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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

共 6946 条记录，以下是571-580 订阅

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processing Delay Optimization for data Intensive Applications for Fog/Cloud computing Using Double Reinforcement Learning 5

Processing Delay Optimization for Data Intensive Application...

引用

5th IEEE international conference on Advances in Electronics, Computers and Communications, ICAECC 2023

作者： Nirupama, M. Bhargavi, K. Siddaganga Institute of Technology Department of Computer Science Karnataka Tumakuru India

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305449

With smartphones becoming our daily companions, it has generated high-quality mobile services everywhere and there is tremendous growth of mobile traffics. This is going to becomea huge hindrance to the next generation mobile networks. Hence fog computing with cloud computing to deal with highly data-oriented applications sounds like a practical solution. Reinforcement learning is sub branch of machine learning which offers multiple solutions and the learning agent makes decision about superlative solution. A novel Double Reinforcement Learning based delay optimization framework is designed to optimize the processed data which need to be stored to fog/cloud storage system. The agent consider dual Q-states to formulate optimal policies for optimization of processing delay encountered by High Performance computing (HPC) applications. The agent performs very well under varying uncertainty of large state space computing system like cloud/fog. The performance of the proposed framework is good pertaining to performance metrics like processing delay, learning rate, throughput, and cost of operation. © 2023 IEEE.

关键词： Reinforcement learning

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Big data monitoring and analysis of power network security under cloud platform 8

Big data monitoring and analysis of power network security u...

引用

8th international conference on Electronic Technology and Information Science, ICETIS 2023

作者： Xu, Sheng-Guo Liu, Jian Guo, Liang Jia, Xue Nanjing Nari information and Communication Technology Co. Ltd Nanjing210003 China

ISBN: (纸本)9781510666535

With the rapid development of cloud computing technology and various applications, enterprises have also built their own cloud *** each information system gradually goes to the cloud and monitoring data increases, the security protection pressure of the cloud platform increases accordingly, resulting in weak data analysis ability and low alarm failure. computing performance has become a key problem that restricts the security monitoring and analysis of power network under cloud platform. Aiming at the performance bottleneck of security monitoring under cloud level, this paper proposes a method to improve the efficiency of big data monitoring and analysis. This method collects the log data of all kinds of security equipment and security system in the network, and uses the real-time processing framework of Flink and ODPS data processing service on the cloud to quickly analyze the big data. Experimental results show that compared with traditional self-built Hadoop platform and real-time computing Storm, this method consumes less resources and has a faster processing speed for the same log volume. © 2023 SPIE.

关键词： Big data

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Parallel Integrity Authentication data Structure Construction for Encrypted Range Queries 21

Parallel Integrity Authentication Data Structure Constructio...

引用

21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Wang, Zhaokang Pan, Jiahui Zhou, Lu Zhang, Zhonghui Ji, Caocong Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350329223

With the rapid growth of cloud computing, outsourcing databases to cloud servers is becoming increasingly popular. Query integrity authentication is an effective technique to obtain reliable query results from untrusted clouds. ServeDB (Wu et al., ICDE 2019) is a state-of-the-art system that provides integrity authentication for encrypted range queries. However, ServeDB is a serial system. The high construction cost of its authentication data structure SVETree is the main performance bottleneck that limits its scalability to large datasets. In this study, we overcome the scalability limitation by parallelizing the SVETree construction workflow using the MapReduce framework. We propose the link-free storage layout to store the tree-based SVETree structure in a distributed key-value storage. The parallel SVETree construction algorithm reduces the communication latency of the key-value storage with the batch put/get optimization. The algorithm balances the workload of different tree nodes with the record-centric parallelization optimization. Furthermore, it avoids triggering out-of-core shuffles with the multi-round in-memory shuffle technique. The experimental results show that the parallel algorithm running on 128 cores achieves a 52.7 × speedup over the original serial algorithm. The parallel algorithm exhibits near-linear data and machine scalability. © 2023 IEEE.

关键词： MapReduce

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Hierarchical data-driven Predictive Control Strategy for Energy Management of Electric Vehicles 21

Hierarchical Data-driven Predictive Control Strategy for Ene...

引用

作者： Chen, Bin He, Guo Zhou, Feng Huang, Hao Liu, Wei Du, Ronghua Changsha University of Science and Technology College of Automotive and Mechanical Engineering Changsha China Changsha University of Science and Technology College of Electrical and Information Engineering Changsha China

ISBN: (纸本)9798350329223

For electric vehicles (EVs) equipped with hybrid energy storage systems (HESS), a challenge is reasonably distributing and optimizing load power. To optimize the performances of the vehicle power and battery capacity, This paper introduces an innovative data-driven Energy Management Strategy (EMS) designed for HESS, which combines a hierarchical strategy of fuzzy neural network (FNN) and data-enabled predictive control (DeePC). At the driving level, FNN predicts load power demand by collecting historical speed and acceleration information along with current data. At the energy management level, the EMS adopts a novel data-enabled model, which collects the load power predicted by an upper layer. The DeePC achieves power distribution by controlling the supercapacitor current while maintaining the state of charge (SoC) within the expected range. The simulation results verify the effectiveness of the developed EMS. Compared with long-short term memory (LSTM), artificial neural network (ANN), and support vector regression (SVR), FNN demonstrates the best-fitting performance, while the prediction results are closer to the actual load power. Compared with model predictive control (MPC), the proposed DeePC approximately reduces battery capacity loss by 0.15% and demonstrates well control performance. © 2023 IEEE.

关键词： Electric vehicles

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Research into Developing a data Mining-Based Quantitative Trading System for Use with Software and High Performance Computers 4

Research into Developing a Data Mining-Based Quantitative Tr...

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4th international conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2023

作者： Zhang, Paulo Hong Kong Red Lion Capital Limited Hong Kong Hong Kong

ISBN: (纸本)9798350341690

This research is dedicated to the design and study of quantitative trading systems based on data mining methods in software and high-performance computing environments. With the continuous accumulation of financial market data and the improvement of computing power, quantitative trading, as a method of trading decision making using mathematical models and algorithms, has received more and more attention from traders and investors. In this study, the basic concepts, methods and techniques of quantitative trading are firstly reviewed to introduce the important role of data mining in quantitative trading. Subsequently, a design framework for a quantitative trading system based on data mining methods is proposed, which combines the key steps of data collection, feature engineering, model construction, and trade execution to improve the real-time performance and efficiency of the trading system. © 2023 IEEE.

关键词： data mining

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Declarative and linear programming approaches to service placement, reconciled 16

Declarative and linear programming approaches to service pla...

引用

IEEE 16th international conference on Cloud computing (IEEE CLOUD)

作者： Massa, Jacopo Forti, Stefano Dazzi, Patrizio Brogi, Antonio Univ Pisa Dept Comp Sci Pisa Italy

ISBN: (纸本)9798350304817

This article proposes an approach to the data-aware multi-service application placement problem in Cloud-Edge settings. We propose both declarative programming and a Mixed-Integer Linear Programming (MILP) approach to determine eligible placements that minimise operational costs and reduce the number of used nodes to contain the amount of data transfers. After assessing the performance of both approaches, we reconcile them into a methodology that combines the best of the two worlds by exploiting a declarative pre-processing step to boost the MILP solver while determining optimal solutions. We open-sourced the methodology into a prototype that is 10x faster than pure MILP, determines optimal results, and easily accommodates non-numerical constraints on application placements.

关键词： fog computing application placement data-awareness mathematical optimisation logic programming

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Methodological Considerations for Anonymizing Tabular data Using Generative Adversarial Networks 24

Methodological Considerations for Anonymizing Tabular Data U...

引用

8th international conference on algorithms, computing and Systems, ICACS 2024

作者： Marchev, Angel Marchev, Vasil Department of Management University of National and World Economy Sofia Bulgaria

ISBN: (纸本)9798400718304

data anonymization is a crucial process in data science, particularly when dealing with sensitive information subject to personal data protection laws. This paper explores using Generative Adversarial Networks (GANs) as an approach to anonymizing data. GANs utilize a Generator to create new data that resembles the original dataset, and a Discriminator to differentiate between real and generated data. The key phases of the GAN-based anonymization process include data preparation, generator and discriminator model design, adversarial training, synthetic data generation, and final data refinement. This approach allows for the creation of synthetic data that retains the statistical characteristics of the original dataset while ensuring individual privacy is protected. The paper provides technical details on the neural network architectures, activation functions, and training procedures that are critical to the success of this anonymization technique. By taking advantage of the capabilities of GANs, the interested parties can gain valuable insights from sensitive data without compromising individual privacy. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Generative adversarial networks

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Energy Efficiency Improvement Method for data Centers Based on Hybrid Proximal Policy Optimization Algorithm

Energy Efficiency Improvement Method for Data Centers Based ...

引用

2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Fang, Heping Yang, Shihang Peng, Shuhong Liu, Shuguang Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China Chongqing Tongnan District Agricultural Science and Technology Promotion Center Chongqing402660 China School of Computer Science and Engineering Huizhou University Huizhou516007 China

ISBN: (纸本)9781510671881

As an essential support for technology applications such as cloud computing and artificial intelligence, data centers face a pressing need to balance their demand with their own high energy consumption. Since the energy consumption of the information technology (IT) systems and cooling systems accounts for the majority of total energy consumption in data centers, a collaborative optimization framework called COHPPO based on a hybrid proximal policy optimization algorithm (HPPO) is proposed, which controls IT system and cooling system in a coordinated manner, fully compressing the resource redundancy of the IT system and cooling redundancy of the cooling system. The HPPO algorithm is trained and verified using a task dataset and a simulation platform that simulates the environmental state inside the data center. The experimental results demonstrate that the COHPPO collaborative optimization framework proposed in this paper can effectively improve the energy efficiency of the data center and the stability of its internal environment. © 2023 SPIE. All rights reserved.

关键词： Energy efficiency

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Automating Real-Time data Analysis with Machine Learning algorithms 3

Automating Real-Time Data Analysis with Machine Learning Alg...

引用

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

作者： Bhoite, Sonali P. Singh, Satendra Naval, Preeti Panduranga Rao, M.V. Bhushan, Bharat Indira, G. Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India

ISBN: (纸本)9798350319125

Device getting-to-know algorithms enable the automation of actual-time information analysis, permitting agencies to behave on well-timed insights and optimize selection-making. By leveraging predictive models, companies can increase customized services, optimize operations, and obtain a competitive area. This paper discusses how device learning algorithms can automate real-time facts evaluation and the ways such algorithms can be used to uncover developments, locate anomalies, and enhance models. Moreover, the paper explores how applying device mastering algorithms can boost accuracy and efficiency in data evaluation, in addition to providing businesses with new opportunities for innovation. © 2023 IEEE.

关键词： Information analysis

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Research on the Method of Improving the Reliability of Distribution and Power Supply Based on Big data 1

Research on the Method of Improving the Reliability of Distr...

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

作者： Zhang, Juncheng Tan, Jing Li, Min Zhang, Qiju Guangxi Power Grid Co. LTD. Guangxi Nanning530023 China

ISBN: (纸本)9798350323795

This article studies the application of big data methods in distribution reliability prediction, proposes a distribution network reliability evaluation method based on big data, and conducts simulation verification. Firstly, the background and significance of research on distribution network reliability were introduced. Secondly, the application of big data technology in distribution reliability analysis was introduced, including modeling of distribution network reliability based on big data, analysis of load transfer after distribution network faults, optimization model of load transfer, and optimization analysis of distribution network power supply reliability. Finally, the application effect of the proposed method in improving distribution reliability was verified through examples. © 2023 IEEE.

关键词： Genetic algorithms

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