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A Survey of RDMA distributed Storage 24

A Survey of RDMA Distributed Storage

引用

5th international conference on computing, Networks and internet of things (CNIOT)

作者： Wang, Ziqi Liu, Yaping Zhang, Shuo Hu, Jinrui Liu, Xinyi Guangzhou Univ Sch Cyberspace Inst Adv Technol Guangzhou Peoples R China Peng Cheng Lab Shenzhen Shenzhen 518000 Peoples R China

ISBN: (纸本)9798400716751

With the evolution of networking technology, RDMA (Remote Direct Memory Access) has become an increasingly critical component in data centers. Its zero-copy and kernel-bypass capabilities significantly boost network transmission speeds and reduce communication latency, leading to software layers such as drivers and libraries becoming the new bottlenecks. distributed storage also plays a vital role in data centers, and integrating RDMA into traditional distributed storage clusters presents a promising direction. However, distributed storage involves complex cluster designs, like hash index, making the deployment and effective utilization of features of RDMA a substantial challenge. Researchers must invest considerable effort to thoroughly understand these complexities. this paper organizes the principles of RDMA and its relation to distributed storage and challenges faced. We summarize recent research in the deployment of RDMA, covering aspects such as RDMA-based distributed file systems, distributed key-value storage systems, and compute offload using smart NICs. Finally, we look ahead at the future research of RDMA in distributed storage, offering direction and insight for ongoing and future research in this evolving field.

关键词： RDMA distributed Storage Datacenter

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the Application of Edge computing in the Operation Control of Integrated Energy Systems 5

The Application of Edge Computing in the Operation Control o...

引用

5th international conference on Clean Energy and Electric Power Engineering, ICCEPE 2024

作者： Yang, Yuqiang Li, Lei Zhao, Qiming Lu, Pengfei State Grid Zhejiang Electric Power Company LTD. Marketing Department Hangzhou China

ISBN: (纸本)9798350390315

With the development of new technologies such as big data, cloud computing, internet of things, mobile internet, and artificial intelligence, the connotation of integrated energy services continues to expand. Integrated energy system services have many management objects, handle a large amount of data, and have high privacy and security requirements. the traditional centralized cloud computing architecture is difficult to meet the application requirements. the distributed edge computing technology provides a new idea to solve this problem. Starting from the actual business requirements, the difficulties and challenges faced by the operation control of the integrated energy system are analyzed, and a hierarchical collaborative control architecture of the integrated energy system based on the internet of things is constructed. the applicability and value points of edge computing technology are analyzed from the four levels of the perception layer, network layer, platform layer, and application layer. then, taking the industrial park as an example, a regional integrated energy system operation control model and solution method based on multilateral collaboration are proposed. Finally, the application prospects of edge computing in integrated energy services are prospected. © 2024 IEEE.

关键词： Mobile edge computing

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Semi-asynchronous federation optimization method based on buffer pre-aggregation 24

Semi-asynchronous federation optimization method based on bu...

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5th international conference on computing, Networks and internet of things (CNIOT)

作者： Chen, Yimi Zheng, Xianghan Zhan, Yichen Fuzhou Univ Fuzhou Peoples R China Univ Sanya Sanya Peoples R China Fujian Petrochem Ind Design Inst Co Ltd Fuzhou Peoples R China

ISBN: (纸本)9798400716751

As a distributed machine learning framework, federated learning has received considerable attention in recent years and has been researched and applied in various scenarios. However, the system heterogeneity due to the physical characteristics of various terminal devices has led to the straggler effect, making the practical implementation of federated learning challenging. therefore, we propose a semi-asynchronous federated optimization method based on buffer pre-aggregation. this method allows every participant to engage in training through pre-aggregation and establishes a training time framework based on the pre-aggregation time. It updates the model adaptively using a semi-asynchronous communication method combined with lag factors, improving communication efficiency while maintaining stable accuracy. Experimental results on datasets demonstrate that our proposed method can effectively accelerate the training process of federated learning compared to existing federated optimization methods.

关键词： Federated Learning Semi-Asynchronous Communication distributed Machine Learning

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Cryptography and Adversarial Analysis for Cloud Data Security 5

Cryptography and Adversarial Analysis for Cloud Data Securit...

引用

5th international conference on Innovative Trends in Information technology (ICITIIT)

作者： Manjula, S. Valarmathi, K. Sudha, M. Swarna Ramco Inst Technol Dept Comp Sci & Engn Rajapalayam 626117 Tamil Nadu India PSR Engn Coll Dept Elect & Commun Engn Sivakasi 626140 Tamil Nadu India

ISBN: (纸本)9798350386813;9798350386820

Cloud computing, a novel paradigm for distributed data processing, has emerged as a transformative technology enabling the storage and retrieval of data over the internet. the prevalence of Software-as-a-Service has revolutionized work dynamics, fostering a culture of secure remote access across diverse devices. Within the realm of Cloud Storage, the imperative for effective security and privacy measures is underscored. this research proposes a robust data security framework employing the Cryptography Splitting Algorithm. User files undergo encryption, increasing security levels from 25 to 75 are securely stored with a cloud service provider. the Cloud Manager empowers users by enhancing security, managing tracking mechanisms, and safeguarding metadata, thus fortifying data confidentiality and flexibility in utilization. Leveraging the Cryptography Splitting technique ensures secure data storage, concurrently enhancing both security and privacy for user data. Furthermore, the proposed methodology undergoes rigorous scrutiny in an adversary setup, incorporating neural network analysis to evaluate the resilience of data security measures. this analysis reveals the encryption scheme protects communication by maintaining a low A-B loss around 0.1 while reducing Eve's ability to intercept messages from 0.7 to 0.2. this research contributes a comprehensive approach to securely managing data in the dynamic landscape of cloud computing.

关键词： Cloud computing AES Encryption and Decryption Algorithm Cryptography splitting and merging Technique

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Exploring Robust DDoS Detection: A Machine Learning Analysis with the CICDDoS2019 Dataset 5

Exploring Robust DDoS Detection: A Machine Learning Analysis...

引用

5th IEEE India Council international Subsections conference, INDISCON 2024

作者： Saluja, Kamal Bagchi, Susama Solanki, Vikas Khan, Muhammad Numan Ali Dhamija, Esha Debnath, Sanjoy Kumar Chitkara University Institite of Engineering and Technology Chitkara University Punjab India University of Technology and Applied Sciences College of Computing and Information Sciences Shinas Oman

ISBN: (纸本)9798350376753

the ever-present threat posed by distributed Denial of Service attack is highlighted. the significance of building robust cybersecurity measures that are not just inventive but also flexible. the purpose of this study paper is to investigate the effectiveness of an algorithm based on machine learning in detecting distributed denial of service assault to strengthen the resilience of internet infrastructure. Random forest, decision tree, naïve bayes, and KNN are some of the machine learning techniques that are investigated in this study. the dataset used for this investigation is the CICDDoS 2019 data, Moreover, the research broadened its approach to identify the role of Artificial Intelligence in the Cybersecurity Framework. It does this by providing insight into the relative merits of various algorithms. the study, in its essence, highlights the critical role that adaptive and intelligent defense systems play in decreasing the impact of distributed denial of service assaults (DDoS). this provides a foundation for prepared strategies in the constantly shifting world of cybersecurity. © 2024 IEEE.

关键词： Cybersecurity

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Research on internet of things Signals Acquisition and Processing based on Edge Computation 5

Research on Internet of Things Signals Acquisition and Proce...

引用

5th international conference on Computer Engineering and Intelligent Control, ICCEIC 2024

作者： Zhang, Xiaolong Qiu, Wenheng Yang, Huaping Hou, Aohua College of Electrical Engineering and New Energy China Three Gorges University Yichang Hubei443002 China Hubei University of Arts and Sciences Faculty of Automotive and Traffic Engineering Xiangyang Hubei441053 China

ISBN: (纸本)9798331507992

the proliferation of internet of things (IoT) devices has led to the generation of vast quantities of data, placing considerable strain on conventional cloud computing infrastructure. Edge computing has emerged as a viable solution to address this challenge. In this study, we propose an internet of things (IoT) signal processing framework that is combined with distributed edge nodes. the objective is to improve data transmission efficiency and real-time performance. Firstly, in consideration of the constrained resources of edge devices, we have devised lightweight preprocessing algorithms, including frequency domain filtering based on Fourier transform and a denoising algorithm based on wavelet transform, with the objective of reducing the noise and redundant information present in the original signal. Subsequently, the adaptive compressed sensing technology was employed to compress the preprocessed signal, which resulted in a notable reduction in the bandwidth requirement for data transmission. Subsequently, in order to fully utilise the computing capabilities of edge nodes, we introduce a multi-task learning collaborative computing model. the model permits the sharing of computing tasks between multiple edge nodes, facilitates data collaboration between edge nodes through federated learning, and reduces computing redundancy and communication overhead. the experimental phase employs the Google Edge TPU Dataset to evaluate the dataset pertinent to the internet of things. the results indicate that the proposed methodology exhibits a notable enhancement in data processing latency and transmission efficacy in comparison to the conventional approach. © 2024 IEEE.

关键词： Collaborative computing Edge Computation internet of things Signal Processing

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Privacy computing technology in the context of energy internet 5

Privacy computing technology in the context of energy Intern...

引用

5th international conference on Communications, Information System and Computer Engineering, CISCE 2023

作者： Gong, Yue Chen, Cuiyun Liu, Zhiyu The First Research Institute of the Ministry of Public Security of P.R.C Beijing China

ISBN: (纸本)9798350326796

the power system and other energy systems are constantly cross-integrated, and a new development direction of multi-subject collaboration is gradually formed. Starting from the typical mode of energy internet collaborative optimization, this paper puts forward a preliminary technical scheme for energy internet collaborative optimization considering privacy protection. Aiming at the scenario without a third party, this paper proposes a secure multi-party interactive computing scheme based on the principle of secret sharing, and analyzes the specific characteristics of the scheme when applied to a small number of subject scenarios based on the fully distributed application architecture. Finally, from the aspects of data privacy and computing security, the proposed scheme protects the rights and interests of data owners, reduces the risk of data leakage in energy internet collaborative optimization computing, and realizes reliable privacy protection. © 2023 IEEE.

关键词： Data privacy

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Lattice-based distributed Broadcast Encryption in Multi-user Communication Mechanism 24

Lattice-based Distributed Broadcast Encryption in Multi-user...

引用

5th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： He, Junfei Ye, Qing Yang, Zhichao Department of Information Security Naval University of Engineering Hubei Wuhan China

ISBN: (纸本)9798400718007

Nowadays, emerging technologies such as smart terminals, cloud computing, and the internet of things are developing rapidly. the"One-to-one" encrypted transmission method is difficult to meet the communication needs in complex network environments due to high computational costs. Broadcast encryption technology can provide data encryption protection during information sharing among multiple users. However, most of the existing schemes are based on bilinear groups, which cannot resist quantum computing attacks. And these schemes also suffer from the key escrow problem. For this reason, a novel distributed broadcast encryption scheme is proposed based on the Learning with Errors (LWE) difficulty assumption on the lattice. the scheme avoids key interaction negotiation among users by introducing a public key bulletin board. Meanwhile, the scheme solves the security problem caused by the authoritative user's complete mastery of the user's private key. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： data security

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distributed Data Sharing and Access Control in Industrial IoT Using Blockchain technology 5

Distributed Data Sharing and Access Control in Industrial Io...

引用

5th international conference on Computer Engineering and Intelligent Control, ICCEIC 2024

作者： Li, Rui Sun, Yan Liu, Caiyun Wen, Ying Liu, Yitong Fujian Provincial Industrial and Information Industry Development Research Center Fujian Fuzhou China China Industrial Control Systems Cyber Emergency Reponse Team Beijing China

ISBN: (纸本)9798331507992

With the rapid development of the Industrial internet of things (IIoT), secure and efficient data sharing has become crucial for enabling industrial automation and smart transformation. However, existing centralized solutions face limitations in scalability, latency, and data privacy. To address these challenges, this paper proposes a blockchain-based distributed data-sharing and access control system tailored for resource-constrained industrial edge networks. the system adopts a three-layer architecture that integrates edge computing and blockchain technology to provide decentralized, low-latency data management. A novel smart contract framework is introduced to enforce multi-level access control and support fine-grained permissions for intra-domain and cross-domain data sharing. Experimental results show the system's improved performance in data access efficiency and throughput, offering a robust solution for real-time, secure IIoT applications. © 2024 IEEE.

关键词： Smart contract

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Blockchain-based Federated Learning for Decentralized Energy Management Systems 5

Blockchain-based Federated Learning for Decentralized Energy...

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5th international conference on Blockchain computing and Applications, BCCA 2023

作者： Zekiye, Abdulrezzak Özkasap, Öznur Koç University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350339239

the internet of Energy (IoE) is a distributed paradigm that leverages smart networks and distributed system technologies to enable decentralized energy systems. In contrast to the traditional centralized energy systems, distributed Energy internet systems comprise multiple components and communication requirements that demand innovative technologies for decentralization, reliability, efficiency, and security. Recent advances in blockchain architectures, smart contracts, and distributed federated learning technologies have opened up new opportunities for realizing decentralized Energy internet services. In this paper, we present a comprehensive analysis and classification of state-of-the-art solutions that employ blockchain, smart contracts, and federated learning for the IoE domains. Specifically, we identify four representative system models and discuss their key aspects. these models demonstrate the diverse ways in which blockchain, smart contracts, and federated learning can be integrated to support the main domains of IoE, namely distributed energy trading and sharing, smart microgrid energy networks, and electric and connected vehicle management. Furthermore, we provide a detailed comparison of the different levels of decentralization, the advantages of federated learning, and the benefits of using blockchain for the IoE systems. Additionally, we identify open issues and areas for future research for integrating federated learning and blockchain in the internet of Energy domains. © 2023 IEEE.

关键词： internet of Energy

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