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KV-CSD: A Hardware-Accelerated Key-Value Store for data-Intensive Applications 25

KV-CSD: A Hardware-Accelerated Key-Value Store for Data-Inte...

引用

25th IEEE international conference on Cluster computing (CLUSTER)

作者： Park, Inhyuk Zheng, Qing Manno, Dominic Yang, Soonyeal Lee, Jason Bonnie, David Settlemyer, Bradley Kim, Youngjae Chung, Woosuk Grider, Gary SK Hynix Cheongju South Korea Los Alamos Natl Lab Los Alamos NM USA NVIDIA Santa Clara CA USA Sogang Univ Seoul South Korea

ISBN: (纸本)9798350307924

Popular software key-value stores such as LevelDB and RocksDB are often tailored for efficient writing. Yet, they tend to also perform well on read operations. This is because while data is initially stored in a format that favors writes, it is later transformed by the DB in the background into a format that better accommodates reads. Write-optimized key-value stores can still block writes. This happens when those background workers cannot keep up with the foreground insertion workload. This paper advocates for a hardware-accelerated key-value store, enabling performance-critical operations, like background data reorganization and queries, to execute directly on storage instead of a host as existing key-value stores do. This better hides background work latency, prevents it from blocking foreground writes, and improves overall I/O efficiency. Our prototype, called KV-CSD, is a key-value based computational storage device consisting of an NVMe SSD and a System-on-a-Chip (SoC) that implements an ordered key-value store atop the SSD. Through offloaded processing, KV-CSD streamlines data insertion, reduces host-device data movement for both background data reorganization and query processing, and shows up to 10.6x lower write times and up to 7.4x faster queries compared to the current state-of-the-art software key-value stores on a real scientific dataset.

关键词： Computational storage High-performance computing Key-value stores Secondary indexes

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Utilizing Machine Learning algorithms to Optimize Real-Time data Analysis 3

Utilizing Machine Learning Algorithms to Optimize Real-Time ...

引用

3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Vaishali Gupta, Eesha Jana, D. Little Femilin Deshmukh, Gopal B. Singla, Abhishek Krishna, K. Gopala Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

This paper offers a way for optimizing actual-time records analysis the usage of a variety of machine gaining knowledge of algorithms. The algorithms paintings by creating predictive fashions which extract useful information from datasets, follow appropriate preprocessing techniques to make sure the high-quality of the records, and model the information to deduce meaningful capabilities from the data. The predictive models are then used to discover applicable developments, find patterns, and summarize the data for clean analysis. specifically, this paper outlines the technique of using supervised and unsupervised learning algorithms, consisting of Random wooded area, Naïve Bayes, and k-approach clustering, to extract meaningful styles and relationships from facts points in real-time. Additionally, the paper discusses wonderful capacity packages of the proposed technique, including fraud detection and prediction of stock costs. Such capability programs demonstrate the fee of actual-time records analysis for organizations and organizations. © 2023 IEEE.

关键词： Clustering algorithms

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An Innovation Development of Decentralized Prove Piercing data Recuperation Using Improved Red in Mobile Grid computing 2

An Innovation Development of Decentralized Prove Piercing Da...

引用

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

作者： Karthikeyan, S. Thomas, Merin Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The recent advancements in mobile computing have opened up the possibilities of decentralized data recovery in mobile grid computing. With the help of improved Red (Recovery of Erased data) technique, data recovery can be done in a decentralized manner. This method is a combination of distributed computing, data replication, and data recovery algorithms which improves the data recovery process. The improved Red technique uses distributed computing to replicate the data and store it in multiple nodes in the grid. This helps to ensure that the data is distributed and available in multiple nodes for recovery. data recovery is done by the Red algorithm which traverses the grid and identifies the nodes which have the data and then recovers it. The algorithm uses a technique called "greedy search"to identify the nodes which have the data. This helps to reduce the time and cost associated with the recovery process. The improved Red technique also makes use of data replication which helps to improve the data recovery process. The data is replicated and stored in multiple nodes which helps to improve the speed of data recovery. The replication process helps to ensure that the data is always available and can be recovered quickly. The improved Red technique also makes use of distributed computing to improve the scalability of the data recovery process. The distributed computing helps to ensure that the data recovery process can be done efficiently and quickly. The data recovery process can also be done on a large scale which helps to improve the speed of data recovery. © 2023 IEEE.

关键词： Recovery

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Edge Intelligent-based data processing Method for High-frequency Information Acquisition in Low-voltage Substation Area 8

Edge Intelligent-based Data Processing Method for High-frequ...

引用

8th international conference on Energy System, Electricity, and Power, ESEP 2023

作者： Ma, Jian He, Junwen Lu, Tao Song, Peng Li, Qian Zhao, Wen Shang, Bing Guangdong Power Grid Co. Ltd. Metrology Center Guangdong China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

Aiming at the problem of large data Transmission delay and computation delay in data processing of High-frequency information acquisition in low-voltage substations area, the model of edge intelligent high-frequency information acquisition system is constructed, and a joint optimization method of task unloading strategy and transmission strategy based on mobile edge computing is proposed. With the goal of minimizing system delay, the task unloading and transmission strategy is optimized to achieve two-way rapid detection of multiple power lines, effectively improving system performance. © 2024 SPIE.

关键词： data handling

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Assessing the Efficacy of Symbolic Regression for Scientific data Classification and Regression 14

Assessing the Efficacy of Symbolic Regression for Scientific...

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Guruprasad, Pradhumna Nagesh, Shreyas Sampath, Aditya N Salai Sanjay, S. Jeyakumar, G. Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering India Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering India

ISBN: (纸本)9798350335095

Symbolic regression (SR) is a subdomain of evolutionary computing which aims to generate mathematical equations which best fit a given dataset. This is done with the help of genetic algorithms. Symbolic Regression is especially useful in cases where features in a dataset are known to have distinct mathematical relations to each other and its output. Therefore, this paper aims to compare and provide insight on the results of using SR on two datasets with two python libraries. These results are further compared with other ML models to assess their performance in classification and regression tasks. The detailed methodology and results are discussed further test metrics and obtained equations. The results show that symbolic regression can perform very well on certain datasets whose target outputs have a mathematical connection to the input features. SR is found to work better than other ML models in the classification and regression tasks. © 2023 IEEE.

关键词： Genetic algorithms

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Cloud-edge collaborative data processing optimization method for low-voltage power distribution in new-Type power system 3

Cloud-edge collaborative data processing optimization method...

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3rd international conference on Mechanical, Electronics, and Electrical and Automation Control, METMS 2023

作者： Zhang, Jie Lu, Tao Song, Peng Chen, Peng Chen, Nan Guangdong Power Grid Co. Ltd. Metrology Center Guangdong China

ISBN: (纸本)9781510666689

To improving the resource utilization and data processing efficiency for low-voltage power distribution in new-Type power system, this paper first investigates a cloud-edge collaborative edge processing architecture for low-voltage power distribution. Then, a cloud-edge collaborative data processing optimization method for low-voltage power distribution is proposed to optimize the data processing mode and computing resource allocation strategies. Simulation results show that the proposed method can effectively ensure the data processing efficiency and stabilization for low-voltage power distribution. © 2023 SPIE.

关键词： Efficiency

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Application of integrated energy data processing based on real-time stream processing platform 4

Application of integrated energy data processing based on re...

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4th international conference on Computer Science and Management Technology, ICCSMT 2023

作者： Jiao, Xinyuan Hou, Zheng Zhang, Mimi Feng, Dechao Cai, Zhencong State Grid Information and Telecommunication Group Co. Ltd. Beijing China Beijing Energy Group Co. Ltd. Beijing China

ISBN: (纸本)9798400709517

Integrated energy is an important component of the new power system, and its IoT components need to collect and process energy equipment measurement data in real time. In order to solve the problems of large batches of equipment measurement data, measurement data requiring multiple calculations based on the relationship between equipment, and timely data processing requirements, distributed real-time data synchronization and processing technology is introduced to build a real-time stream processing platform, thereby ensuring stable operation of the system and efficient use of computing resources. First, the key technologies of the real-time stream processing platform are introduced, then the design of the integrated energy data collection and processing architecture is explained, and finally the real-time stream processing platform is built and functionally verified. © 2023 ACM.

关键词： Computer architecture

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Research on Wireless Encrypted Communication Based on Multi-Channel Transmission 3

Research on Wireless Encrypted Communication Based on Multi-...

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3rd international conference on Intelligent Communications and computing, ICC 2023

作者： Li, Yuefeng Zhang, Xuewei Zhang, Ziqiang Huang, Qiangqiang Yan, Rong Li, Fen Zhang, Jingyu Yin, Yuan Beijing Aerospace Xinfeng Mechanical Equipment Co. Ltd Beijing China

ISBN: (纸本)9798350308327

With the development of the information age and the popularization of the internet, the ways of obtaining information are becoming increasingly rich, and the users' information security awareness is also constantly improving. The secure transmission of information has gradually become an inevitable requirement and tendency. Currently, most of the encryption algorithms utilize single-channel encryption for transmission, and sophisticated algorithms are mostly used for commercial purpose, which are not very economical for ordinary users. Besides, no matter how complex the encryption algorithm is, there is always a risk of being deciphered. Furthermore, once some commercial or industrial confidences are hijacked and deciphered, it will cause unpredictable losses. This paper proposes a wireless encrypted communication method based on multi-channel transmission. The proposed method integrates 'unpacking, multi-channel transmission, combining' operations on transmitting data, by applying corresponding algorithms. The data is transmitted in the form of 'fragments', which increases information entropy during the transmission process, and then improves the security of information transmission. Experimental results show that the method proposed has obvious advantages compared to the traditional data encryption transmission methods. At the same time, it provides a new low-cost, easy-to-operate solution for secure information transmission. © 2023 IEEE.

关键词： Security of data

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A Blockchain-empowered Federated Learning Framework Supprting GDPR-compliance 10

A Blockchain-empowered Federated Learning Framework Supprtin...

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10th IEEE international conference on Cyber Security and Cloud computing and 9th IEEE international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Xiao, Lijun Han, Dezhi Zhou, Sisi Xu, Nengxiang Chen, Lin Xie, Siqi College of Information Engineering Shanghai Maritime University Shanghai200135 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

ISBN: (纸本)9798350312461

In recent years, data privacy security has been widely and highly valued by countries around the world. In the context of European Union's General data Protection Regulation (GDPR), the regulatory requirements of laws and regulations are becoming increasingly strict, bringing huge impacts and challenges to enterprises with user's personal data such as internet services and financial technology. Up to a point, federal learning ensures data privacy by storing and processing personal data locally. However, due to malicious clients or central servers being able to launch attacks on global models or user privacy data, the security of federated learning is questioned, introducing blockchain into the federated learning framework is a feasible solution to address these data security issues. In this work, the concept of Blockchain (BC), Federated Learning (FL), GDPR and other similar data protection laws are presented, where a Blockchain-empowered Federated Learning (BC-empowered FL) framework is introduced. The challenges on complying with the GDPR are described, and the solutions or principles for improving the GDPR-compliance of BC-empowered FL systems are analyzed, sorting out the differences and connections among the GDPR-compliance methods yet laying a foundation to design legal and compliant applications for different domains and scenarios which need touch upon the user's personal data. © 2023 IEEE.

关键词： data privacy

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Secure data Transmission using Hybrid Crypto Processor based on AES and HMAC algorithms 2

Secure Data Transmission using Hybrid Crypto Processor based...

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2nd international conference on Advancements in Electrical, Electronics, Communication, computing and Automation, ICAECA 2023

作者： Umamaheswari, S. Vishal, N.R. Pragadesh, N.R. Lavanya, S. Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (纸本)9798350306811

In today's digital age, vulnerabilities in digital platforms have become a major concern, as cyber-attacks can lead to significant financial losses and privacy breaches. Encryption and authentication algorithms, such as AES and HMAC, are commonly used to secure digital platforms. However, running these algorithms in software can burden the CPU, leading to inefficiencies and potential security vulnerabilities. In this proposed paper, the implementation of a crypto-processor based on AES and HMAC to address the inefficiencies of software-based encryption and authentication. Our implementation uses AES for encryption and HMAC for integrity and authentication. By implementing these functions in hardware, we achieve greater efficiency and security, while reducing resource consumption and power usage. Our proposed crypto-processor design provides an efficient and secure solution for digital platform security, improving the overall performance of cryptographic operations. We demonstrate the effectiveness of our design through a comparison with software-based implementations of AES and HMAC, showing that our design provides a significant improvement in terms of throughput, latency, and power consumption. Our research contributes to the ongoing efforts to improve the security and efficiency of digital platforms, helping to protect sensitive data and ensure the privacy of users. Our proposed crypto-processor design provides a cost-effective and scalable solution for securing digital platforms, making it suitable for a wide range of applications in both commercial and government sectors. © 2023 IEEE.

关键词： Efficiency

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