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Enhancing the Performance of an Autonomous distributed Storage System in a Large-Scale Network 23

Enhancing the Performance of an Autonomous Distributed Stora...

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23rd ACIS international Summer Virtual conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing, SNPD-Summer 2022

作者： Nunome, Atsushi Hirata, Hiroaki Kyoto Institute of Technology Faculty of Information and Human Sciences Kyoto Japan

ISBN: (纸本)9798350396379

We are developing an autonomous distributed storage system that autonomously relocates data blocks according to their utilization and the effective performance of storage. This paper proposes an enhancement to the distributed storage system to address large network environments where many network nodes are connected to multiple network switches. In the proposed method, the latency time when a network frame travels on the network is evaluated together with the effective performance of the storage, and this is used to determine the destination of the data block. Simulation evaluation results show that in the networks with cascading multiple network switches and high I/O load on storage, the performance improvement is significantly greater than that of the previous method. This showed that our storage system can provide sufficient performance improvement even in large network environments. © 2022 IEEE.

关键词： Digital storage

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A Learning-Based Scheduler for High Volume Processing in Data Warehouse Using Graph Neural networks 22nd

A Learning-Based Scheduler for High Volume Processing in Dat...

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

作者： Bengre, Vivek HoseinyFarahabady, M. Reza Pivezhandi, Mohammad Zomaya, Albert Y. Jannesari, Ali Iowa State Univ Dept Comp Sci Lab Software Analyt & Pervas Parallelism SwAPP Ames IA USA Univ Sydney Ctr Distributed & High Performance Comp Sch Comp Sci Camperdown NSW Australia

ISBN: (纸本)9783030967727;9783030967710

The process of extracting, transforming, and loading (also known as ETL) of a high volume of data plays an essential role in data integration strategies in data warehouse systems in recent years. In almost all distributed ETL systems currently use in both industrial and academia context, a simple heuristic-based scheduling policy is employed. Such a heuristic policy tries to process a stream of jobs in the best-effort fashion, however, it can result in under-utilization of computing resources in most practical scenarios. On the other hand, such inefficient resource allocation strategy can result in an unwanted increase in the total completion time of data processing jobs. In this paper, we develop an efficient reinforcement learning technique that uses a Graph Neural Network (GNN) model to combine all submitted tasks graphs into a single graph to simplify the representation of the states within the environment and efficiently make a parallel application for processing of the submitted jobs. Besides, to positively augment the embedding features in each leaf node, we pass messages from leaf to root so the nodes can collaboratively represent actions within the environment. The performance results show up to 15% improvement in job completion time compared to the state-of-the-art machine learning scheduler and up to 20% enhancement compared to a tuned heuristic-based scheduler.

关键词： Extract Transform Load (ETL) operations Scheduling policy Data streaming processing system Graph neural networks Job completion time Reinforcement learning

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distributed computing of R Applications Using RBOINC Package with Applications to parallel Discrete Event Simulation 24th

Distributed Computing of R Applications Using RBOINC Packag...

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24th international conference on distributed Computer and Communication networks, DCCN 2021

作者： Astafiev, S.N. Rumyantsev, A.S. Petrozavodsk State University 33 Lenina Street Petrozavodsk Russia Institute of Applied Mathematical Research Karelian Research Centre of Russian Academy of Sciences 11 Pushkinskaya Street Petrozavodsk Russia

ISBN: (纸本)9783030971090

R programming language is commonly used for statistical computing, data science and stochastic simulation. Existing packages for R allow to run parallel code on various parallel architectures, however, the support for distributed (volunteer) computing is rather weak. This article describes a new R package RBOINC that allows to run parallel code on desktop grid systems via the BOINC open source system for grid computing, which is a promising approach for parallel stochastic simulation. Among the possible ways to utilize the new package, an approach to parallel regenerative stochastic simulation within the generalized semi-Markov processes framework is suggested. © 2022, Springer Nature Switzerland AG.

关键词： Stochastic systems

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2nd international conference on Advances in computing Research, ACR 2024

2nd International Conference on Advances in Computing Resear...

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2nd international conference on Advances in computing Research, ACR 2024

ISBN: (纸本)9783031569494

The proceedings contain 48 papers. The special focus in this conference is on Advances in computing Research. The topics include: Public Policy Decision Making: Confirmatory Factor Analysis;An Analytical Study of Traffic Accidents in Connecticut, USA Using Python;improving the Efficiency of Multimodal Approach for Chest X-Ray;wrist Crack Classification Using Deep Learning and X-Ray Imaging;improving Weeds Detection in Pastures Using Illumination Invariance Techniques;credit Card Batch Processing in Banking System;irregular Frame Rate Synchronization of Multi-camera Videos for Data-Driven Animal Behavior Detection;increasing the Accuracy of a Deep Learning Model for Traffic Accident Severity Prediction by Adding a Temporal Category;2ARTs: A Platform for Exercise Prescriptions in Cardiac Recovery Patients;understanding Consumers Attitudes Towards Sustainability;Enriching Ontology with Named Entity Recognition (NER) Integration;data Transfer Methods and Strategies: Unified Replication Model Using Trees;optimized Vehicle Repair Cost by Means of Smart Repair Distribution Model;classification of Eye Disorders Using Deep Learning and Machine Learning Models;Effects of parallel and distributed Learning on CNN Performance for Lung Disease Classification;a Federated Learning Anomaly Detection Approach for IoT Environments;Taxonomy of AR to Visualize Laparoscopy During Abdominal Surgery;open Platform Infrastructure for Industrial Control Systems Security;Towards Hybrid NIDS: Combining Rule-Based SIEM with AI-Based Intrusion Detectors;security Challenges and Solutions in Smart Cities;cloudSec: An Extensible Automated Reasoning Framework for Cloud Security Policies;optimizing Energy Consumption for IoT Adaptive Security: A Mobility-Based Solution;cyber Edge: Mitigating Cyber-Attacks in Edge computing Using Intrusion Detection System;A New Security Mechanism for IoT Devices: Electroencephalogram (EEG) Signals;an Assessment of the Cyber Security Challenges and Issues Associated wi

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Research on Big Data distributed computing Model and Algorithm Optimization for Complex Network Analysis

Research on Big Data Distributed Computing Model and Algorit...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), international conference on

作者： Aiting Sun Liaoning Vocational College of light industry Liaoning China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the advent of the era of big data, the traditional stand-alone computing model can no longer meet the needs of complex network analysis, and the distributed computing model provides a new solution. This paper first introduces the importance of complex network analysis and the challenges brought by big data, then discusses the application of distributed computing model in complex network analysis in detail, and focuses on the distributed computing algorithm based on MapReduce. Then, this paper puts forward an optimization strategy of distributed community detection algorithm based on modularity optimization. This strategy performs community detection tasks in parallel through MapReduce framework, and optimizes community division through iterative updating to maximize modularity. The experimental results show that the optimized algorithm has a significant improvement in computing time, accuracy and resource consumption. The distributed computing model based on MapReduce framework and its optimization strategy have obvious advantages in dealing with large-scale complex networks. It can not only improve the calculation efficiency and accuracy, but also reduce the resource consumption, which provides an efficient and feasible solution for complex network analysis.

关键词： Analytical models Accuracy Computational modeling Memory management Complex networks Big Data Data models distributed computing Detection algorithms Optimization

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Problem Specific Communications in distributed Quantum computing

Problem Specific Communications in Distributed Quantum Compu...

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Quantum Communications, Networking, and computing (QCNC), international conference on

作者： Joyanta Basak Bing Wang Sanguthevar Rajasekaran School of Computing University of Connecticut USA

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

When all the qubits needed for solving a problem are not located in a single quantum computer, qubits from different quantum computers can be collectively utilized. In this case, quantum communication is needed for the multiple quantum computers to communicate with each other. Several studies address the problem of minimizing the number of quantum communications when evaluating a general quantum circuit. The solutions proposed typically involve solving some intractable problems. In this paper, we show that we can obtain much better solutions when we focus on solving specific problems (instead of seeking solutions for generic circuits). Specifically, we consider several fundamental quantum circuits and identify communication protocols that need a much smaller number of communication steps than those offered by generic solutions. Our work is in line with traditional parallel and distributed computing research where typically scientists focus on solving specific problems (such as sorting, matrix multiplication, network flow, etc.) in a parallel or distributed setting.

关键词： Quantum computing Protocols Source coding Qubit Search problems Complexity theory Quantum networks Quantum circuit parallel algorithms Sorting

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Improved memory performance through the development of an energy efficient distributed memory Management System

Improved memory performance through the development of an en...

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2022 international conference on Machine Learning, Big Data, Cloud and parallel computing, COM-IT-CON 2022

作者： Sundari, K.Siva Narmadha, R. Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

In multiprocessor networks, shared distributed memory is a popular research subject. It has been studied by scientists and engineers from a variety of fields. To construct high-performance, large-scale multiprocessor systems, it is necessary to maximise distributed shared memory (SDM) performance. SDM algorithm, locking shared space, thrashing, concurrent access, page faults, extension, transparency, huge database support and cost have all been the subject of extensive investigation in the past. Memory structure has a substantial impact on the system's power consumption and performance;hence a low-power system is needed. There are a number of problems with the design of shared and private memory systems for multiple processors that this dissertation seeks to address. Using software parameters, these researchers developed a new distributed shared memory design that outperforms conventional structural design in managing a distributed shared address space. The primary goal of this study is to use a software method to create novel distributed shared memory architecture. © 2022 IEEE.

关键词： Structural design

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Attention Based Generative Adversarial networks to Augment Image Data for Rapid Training

Attention Based Generative Adversarial Networks to Augment I...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Vijayakumar Adaickalam Sadagopan Selvarasu Gowthul Alam M A Suresh Kumar S Annamalai Sangeetha N School of Computer Science and Engineering and Information Presidency University Bangalore Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai School of Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Department of AI & ML City Engineering College Bangalore

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In this paper, we develop an Attention based Generative Adversarial networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enables generation of images from the sample images that helps in training the classifier and in this study a fundamental classifier namely a convolutional neural network is used. A robust training is conducted to test the accuracy of detecting the instances effectively using the dataset. The simulation shows that the proposed AGAN-CNN attains improved accuracy after robust training than the existing methods.

关键词： Training Accuracy Training data Imaging Production Computer architecture Generative adversarial networks Grid computing Convolutional neural networks Hyperspectral imaging

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Hyperverse: A High Throughput Pattern Matching Engine for Metaverse 42

Hyperverse: A High Throughput Pattern Matching Engine for Me...

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42nd IEEE international conference on distributed computing Systems (ICDCS)

作者： Zhu, Wenjun Chang, Harry Hong, Yang Wang, Xiang Langdale, Geoff Qiu, Kun Zhang, Mingyi Intel Corp Beijing Peoples R China

ISBN: (数字)9781665488792

ISBN: (纸本)9781665488792

Cyberspace has continued to evolve since the Internet became widespread in the 1990s. A variety of computer-mediated virtual environments have been created, including social networks, video conferencing, virtual 3D worlds (e.g., VR chat), augmented reality applications (e.g., Ingress), and non-fungible token games. Such virtual environments, while not permanent and incoherent, have brought us varying degrees of digital transformation. The term "metaverse" was devised to further facilitate the digital transformation of all aspects of our physical lives. With the increasing number of devices and users such as IoT, augmented reality and virtual reality glass connected to the metaverse, huge amounts of data need to be filtered or captured for metaverse security or user behavior analysis by utilizing pattern matching. However, directly utilizing the existing pattern matching engine is impossible since it cannot achieve the throughput that is required by the metaverse, where low throughput could cause poor user experiences. Thus, in this paper. we propose a new pattern matching design called Hyperverse. Hyperverse can significantly increase the throughput of pattern matching by designing a new algorithm that is based on instruction-level parallelism. We implement Hyperverse in Hyperscan. which is the fastest regular expression engine in the world. Compared with the existing solution, Hyperverse can achieve a throughput of up to 10.1Gbps per core, which is a 3.83x boost than the existing solution. Thus, the significantly increased throughput will prevent a negative impact on the user experience in the metaverse.

关键词： Metaverse Digital transformation parallel processing Throughput User experience Security Pattern matching

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MACC: A Heterogeneous Multi-Agent Mobile Edge Coding Caching Strategy for Reducing Traffic Load 29

MACC: A Heterogeneous Multi-Agent Mobile Edge Coding Caching...

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

作者： Yuan, Xuemei Chen, Ningjiang Guangxi University School of Computer and Electronic Information Nanning China

ISBN: (纸本)9798350330717

The rapid development of industrial IoT communication and the wide application of smart terminal devices, large-scale mobile data traffic and frequent data requests pose challenges to resource-constrained wireless communication networks. In order to reduce the network load, the introduction of content encoding caching to the network edge is considered an effective solution. Due to the heterogeneity of the resources of the mobile edge system, the variability of user requests, and the mobility of users, the coded caching strategy at the mobile edge needs to be continuously optimized. However, existing articles do not adequately investigate how to intelligently update the coded caching policy in dynamic environments. For this reason, this paper proposes a heterogeneous multi-agent MDS coded caching scheme (MACC) based on value decomposition, which considers the edge caching server with storage capacity and the mobile user as two different types of agents and improves the caching policy through interaction. In addition, considering the heterogeneous preference of heterogeneous multi-agent and the fact that an increase in the number may lead to a sharp increase in the training dimension, the idea of a value decomposition network is introduced in the training learning of the coded caching scheme. Simulation experiments verify that the proposed MACC can effectively reduce the load on the forward link and achieve a higher cache hit rate. © 2023 IEEE.

关键词： Coding Cache Cooperative Caching Mobile Edge computing Multi-Agent Reinforcement Learning

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