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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

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Enhanced Slicing based Deep Learning architecture for 5G networks in distributed Learning 15

Enhanced Slicing based Deep Learning architecture for 5G net...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dadhich, Atul Shankar, Amar Kumari, Aarsi Department of Electrical Engineering Vivekananda Global University Jaipur India Karnataka Bangalore India Department of Computer Science & IT ARKA JAIN University Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

This paper gives a distributed deep getting-to-know (DDL) architecture for 5G community cutting. DDL is an allotted method which has been a success in schooling massive-scale deep mastering fashions. It applies the ideas of disbursed computing to reduce the dimensions of the statistics being trained at the same time as education samples in parallel on different computing assets. The proposed solution in this paper exploits the improvements in 5G networks' virtualization skills, which include community cutting, to provide a green architecture which allows DDL fashions to be taught with dispensed resources to limit the training time. The proposed structure consists of three predominant modules: a useful resource-cutting manager, a dispensed training cluster, and a dispensed inference cluster. The resource-reducing manager allows dynamic aid cutting throughout numerous assets (e.g. GPU, CPU). The distributed schooling cluster includes workers distributing the training duties across the sources. The allotted inference cluster is used to perform inference on the trained fashions throughout more than one allotted computing source. The advantages of the proposed structure are that it allows green disbursed schooling, green disbursed inference, and value-green education of complex deep gaining knowledge of models. As such, it may be used to educate huge and complex models with decreased prices and also boost the schooling throughput. Additionally, 5G community cutting presents flexibility in useful resource provision. ©2024 IEEE.

关键词： 5G mobile communication systems

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GeckoDAG: Towards a Lightweight DAG-based Blockchain via Reducing Data Redundancy 43

GeckoDAG: Towards a Lightweight DAG-based Blockchain via Red...

引用

43rd IEEE international conference on distributed computing Systems (ICDCS)

作者： Dai, Xiaohai Zhou, Yifan Xiao, Jiang Cheng, Feng Xie, Xia Jin, Hai Li, Bo Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Wuhan Peoples R China Huazhong Univ Sci & Technol Cluster & Grid Comp Lab Serv Comp Technol & Syst Lab Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou Hainan Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9798350339864

To overcome the scaling and performance limitations, the Directed Acyclic Graph (DAG) is utilized as the underlying storage model of blockchain systems, which enables concurrent transaction processing and confirmation. However, accompanied by high performance, DAG-based blockchains still suffer from the severe challenge of constrained storage scalability, i.e., expensive storage overhead. Based on an in-depth analysis of the data, we discover that the root cause of storage overhead stems from the considerable data redundancy in the DAG-based blockchains. In this paper, we propose GeckoDAG, a lightweight DAG-based blockchain, whose design consists of two steps. First, we abstract a storage model named Basic from the existing DAG-based blockchain systems, which offers both high performance and security. On top of Basic, we then devise GeckoDAG, which merges previous transactions into Transaction Union (TU) and reduces the data redundancy in TU, thus lowering the storage overhead. To evaluate our design, we implement a prototype of GeckoDAG and conduct various experiments on it. The experimental results demonstrate that GeckoDAG can offer storage scalability while maintaining the security and efficiency of DAG-based blockchains.

关键词： Blockchain Directed Acyclic Graph Lightweight Redundancy Storage scalability

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Dynamic Checkpointing: Fault Tolerance in High-Performance computing 3

Dynamic Checkpointing: Fault Tolerance in High-Performance C...

引用

3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Bhowmik, Biswajit Verma, Tushar Dineshbhai, Navdeep Dadhania Reddy, Manideep Raya Vsrs Girish, K.K. National Institute of Technology Karnataka Maharshi Sushrut Cas Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Kanad Qc Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331528201

parallel computing has become a cornerstone of modern computational systems, enabling the rapid processing of complex tasks by utilizing multiple processors simultaneously. However, the efficiency and reliability of these systems can be significantly compromised by inherent challenges such as hardware failures, communication delays, and uneven workload distribution. These issues not only slow down computations but also threaten the dependability of applications reliant on parallel processing. To address these challenges, researchers have developed strategies like dynamic checkpointing and load balancing, which are crucial for enhancing fault tolerance and optimizing performance. Dynamic checkpointing periodically saves the computational state, allowing for recovery from failures without significant data loss, while load balancing ensures that tasks are evenly distributed across processors, preventing bottlenecks and underutilization of resources. By integrating these mechanisms, this paper proposes a robust framework that improves the reliability and efficiency of parallel systems, particularly in high-performance computing environments where the ability to handle large-scale data processing with minimal downtime is critical. © 2024 IEEE.

关键词： parallel processing systems

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Challenges and Solutions for distributed File System 2

Challenges and Solutions for Distributed File System

引用

2nd international conference on Disruptive Technologies, ICDT 2024

作者： Gupta, Richa Gupta, Amit Bhatla, Shruti Tripathi, Vikas Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350371055

A distributed File System (DFS) is a file system that allows multiple users to access and share files and data across a network. In a DFS, files are stored on multiple servers, and users can access them from any location on the network as if they were stored on their local computer. DFS provides a way to organize and manage files and folders across multiple servers in a unified way, making it easier for users to access and share data without worrying about the physical location of the files. This can help to improve data availability, reliability, and scalability in a networked environment. DFS also provides a way to replicate data across multiple servers for redundancy and fault tolerance, so that if one server fails, the data can still be accessed from other servers. Some examples of popular distributed file systems include Hadoop distributed File System (HDFS), Google File System (GFS), and Windows distributed File system (DFS). LCCFS (light weight cloud computing file system) which help to reduce the overhead and increase the performance of the distributed system RADOS (Reliable autonomic distributed object storage) which utilized for implementing distributed file system for cloud computing Pangea is a new system which allow different units to combine into a single unit and PLSF (Virtual parallel log structured file system) which pick up the performance by improving the bandwidth are some new terminologies introduced by some previous research are discussed in this research. © 2024 IEEE.

关键词： Ceph Cloud file system distributed File System Gluster HDFS

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Research on Smart grid Information Security System Based on Computer GIS Wide Area Information Big Data

Research on Smart Grid Information Security System Based on ...

引用

2023 international conference on Internet of Things, Robotics and distributed computing, ICIRDC 2023

作者： Lu, Xiaofen Wang, Zhiyong Liu, Fuyan Wang, Yuhang Economic Research Institute of State Grid Zhejiang Electric Power Company Hangzhou China State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China Zhejiang Huayun Information Technology Co. Ltd. Hangzhou China

ISBN: (纸本)9798350375275

This paper proposes a smart grid information security system based on GIS wide area information and domestic SM2 cryptographic system. The problems of SM2 algorithm in security platform are studied and a component-based cryptographic channel construction method is proposed. DS18B20 is used to measure the electric energy and extract the clock signal to collect the electric energy signal. In order to ensure the safety of the pulse power supply, the voltage and current of the power supply are protected. Through video connection to achieve monitoring interaction and collaboration, so as to achieve remote control. Use the SM2 password security element to determine user access to resources. The results show that the platform can realize seamless integration among the applications of power secondary system and form an organic whole among them. In this way, the centralized management and control of user identity and access rights are realized. © 2023 IEEE.

关键词： Smart power grids

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Research on distributed Multi-Task Learning System Based on Data Analysis Algorithm 1

Research on Distributed Multi-Task Learning System Based on ...

引用

1st IEEE international conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Liu, Xin Wang, Yanjun Wang, Qiaoli Ma, Jun Cao, Jinping State Grid Xinjiang Electric Power Co. Ltd. Xinjiang Urumqi830000 China Information and Telecommunication Company State Grid Xinjiang Electric Power Co. Ltd. Xinjiang Urumqi830000 China

ISBN: (纸本)9798350323795

This paper presents a research study on distributed multi-task learning systems based on data analysis algorithms. The paper starts by providing an introduction to distributed multi-task learning and data analysis algorithms, along with their importance in machine learning and artificial intelligence applications. The background section provides a more detailed discussion of these concepts, highlighting their specific applications and challenges. The paper then focuses on data analysis algorithms, discussing their types, features, and usage in different applications, including distributed multi-task learning. The paper also highlights some of the popular data analysis algorithms, such as clustering, classification, regression, and dimensionality reduction, along with their mathematical formulations and applications. The section on distributed multi-task learning provides an overview of its concepts, benefits, and challenges. The paper then discusses some of the popular algorithms used in distributed multi-task learning, such as Bayesian methods, transfer learning, and multi-task feature learning, along with their mathematical formulations and applications. The paper concludes by discussing some of the recent research trends in this field, including the use of deep learning and reinforcement learning in distributed multi-task learning systems. Overall, this paper provides a comprehensive overview of the distributed multi-task learning system based on data analysis algorithms, highlighting its importance and potential in various applications. © 2023 IEEE.

关键词： Deep learning

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Atlas: Hierarchical Partitioning for Quantum Circuit Simulation on GPUs 24

Atlas: Hierarchical Partitioning for Quantum Circuit Simulat...

引用

2024 international conference for High Performance computing, Networking, Storage and Analysis

作者： Xu, Mingkuan Cao, Shiyi Miao, Xupeng Acar, Umut A. Jia, Zhihao Carnegie Mellon Univ Comp Sci Dept Pittsburgh PA 15213 USA Univ Calif Berkeley Dept EECS Berkeley CA USA

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

This paper presents techniques for theoretically and practically efficient and scalable Schrodinger-style quantum circuit simulation. Our approach partitions a quantum circuit into a hierarchy of subcircuits and simulates the subcircuits on multi-node GPUs, exploiting available data parallelism while minimizing communication costs. To minimize communication costs, we formulate an Integer Linear Program that rewards simulation of "nearby" gates on "nearby" GPUs. To maximize throughput, we use a dynamic programming algorithm to compute the subcircuit simulated by each kernel at a GPU. We realize these techniques in Atlas, a distributed, multi-GPU quantum circuit simulator. Our evaluation on a variety of quantum circuits shows that Atlas outperforms state-of-the-art GPU-based simulators by more than 2x on average and is able to run larger circuits via offloading to DRAM, outperforming other large-circuit simulators by two orders of magnitude.

关键词： parallel programming quantum simulation

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Privacy Preservation in Quantitative Association Rule Mining Using Fuzzy Logic-Based Sanitization

Privacy Preservation in Quantitative Association Rule Mining...

引用

international conference on parallel distributed and grid computing (PDGC)

作者： Yadhveer R M Geetha Mary Amalanathan School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In the present world order, where the use of artificial intelligence and machine learning has been predominant in various industries, information and data are of prime importance. Various algorithms have made it possible to analyze large databases and provide insights for making strategic decisions. One such tool is data mining, where relations between items in transactional databases can be retrieved. Such relations can have great value in the proper situation, providing immense value to the observer. In contrast, some associations can also be deemed sensitive by the data publishers and hence, have to be sanitized before being made public. This is where PPDM (Privacy Preserving Data Mining) comes into play. The below research presents a way to sanitize quantitative databases based on fuzzy logic.

关键词： Fuzzy logic Industries Data privacy Machine learning algorithms distributed databases Observers grid computing Association rule learning Round robin Indexing

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Spark distributed System and GPU parallel computing

Spark Distributed System and GPU Parallel Computing

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2022 international conference on Electronic Information Engineering and Computer Science, EIECS 2022

作者： Chang, Dajun Qiao, Zhangquan Zheng, Qi Li, Li College of Computer Science and Technology Changchun University of Science and Technology Jilin Changchun130022 China

ISBN: (纸本)9781510663312

This study mainly uses virtual reality technology (Virtual Reality, VR) to create the basic theoretical knowledge and analysis of real-time data processing and GPU parallel computing in Spark+GPU heterogeneous environment. The quadratic programming algorithm and time sliding window algorithm are analyzed in detail from theory to practical application, and in the Spark+GPU cluster environment built in the virtual environment, the feasibility of the above algorithm is verified by real-time data processing, GPU scheduling and parallel computing. Through the numerical experiments of test functions and practical engineering applications, the GPU optimization after interval segmentation is more powerful than CPU, the algorithm is sensitive, and the global optimization of surface model provides a new solution. © 2023 SPIE.

关键词： Graphics processing unit

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Design and Implementation Of Cloud computing System for Station Networks 400KV 5

Design and Implementation Of Cloud Computing System for Stat...

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5th international conference on Information Technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Abdul-Rahaim, Laith Khaleel, Hamid Khudair University Of Babylon Dept. Of Electrical Engineering College Of Engineering Hilla Iraq

ISBN: (纸本)9798350335118

The benefits of computing to systems that can be integrated with computers due to their multitasking ability are being improved by distributed and cloud computing developments. This is already the case (SG) when talking about the Smart grid. Previously, the data of the 400kV network was sent to the National Control Center through a remote terminal unit (RTU), and the process of storing information is limited. In the event of an expansion of the 400kV network, this requires replacing a complete system and a small storage capacity. This improvement can boost energy management system efficiency in the production, transmission, and distribution of power. Since electricity has essentially replaced all other forms of energy on the planet, it is only natural that every country, even emerging ones like Iraq, seeks to constantly improve its ability to produce electrical energy by integrating network and computing infrastructure. The use of cloud computing technology for smart grid management is discussed in this article, including parameters of the grid on the cloud system, and seeks to lower investment costs of data storage devices for the power sector to enable operation may push parameters or update the data of the grid on the cloud system in a flexible and highly secure manner. The use of cloud computing to store and transmit data from *** in the Middle Euphrates power-producing plants to the National Control Center is also covered in this article. © 2023 IEEE.

关键词： Relay protection

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