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Challenges and Solutions for distributed File System 2

Challenges and Solutions for Distributed File System

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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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Application Analysis and Development Strategy of cloud computing Technology in Computer Data Processing 2

Application Analysis and Development Strategy of Cloud Compu...

引用

2nd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Lina, Liu Changchun Humanities and Sciences College China

ISBN: (纸本)9798350347456

In the past, data processing was completed on a computer or a server. This is because the resources available at that time were limited. However, with the progress of technology, it is possible to use multiple computers and servers to process large amounts of data. This is where cloud computing comes into play. cloud computing allows users to access data from anywhere at any time using an Internet connection. It also reduces the cost of enterprises because they do not have to buy additional hardware or software for each user. Aiming at the problems of single scale of traditional computer data processing and poor degree of adaptive parallel computing, this paper proposes a multi-source information accelerated data processing method of cloud computing technology, analyzes the application of cloud computing technology in computer data processing, and adopts the dynamic optimization technology of cloud computing speed to build a dynamic adjustment model of multi-source information data flow application of cloud computing, so as to reduce the energy cost of cloud computing, Combined with the experience of data processing users, the model can support multi-user computing entity sharing in the cloud to improve the running efficiency of data flow applications and reduce the energy cost of computer data processing. This article first outlines the basic meaning of cloud computing technology, starting with providing conditions for data transmission and sharing, and highlighting the application value of cloud computing technology. It analyzes the trend of cloud computing technology, and combines the modeling of cloud computing technology in computer data processing to propose application countermeasures for cloud computing technology in computer data processing. © 2023 IEEE.

关键词： cloud computing

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Load Balancing of cloud Resources for Real-Time Tasks Management using Deep Learning 2

Load Balancing of Cloud Resources for Real-Time Tasks Manage...

引用

2nd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Yadav, Savita Goel, Bhawana Greater Noida India Galgotias University Department of Electrical Electronics and Communication Engineering Greater Noida India

ISBN: (纸本)9798350347456

cloud computing is an emerging technology that improves the efficiency of computers by sharing their resources. New cloud-based apps mean more work for existing servers. Although much research has been done in the field of cloud computing, there are still challenges that must be overcome in order to improve performance and provide the greatest possible customer happiness. In this work, we cover the Machine Learning methods used for assembling VM squads with certain CPUs in mind. Quality of Service (QoS) is achieved through the application of DL methods, which entails the increased efficiency with which resources are used and deployed, the decrease in waiting time, response time, and costs that results from this, and the resulting improvement in system reliability due to the more even distribution of load across all equipment. For those interested in load balancing algorithms, this article provides a thorough discussion of many machine learning and deep learning-based methods. © 2023 IEEE.

关键词： cloud computing

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Seagull-optimized Deep Convolutional Neural Network for Detecting distributed Denial of Service Attack in cloud computing 2

Seagull-optimized Deep Convolutional Neural Network for Dete...

引用

2nd international conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sanap, Yogesh B. Aher, Pushpalata G. Sandip University Nashik Maharashtra Nashik422213 India

ISBN: (纸本)9798350368413

Due to the enormous benefits and applications of cloud computing, there is a lot of interest from researchers on cloud computing. The most significant security risk associated with cloud computing is insider distributed denial of service (DDoS), which affects the entire deactivation of services due to its dispersed architecture and utter dependency on the internet for service supply. Machine learning and Deep learning have appeared as a potent tool in cyber security recently, and numerous attack detection methods have been put out in recent studies. Nevertheless, existing studies on attack detection encounter several challenges, including data imbalance, interpretability issues, inaccurate detection, overfitting, and time complexity issues. Therefore, in order to overcome these difficulties, a deep learning model, Seagull optimized Synthetic Minority Over-sampling Technique enabled Deep Convolutional Neural Network (SgO-SCNN) is developed in this research to detect DDoS attacks. The optimized SMOTE employed in the model addresses the imbalanced data issue and improves the model's performance by effectively rebalancing the data. The DCNN classifier can detect the attacks more efficiently and reduces the computation of the model. The seagull optimization tunes the DCNN classifier's parameters and improves the model's performance in attack detection with fast convergence, which reduces the model's time complexity. The experimental outcomes indicate that the developed SgO-SCNN model outperforms the existing approaches by attaining enhanced values of accuracy 96.05%, F1-score 94.66%, precision 94.84%, and recall 94.48%. © 2024 IEEE.

关键词： Convolutional neural networks

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Advancing cloud Security Frameworks Implementing distributed Ledger Technology for Robust Data Protection and Decentralized Security Management in cloud computing Environments 2

Advancing Cloud Security Frameworks Implementing Distributed...

引用

2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Rajasekar, P. Kalaiselvi, K. Shanmugam, Raju Tamilselvan, S. Pandian, A. Pasumpon Department of ECE Sri Krishna College of Technology Kovaipudur Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Tamil Nadu Coimbatore India Unitedworld School of Computational Intelligence Karnavati University Gujarat India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu Chennai India Department of Computer Science and Engineering CARE College of Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350383867

cloud computing caused the paradigm shift in business performance, from scalability to flexibility as well as affordability in the management of computing resources. Nevertheless, ensuring security of the data remains a key issue in the cloud due to the centralized interactions between service provider and a single vulnerability in the network can put rest of the data at risk. Consequently, the application of DLT, in addition to other solutions such as DLT, demonstrates a big step towards the improvement of cloud security frameworks. This article focuses on the implementation of DLT for data protection applications that are based on a solid dataset and decentralized security procedures in cloud environments. DLT utilization, like blockchain, provides an organization with integrity data, transparency, and fitness to counter cyber threats. Architecture stacks up sponsors such as cloud providers, users, and applications and DLT is responsible for the security of the transaction and the security policy enforcement. Customers use those services via desktop based application with transparent security policy arisen from DLT. Moreover, DLT also does identity management in the way that is decentralized, data sharing in the manner that it is secure, and the compliance to regulatory frameworks, which in addition reduces the trust and privacy risks in the cloud ecosystems. The proposed architecture is genuinely a big jump toward protecting cloud security from centralized control interruptions and thus, enhancing resilience and stability in cloud computing. © 2024 IEEE.

关键词： cloud security

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Impacts of High Denisty cloud computing on Data Protection and Security Management for 6G Networking 2

Impacts of High Denisty Cloud Computing on Data Protection a...

引用

2nd international conference on Disruptive Technologies, ICDT 2024

作者： Mallikarjunaradhya, Vinay Yennapusa, Haritha Palle, Ranadeep Reddy Suganyadevi, K. Gupta, Nidhi Legal Tech Platform Thomson Reuters Toronto Canada University of Central Missouri Department of Computer Science AustinTX United States University of Houston Department of Computer Science AustinTX United States Sri Eshwar College of Engineering Department of Ece Tamil Nadu Coimbatore India G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

Data protection and security management in the 6G era is a key challenge due to the emergence of high density cloud computing. The availability of high density cloud computing technologies creates a platform for massive data storage, processing, and analytics capabilities at a scale that has never been seen before. High density cloud computing facilitates the deployment of complex distributed systems consisting of hundreds or thousands of nodes in a single deployment. This can have a significant impact on data protection and security management as it increases the potential threat to the system from malicious actors. To protect data in this environment, organizations must adopt more advanced security practices such as access control, encryption, and defensive layers to protect against malicious actors. The use of encryption can provide a layer of security by making it difficult for outsiders to access information without authorization. Access control can also prevent unauthorized users from accessing certain parts of the system, while defensive layers can help detect and block malicious attacks. The results of this study showed that high density cloud computing can potentially improve the efficiency and performance of data protection and security management in 6G networks. © 2024 IEEE.

关键词： 6G Networking cloud Data Storage Data Protection Network Reliability Network Security Security Management

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Music Intelligent Learning System Based on cloud computing and Improved Ant Colony Algorithm 2

Music Intelligent Learning System Based on Cloud Computing a...

引用

2nd international conference on distributed computing and Optimization Techniques, ICDCOT 2024

作者： Li, Meng University of Music and Performing Arts Mianyang Teachers'college Sichuan Mianyang China

ISBN: (纸本)9798350382952

Music learning plays an important role in cultivating the aesthetic taste and developing creativity of young people. The current traditional music education is unable to provide rich and diverse learning resources, nor can it pay attention to individual differences among young people and achieve personalized teaching. This article conducted extensive research on the development of a music intelligent learning system by combining cloud computing and improved ant colony optimization (ACO). The goal was to improve the quality of music learning for teens and accomplish individualized music learning. This article built a system architecture with three levels: application layer, service layer, and data layer, after first analyzing the design requirements of a music intelligent learning system. Finally, by adding adjustable control parameters for state transition rules, ACO random optimization was achieved, and the cloud computing technology was combined to achieve personalized learning resource recommendation. This article tested the system performance and learning efficiency from two angles to confirm the efficacy of the music intelligent learning system. The results showed that compared to students who used traditional offline courses for music learning, the average score of students who used the music intelligent learning system in this article for music learning increased by 0.38 points. The conclusion indicated that a music intelligent learning system based on cloud computing and improved ACO could help achieve personalized music learning and improve the effectiveness of youth music learning. © 2024 IEEE.

关键词： cloud computing

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forward and Backward Private Searchable Encryption with Fine-grained Access Control 2

Forward and Backward Private Searchable Encryption with Fine...

引用

2nd international conference on cloud computing, Big Data Application and Software Engineering, CBASE 2023

作者： Xu, Peiming Xu, Lingling Jin, Xiang Network Security Electric Power Research Institute CSG Guangdong Provincial Key Laboratory of Power System Guangzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350331448

With the rapid development of cloud computing, more and more data are stored in the cloud server. However, due to the increasing demands for security and user privacy, sensitive data are expected to be stored in an encrypted manner. Then how to perform keyword search over the encrypted data becomes a key issue. The technology of searchable encryption has been proposed to address this issue, which allows the data owners share their encrypted data with users without leaking the queried keyword privacy. In this paper, we study forward and backward private searchable encryption with fine-grained access control. Our system can resist against the file injection attacks from malicious cloud and support forward and backward data privacy. In addition, it enables the data owners to enforce "And""NOT"attribute-based access control on the data. We analyze the security and simulate the system. The experiment results show that our system can be applicable in the real-world scenarios. © 2023 IEEE.

关键词： Access control

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Performance Optimization in distributed Multi-Agent Reinforcement Learning on cloud-Based Platforms 15

Performance Optimization in Distributed Multi-Agent Reinforc...

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

作者： Choudhary, Binod Kumar Kumar, Deepak Sambargi, Seema Department of Electrical & Electronics Engineering ARKA JAIN University Jharkhand Jamshedpur India Department of Mathematics Vivekananda Global University Jaipur India Karnataka Bangalore India

ISBN: (纸本)9798350370249

Dispensed multi-agent reinforcement learning (MARL) is an effective tool for tackling tough manage troubles by means of allowing coordination and cooperation among multiple agents. In cloud-based platforms, MARL can benefit from the availability of compute resources for education, but they face precise scalability and optimization-demanding situations. In this paper, we endorse a collection of methods for improving the overall performance of disbursed MARL in cloud-primarily based structures. We attention on two elements of overall performance optimization for scaling out distributed MARL: implementation optimization and optimization across a couple of stages of education. First, we advocate numerous strategies for enhancing the implementation of dispensed MARL, which include optimizing reminiscence usage and introducing size-aware optimizers. 2nd, we propose methods of optimizing allotted MARL across one-of-a-kind phases of education, inclusive of optimization for hyper parameter selection and optimizing education convergence. Our experiments display that our proposed methods improve the performance of distributed MARL in cloud-primarily based platforms, leading to higher scalability and quicker mastering. ©2024 IEEE.

关键词： cloud platforms

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2nd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

2nd IEEE International Conference on Distributed Computing a...

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2nd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

ISBN: (纸本)9798350347456

The proceedings contain 295 papers. The topics discussed include: deep learning & computer vision integrated smart voting system;performance analysis of machine learning algorithms with hyperparameter tuning for diabetes prediction;transient analysis of motor terminal voltage, common mode voltage and bearing voltage in 2-level and multilevel PWM inverter fed induction motor with long cable;comparative analysis of phase/frequency detector in a complete PLL system;a research on various security aware mechanisms in multi-cloud environment for improving data security;network traffic virtualization using Wireshark and Google maps;cluster enabled routing algorithm for wireless sensor networks: an optimization and future prospective;detection and recognition of animals using Yolo algorithm;voltage improvement and loss reduction by placement and sizing of DG using grid oriented multi objective particle swarm optimization;prediction of heart disease using naive bayes and particle swarm optimization (PSO) method;lane detection using video processing for robot cars;convolutional neural network for printed circuit board verification;and multiple disease prediction based on user symptoms using machine learning algorithms.

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