypergraph labeling formulation;a lattice linear predicate parallel algorithm for the dynamic programming problems;a fast wait-free multi-producers single-consumer queue;limited associativity makes concurrent software caches a breeze;and topology inference of networks utilizing rooted spanning tree embeddings.

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Hebo System: Trusted Copyright Authorization in Computer networks 28

Hebo System: Trusted Copyright Authorization in Computer Net...

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28th IEEE international conference on parallel and distributed Systems, ICPADS 2022

作者： Zeng, Chen Liang, Fan Yixuan, Sun Xiaohui, Peng Institute of Computing Technology Cas Beijing China Nanjing Institute of Information Superbahn Nanjing China

ISBN: (纸本)9781665473156

The DRM (Digital Rights Management) systems protect owners' copyrights by controlling consumers' access to digital works. However, they fail to provide authorization evidence if customers use digital works on other platforms outside the DRM systems. There is no such evidence that can trustworthily be disseminated in computer networks, which results in many copyright lawsuits. To address the problem, we propose the Copyrights Authorization Model (CAM) and the Hebo system to ensure the consensus on copyright authorization in computer networks. The CAM proves that participants agree on copyright authorizations if they are traceable, integrated, and non-repudiated. Based on the CAM, we design the ledger of trusted authorization forest that keeps the three properties and independent zones that maintain the ledger. The Hebo system is composed of these zones. It can provide authorization evidence for consumers to avoid copyright disputes. Besides, it has the advantages of a beneficial locality. The TPS (transactions per second) can increase with the number of independent zones, nodes can flexibly choose ledgers according to their capability, the system reduces redundant storage, and zones allow asynchrony. Finally, we evaluate the system on five different platforms, and the average time costs of operations are less than 60 milliseconds. The TPS of a single node depends on the configuration of the hardware, which is an average of 138, 19, 24, 21, and 98 in Server, MacBook, J-Nano, Pi4B, and J-TX2, respectively. © 2023 IEEE.

关键词： Copyrights

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