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Comparative Analysis of Various Techniques used for Selfish Node Detection in DTN 7

Comparative Analysis of Various Techniques used for Selfish ...

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

作者： Sharma, Rakhi Dinkar, Shail Kumar Veer Madho Singh Bhandari Uttarakhand Technical University Uttarkhand Dehradun India Govind Ballabh Pant Institute of Engineering and Technology Department of Computer Science and Applications Uttarakhand Garhwal India

ISBN: (纸本)9781665454018

Delay Tolerant Network is a network which is wireless and which has no infrastructure. It is designed in a way that it can operate effectively even in extreme diverse conditions and can cover large distances, as in the case of space communications when there is not a single direct pathway between source and destination. Messages are delivered based on store, carry and forward Policy. Data is transmitted in DTN through the number of intermediate nodes but some of them are malicious that are called selfish nodes. A selfish node can be described as a node which do not responds to any of the received messages in order to save its energy. There are many terms coined for this such as selfish node attack, byzantine attack, black hole attack and denial of service attack. An analysis of the techniques proposed to detect selfish behavior in DTN in the perspective of simulation tools used has been presented in this paper. © 2022 IEEE.

关键词： Delay tolerant networks

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A Global Task Scheduling Method Based on Network Measurement and Prediction in computing Power networks

A Global Task Scheduling Method Based on Network Measurement...

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international conference on parallel and distributed Systems (ICPADS)

作者： Chuanfu Zhang Wen Li Jing Chen Jing Ge Di Wang Na Li Hao Sun Yudong Geng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

To improve the task scheduling efficiency in computing power networks, this paper proposes a global task scheduling method based on network measurement and prediction in computing power networks (GTS-MP), which selects a more suitable computing power cluster and storage platform with a higher network availability score between them to implement tasks. The experiments are conducted on a test bench of urban area networks in 12 cities. The results show that this method greatly reduces the execution time of artificial intelligence model training tasks, with a maximum reduction of 40% compared to other plans.

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Dynamic Tracing of DoS Attack Over Software-Defined networks Using Machine Learning 7

Dynamic Tracing of DoS Attack Over Software-Defined Networks...

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

作者： Chopra, Amit Verma, Dinesh Chander PEC-CCE Chandigarh India Panipat Institute of Engineering and Technology Department of Computer Applications Panipat India

ISBN: (纸本)9781665454018

In this paper, a machine learning approach to detect the Denial-of-Service attack over software-defined networks (SDN) is introduced. It builds the training model using NS-3 traces to detect the Denial-of-Service (DoS) attack that can be launched by an intruder to block access to network resources. This work represents a machine learning approach to detect and prevent the SDN from DoS threat by examining the abnormalities in a given traffic pattern using classifiers. A machine learning model is developed based on the dynamic tracing of log files and traffic classification has been carried out to identify the DoS threat. A dynamic trace log is maintained to filter out the identified nodes from the SDN. Various classifiers viz K-Nearest Neighbors (KNN), Decision tree (DT), Logistic regression(LG), and Support Vector Machine (SVM) are used for traffic classification and their performance is also measured using various parameters Precision, Recall, F1-score, and Accuracy. SDN performance is also analyzed using different simulation scenarios with respect to node densities 50, 100, and 200. © 2022 IEEE.

关键词： Network security

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Proceedings of the 2022 ACM SIGSIM international conference on Principles of Advanced Discrete Simulation, ACM SIGSIM PADS 2022

Proceedings of the 2022 ACM SIGSIM International Conference ...

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2022 ACM SIGSIM international conference on Principles of Advanced Discrete Simulation, SIGSIM PADS 2022

ISBN: (纸本)9781450392617

The proceedings contain 25 papers. The topics discussed include: temporally synchronized emulation of devices with simulation of networks;adaptive partitioning for distributed multi-agent simulations;addressing white-box modeling and simulation challenges in parallel computing;explainable artificial intelligence: requirements for explainability;data assimilation of opinion dynamics based on particle filter;towards an open repository for reproducible performance comparison of parallel and distributed discrete-event simulators;a data-driven approach for pedestrian intention prediction in large public places;integrating I/O time to virtual time system for high fidelity container-based network emulation;effective simulation of on-demand services;simulation of entanglement generation and distribution - towards practical quantum networks;and formal methods for modeling and simulation of emergent behavior in complex adaptive systems.

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Performance Comparison of Multi-layer Perceptron Training on Electrical and Optical Network-on-Chips 22nd

Performance Comparison of Multi-layer Perceptron Training on...

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22nd international conference on parallel and distributed computing, Applications and Technologies (PDCAT 2021)

作者： Dai, Fei Chen, Yawen Huang, Zhiyi Zhang, Haibo Univ Otago Dunedin New Zealand

ISBN: (纸本)9783030967727;9783030967710

Multi-layer Perceptron (MLP) is a class of Artificial Neural networks widely used in regression, classification, and prediction. To accelerate the training of MLP, more cores can be used for parallel computing on many-core systems. With the increasing number of cores, interconnection of cores has a pivotal role in accelerating MLP training. Currently, the chip-scale interconnection can either use electrical signals or optical signals for data transmission among cores. The former one is known as Electrical Network-on-Chip (ENoC) and the latter one is known as Optical Network-on-Chip (ONoC). Due to the differences of optical and electrical characteristics, the performance and energy consumption of MLP training on ONoC and ENoC can be very different. Therefore, comparing the performance and energy consumption between ENoC and ONoC for MLP training is worthy of study. In this paper, we first compare the differences between ONoC and ENoC based on a parallel MLP training method. Then, we formulate their performance model by analyzing communication and computation time. Furthermore, the energy model is formulated according to their static energy and dynamic energy consumption. Finally, we conduct extensive simulations to compare the performance and energy consumption between ONoC and ENoC. Results show that compared with ENoC, the MLP training time of ONoC is reduced by 70.12% on average and the energy consumption of ONoC is reduced by 48.36% under batch size 32. However, with a small number of cores in MLP training, ENoC consumes less energy than ONoC.

关键词： Multi-layer perceptron Optical network-on-chip Artificial Neural networks Energy consumption

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Reinforcement Learning based Matching for parallel Computation Offloading in Dynamic Fog computing networks

Reinforcement Learning based Matching for Parallel Computati...

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international conference on Smart computing (SMARTCOMP)

作者： Hoa Tran-Dang Dong-Seong Kim Kumoh National Institute of Technology South Korea

ISBN: (数字)9798350349948

ISBN: (纸本)9798350349955

Matching theory has been efficiently applied in fog computing networks (FCNs) to design distributed task offloading algorithms in the presence of selfishness and rationals of fog nodes. Given the dynamic nature of fog computing environment, it is challenging to obtain the stable matching since the preference relations of two sides of matching game is unknown a prior. To address this challenge, this paper proposes RL-MATCH, a framework for parallel computation offloading in dynamic fog computing networks (FCNs). RL-MATCH is based on the matching theory and Thompson Sampling (TS) empowered Multi-Armed Bandit (MAB) learning to deal with the inherent challenges allowing task nodes with needed computation tasks to estimate the informed preference relations of helper nodes with available computing resource quickly and accurately. Extensive simulation results demonstrate the potential advantages of the TS based learning over the $\epsilon$ -greedy and upper confidence bound (UCB) based baselines.

关键词： Simulation Heuristic algorithms Computational modeling Reinforcement learning Games Delays Task analysis

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Lobster: Load Balance-Aware I/O for distributed DNN Training 51

Lobster: Load Balance-Aware I/O for Distributed DNN Training

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51st international conference on parallel Processing (ICPP)

作者： Liu, Jie Nicolae, Bogdan Li, Dong Univ Calif Merced Merced CA 95343 USA Argonne Natl Lab Argonne IL USA

ISBN: (纸本)9781450397339

The resource-hungry and time-consuming process of training Deep Neural networks (DNNs) can be accelerated by optimizing and/or scaling computations on accelerators such as GPUs. However, the loading and pre-processing of training samples then often emerges as a new bottleneck. This data loading process engages a complex pipeline that extends from the sampling of training data on external storage to delivery of those data to GPUs, and that comprises not only expensive I/O operations but also decoding, shuffling, batching, augmentation, and other operations. We propose in this paper a new holistic approach to data loading that addresses three challenges not sufficiently addressed by other methods: I/O load imbalances among the GPUs on a node;rigid resource allocations to data loading and data preprocessing steps, which lead to idle resources and bottlenecks;and limited efficiency of caching strategies based on pre-fetching due to eviction of training samples needed soon at the expense of those needed later. We first present a study of key bottlenecks observed as training samples flow through the data loading and preprocessing pipeline. Then, we describe Lobster, a data loading runtime that uses performance modeling and advanced heuristics to combine flexible thread management with optimized eviction for distributed caching in order to mitigate I/O overheads and load imbalances. Experiments with a range of models and datasets showthat the Lobster approach reduces both I/O overheads and end-to-end training times by up to 1.5x compared with state-of-the-art approaches.

关键词： Loading

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MACSQ: Massively Accelerated DeepQ Learning on GPUs Using On-the-fly State Construction 22nd

MACSQ: Massively Accelerated DeepQ Learning on GPUs Using On...

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22nd international conference on parallel and distributed computing, Applications and Technologies (PDCAT 2021)

作者： Koester, Marcel Gross, Julian Krueger, Antonio German Res Ctr Artificial Intelligence DFKI Saarland Informat CampusCampus D3-2 D-66123 Saarbrucken Germany

ISBN: (纸本)9783030967727;9783030967710

The current trend of using artificial neural networks to solve computationally intensive problems is omnipresent. In this scope, DeepQ learning is a common choice for agent-based problems. DeepQ combines the concept of Q-Learning with (deep) neural networks to learn different Q-values/matrices based on environmental conditions. Unfortunately, DeepQ learning requires hundreds of thousands of iterations/Q-samples that must be generated and learned for large-scale problems. Gathering data sets for such challenging tasks is extremely time consuming and requires large data-storage containers. Consequently, a common solution is the automatic generation of input samples for agent-based DeepQ networks. However, a usual workflow is to create the samples separately from the training process in either a (set of) pre-processing step(s) or interleaved with the training process. This requires the input Q-samples to be materialized in order to be fed into the training step of the attached neural network. In this paper, we propose a new GPU-focussed method for on-the-fly generation of training samples tightly coupled with the training process itself. This allows us to skip the materialization process of all samples (e.g. avoid dumping them disk), as they are (re)constructed when needed. Our method significantly outperforms usual workflows that generate the input samples on the CPU in terms of runtime performance and memory/storage consumption.

关键词： Massively-parallel processing Neural networks Q-learning Graphics processing units GPUs State construction

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HP3C 2022 - 2022 6th international conference on High Performance Compilation, computing and Communications

HP3C 2022 - 2022 6th International Conference on High Perfor...

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6th international conference on High Performance Compilation, computing and Communications, HP3C 2022

ISBN: (纸本)9781450396295

The proceedings contain 34 papers. The topics discussed include: large-scale parallel exact diagonalization algorithm of the Hubbard model on Tianhe-2 supercomputer;reinforcement learning enabled throughput optimization for interconnection networks of interposer-based system;a node selection scheme for data repair using erasure code in distributed storage system;research on recognition method of textual implication;research on used car valuation problem based on model fusion;an efficient parallel architecture for convolutional neural networks accelerator on FPGAs;performance optimization of sparse deep neural networks based on GPU;a quantum group signature based on quantum walk in d dimensions;parallel performance and optimization of the lattice Boltzmann method software Palabos using CUDA;secure mechanism of intelligent urban railway cloud platform based on zero-trust security;and a digital signature virtual platform based on hardware-software co-design.

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18th international conference on distributed computing and Intelligent Technology, ICDCIT 2022

18th International Conference on Distributed Computing and I...

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18th international conference on distributed computing and Intelligent Technology, ICDCIT 2022

ISBN: (纸本)9783030948757

The proceedings contain 19 papers. The special focus in this conference is on distributed computing and Intelligent Technology. The topics include: An Introduction to Graph Neural networks from a distributed computing Perspective;Towards Temporally Uncertain Explainable AI Planning;the Impact of Synchronization in parallel Stochastic Gradient Descent;a distributed Algorithm for Constructing an Independent Dominating Set;An Approach to Cost Minimization with EC2 Spot Instances Using VM Based Migration Policy;transforming Medical Resource Utilization Process to Verifiable Timed Automata Models in Cyber-Physical Systems;An SDN Implemented Adaptive Load Balancing Scheme for Mobile networks;rewriting Logic and Petri Nets: A Natural Model for Reconfigurable distributed Systems;preface;Modern AI/ML Methods for Healthcare: Opportunities and Challenges;MCDPS: An Improved Global Scheduling Algorithm for Multiprocessor Mixed-Criticality Systems;replication Based Fault Tolerance Approach for Cloud;A Comparative Study on MFCC and Fundamental Frequency Based Speech Emotion Classification;efficient Traffic Routing in Smart Cities to Minimize Evacuation Time During Disasters;early Detection of Parkinson’s Disease as a Pre-diagnosis Tool Using Various Classification Techniques on Vocal Features;extracting Emotion Quotient of Viral Information Over Twitter;a Novel Modified Harmonic Mean Combined with Cohesion Score for Multi-document Summarization.

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