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

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CNN-Based Detection of DDoS Attacks in Multi-Cloud Environments

CNN-Based Detection of DDoS Attacks in Multi-Cloud Environme...

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Communication, Computer Sciences and Engineering (IC3SE), international conference on

作者： Rashmi Verma Manisha Jailia Munish Kumar Bhawna Kaliraman Department of Computer Science and Engineering Banasthali Vidyapith Tonk Rajasthan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering DPG Institute of Technology and Management Gurugram India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

With the increasing reliance on cloud computing services, ensuring the security of cloud-based infrastructures has become paramount. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) to detect distributed Denial of Service (DDoS) attacks in a multi-cloud environment. The utilization of CNNs, known for their proficiency in feature extraction from structured and unstructured data, offers an innovative solution to the dynamic and evolving nature of DDoS attacks. The datasets utilized in this study include NSL-KDD. As the global frequency of cyberattacks rises, the digital landscape faces significant threats impacting both individual online presence and corporate entities. This paper employs deep learning techniques to enhance security against DDoS attacks, utilizing the inherent ability of deep learning to extract intricate patterns from vast datasets. This makes it a potent tool for constructing effective detection and mitigation systems for the DDoS threat. The research presents a comprehensive approach for detecting DDoS attacks by leveraging CNNs (Convolutional Neural Networks) and advanced data preprocessing methods, focusing on the widely recognized NSL-KDD dataset. The research findings reveal that the proposed CNN-based approach consistently outperforms, achieving an impressive accuracy score of 97.46%. These results underscore the promising potential of the proposed methodology in significantly improving the accuracy and effectiveness of intrusion detection systems. In a parallel exploration, another research paper introduces an alternative approach by offering a CNN model on the same dataset, achieving an accuracy outcome of 96.61%. While this alternative approach demonstrates commendable performance, the findings highlight the superior accuracy achieved by the proposed CNN methodology in the context of DDoS attack detection.

关键词： Deep learning Cloud computing Accuracy System performance Data preprocessing Computer architecture Denial-of-service attack

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Plan-Based Job Scheduling for Supercomputers with Shared Burst Buffers 27th

Plan-Based Job Scheduling for Supercomputers with Shared Bur...

引用

27th international European conference on parallel and distributed computing (Euro-Par)

作者： Kopanski, Jan Rzadca, Krzysztof Univ Warsaw Inst Informat Stefana Banacha 2 PL-02097 Warsaw Poland

ISBN: (纸本)9783030856656;9783030856649

The ever-increasing gap between compute and I/O performance in HPC platforms, together with the development of novel NVMe storage devices (NVRAM), led to the emergence of the burst buffer concept an intermediate persistent storage layer logically positioned between random-access main memory and a parallel file system. Despite the development of real-world architectures as well as research concepts, resource and job management systems, such as Slurm, provide only marginal support for scheduling jobs with burst buffer requirements, in particular ignoring burst buffers when backfilling. We investigate the impact of burst buffer reservations on the overall efficiency of online job scheduling for common algorithms: First-Come-First-Served (FCFS) and Shortest-Job-First (SJF) EASY-backfilling. We evaluate the algorithms in a detailed simulation with I/O side effects. Our results indicate that the lack of burst buffer reservations in backfilling may significantly deteriorate scheduling. We also show that these algorithms can be easily extended to support burst buffers. Finally, we propose a burst-buffer-aware plan-based scheduling algorithm with simulated annealing optimisation, which improves the mean waiting time by over 20% and mean bounded slowdown by 27% compared to the burst-buffer-aware SJF-EASY-backfilling.

关键词： High performance computing (HPC) EASY backfilling Online job scheduling Multi-resource scheduling Simulated annealing Nonvolatile memory Simulation

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Electronic Signature Handwriting Classification and Recognition System Based on Edge computing

Electronic Signature Handwriting Classification and Recognit...

引用

Knowledge Engineering and Communication Systems (ICKECS), international conference on

作者： Hong Li Tao Wang Jiangtao Guo Liu Yang Bingkun Wu State Grid XinJiang Information & Telecommunication Company Urumqi XinJiang China Enterprise Management Division State Grid Info-Telecom Great Power Science and Technology Co. Ltd. Fuzhou Fujian China

With the rapid development of pattern recognition technology, online handwriting identification has become more and more important, and the application scenarios are more abundant. There is a great demand for online handwriting identification in finance, self-study examination, justice and other fields. Online handwriting recognition is an important part of feature recognition research, which realizes identity verification through the signature handwriting of handwritten objects and the behavioral characteristics embodied in the writing process. Online handwritten signature authentication technology is a high combination of electronic signature and traditional handwritten signature. It solves the problem that traditional signatures are easy to be imitated, and has important practical significance. This paper mainly studies the electronic signature handwriting classification and recognition system based on edge computing. This paper proposes a secure distributed image classification scheme based on heterogeneous edge computing for the signature image classification process. PyQT 5, QT Designer, MySQL and other technologies are used to design and develop the computer handwriting verification system software, provide an interactive interface for relevant personnel to use, and conduct relevant tests to ensure the integrity of software functions and logical correctness.

关键词： Training Handwriting recognition Image edge detection Computational modeling Authentication Finance Writing

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Improved ZSI-based DVR for Power Quality Improvement in grid Connected PV Systems

Improved ZSI-based DVR for Power Quality Improvement in Grid...

引用

international conference on Circuits, Communication, Control and computing

作者： Ch. Rami Reddy P. Prudvi Raj P. V. V. Satyanarayana B. Nagi Reddy Obbu Chandra Sekhar B. Srikanth Goud Electrical and Electronics Engineering Malla Reddy Engineering College Secunderabad India Electrical and Electronics Engineering Vignana Bharathi Institute of Technology Ghatkesar Telangana India Electrical Engineering National Institute of Technology Delhi India Electrical and Electronics Engineering Anurag University Ghatkesar Telangana India

Power Quality (PQ) is a major concern while integrating the distributed Generation plants with the grid. This article describes a novel Fuzzy Logic Controller (FLC) with a Z-Source inverter (ZSI) based Dynamic Voltage Restorer (DVR) to improve the PQ of grid-connected Photo Voltaic (PV) systems. DVR is a series-connected IGBT-based Voltage Source Inverter (VSI) designed to compensate for voltage-related power quality concerns such as sag and swell. ZSI obtained superior benefits of buck/boost converter-based DVR in the proposed FLC with DVR than regular VSI. In the instance of a PV system, a hybrid unit vector controller is proposed for efficient detection, accurate voltage disturbance mitigation and also for reducing injected voltage harmonics under severe sag, slight sag with harmonics, swells and interruption. The efficiency of the suggested ZSI-DVR has been demonstrated by comparison research and simulation results, VSI-DVR and ZSI-DVR are traditional DVRs. The PV system ZSI-DVR has reduced voltage sag/interruption/swell. The power quality of the total system is improved compared to existing methods.

关键词： Insulated gate bipolar transistors Fuzzy logic Voltage fluctuations PI control Voltage source inverters Simulation Power quality

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Design of a New Distribution Network Fault Location and Diagnosis Algorithm Based on Multivariate Data Fusion

Design of a New Distribution Network Fault Location and Diag...

引用

Power, Electrical Engineering, Electronics and Control (PEEEC), international conference on

作者： Xinxin Wang Lei Huang Jiaxin Mei Peng Wu Wei Wu Tongren Power Supply Bureau of Guizhou Power Grid Co. LTD Tongren Guizhou China

The access of distributed power sources to distribution networks not only improves the reliability of power supply, but also has a negative impact on relay protection schemes. This has brought difficulty to fault detection and a significant workload to the search for fault points, resulting in prolonged power outages and damage to electrical equipment, seriously affecting power supply reliability. This paper proposes a novel fault diagnosis algorithm for distribution network lines based on multivariate data fusion. The fault location equation is derived from the matrix inversion lemma, which can be easily applied to any system with phase balance conditions. The algorithm has been tested through simulation experiments. Taking the actual positioning distance of 8 km as an example, for inter pole short circuit faults and single pole ground faults, the value of 1/S converges, ensuring good convergence while ensuring convergence speed. The positioning accuracy is independent of the fault type, fault location, and transition resistance values, and can ensure that the error is less than 1%. This method has high positioning accuracy and a wide range of applications. In the process of identifying fault segments, it is only necessary to transmit measurement information within the relevant region to the host, thereby reducing communication traffic and improving computing speed.

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Petrel: Community-aware Synchronous parallel for Heterogeneous Parameter Server 40

Petrel: Community-aware Synchronous Parallel for Heterogeneo...

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40th IEEE international conference on distributed computing Systems (ICDCS)

作者： Zhou, Qihua Guo, Song Li, Peng Sun, Yanfei Li, Li Guo, Minyi Wang, Kun Hong Kong Polytech Univ Hong Kong Peoples R China Univ Aizu Aizu Wakamatsu Fukushima Japan Nanjing Univ Posts & Telecommun Nanjing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9781728170022

As to address the impact of heterogeneity in distributed Deep Learning (DL) systems, most previous approaches focus on prioritizing the contribution of fast workers and reducing the involvement of slow workers, incurring the limitations of workload imbalance and computation inefficiency. We reveal that grouping workers into communities, an abstraction proposed by us, and handling parameter synchronization in community level can conquer these limitations and accelerate the training convergence progress. The inspiration of community comes from our exploration of prior knowledge about the similarity between workers, which is often neglected by previous work. These observations motivate us to propose a new synchronization mechanism named Community-aware Synchronous parallel (CSP), which uses the Asynchronous Advantage Actor-Critic (A3C), a Reinforcement Learning (RL) based algorithm, to intelligently determine community configuration and fully improve the synchronization performance. The whole idea has been implemented in a system called Petrel that achieves a good balance between convergence efficiency and communication overhead. The evaluation under different benchmarks demonstrates our approach can effectively accelerate the training convergence speed and reduce synchronization traffic.

关键词： distributed System Deep Learning Parameter Server Heterogeneous Environment

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Research on parallel/off-grid control and simulation of distributed optical storage system under distribution network

Research on parallel/off-grid control and simulation of dist...

引用

IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Dehuai Kong Xiaojun Zhang State Grid Shandong Electric Power Company Weihai Power Supply Company Weihai China

ISBN: (数字)9781665471978

ISBN: (纸本)9781665471985

in response to the global energy crisis and environmental problems, the State grid has put forward a “new power system”, which mainly increases the proportion of new energy in the traditional power system. Among them, the new energy based on photovoltaic+energy storage at the distribution network terminal will be the most promising form of power generation. Optical storage can not only solve the shortcomings of randomness, intermittence and fluctuation of sub-photovoltaic power generation, provide stable power, but also undertake the task of off-grid power supply after grid failure or in remote areas. Therefore, this paper studies the optical storage system under the distribution network from the perspective of optical storage model and grid-connected control, and builds the optical storage system under the distribution network for simulation verification, which provides a solid foundation for the subsequent coordinated control of load and storage in the source network.

关键词： Photovoltaic systems Analytical models Solid modeling Distribution networks Optical fiber networks Solids Adaptive optics

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IAAT: A Input-Aware Adaptive Tuning framework for Small GEMM 27

IAAT: A Input-Aware Adaptive Tuning framework for Small GEMM

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27th IEEE international conference on parallel and distributed Systems, ICPADS 2021

作者： Yao, Jianyu Shi, Boqian Xiang, Chunyang Jia, Haipeng Li, Chendi Cao, Hang Zhang, Yunquan Institute of Computing Technology Chinese Academy of Sciences SKL of Computer Architecture Beijing China University of Chinese Academy of Sciences Beijing China Indiana University BloomingtonIN United States

ISBN: (纸本)9781665408783

GEMM with the small size of input matrices is becoming widely used in many fields like HPC and machine learning. Although many famous BLAS libraries already supported small GEMM, they cannot achieve near-optimal performance. This is because the costs of pack operations are high and frequent boundary processing cannot be neglected. This paper proposes an input-aware adaptive tuning framework(IAAT) for small GEMM to overcome the performance bottlenecks in state-of-the-art implementations. IAAT consists of two stages, the install-time stage and the run-time stage. In the run-time stage, IAAT tiles matrices into blocks to alleviate boundary processing. This stage utilizes an input-aware adaptive tile algorithm and plays the role of runtime tuning. In the install-time stage, IAAT auto-generates hundreds of kernels of different sizes to remove pack operations. Finally, IAAT finishes the computation of small GEMM by invoking different kernels, which corresponds to the size of blocks. The experimental results show that IAAT gains better performance than other BLAS libraries on ARMv8 platform. © 2021 IEEE.

关键词： Matrix algebra

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Empowering Energy Communities by A User-Centric Model for Self-Managed Congestion via Local P2P and Flexibility Markets

Empowering Energy Communities by A User-Centric Model for Se...

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international conference on European Electricity Market, EEM

作者： Fernando García-Muñoz Sebasttían San Martín Josh Eichman University of Santiago of Chile (USACH) Santiago Chile Catalonia Institute for Energy Research Barcelona Spain

ISBN: (数字)9798350381740

ISBN: (纸本)9798350381757

The expected increase in distributed energy resource (DER) penetration at residential levels is promoting new local market frameworks to manage the use of these resources efficiently and improve users' and energy communities' welfare. In this regard, Peer-to-peer (P2P) energy trading and the flexibility market emerge as tentative solutions to address this purpose, empowering the users and energy communities' role in the electricity markets and in the energy transition. This article introduces a deterministic three-stage optimization model to self-manage congestion arising from the high penetration of DERs within an energy community through a local P2P and flexibility market framework. Thus, under a day-ahead time framework, the initial stage considers that each user minimizes their own objective function, which is the energy bought from the grid. Then, in the second stage, the DSO receives the scheduling dispatch from each user through the local market operator and solves an optimal power flow problem to identify possible congestion line issues. If any congestion appears in the second stage, it is managed in the third stage using the flexibility provided by the DERs and the P2P energy trading, considering the user's preferences derived from the first stage problem. The model has been tested in a modified version of the IEEE 33 bus system and shows the capability to mitigate congestion line issues using the flexibility from the storage and PV systems under a P2P energy trading scheme.

关键词： Privacy Profitability Optimization models Stochastic processes Europe Linear programming Peer-to-peer computing

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Software-Defined Storage Optimization of distributed ALICE Resources 28

Software-Defined Storage Optimization of Distributed ALICE R...

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28th international conference on Software, Telecommunications and Computer Networks (SoftCOM)

作者： Loncar, Petra Gotovac, Sven Univ Split Fac Elect Engn Mech Engn & Naval Architecture Split Croatia

ISBN: (纸本)9789532900996

In today's big data era, data and information are the main capital. High Energy Physics (HEP) as a data-intensive field of research drives significant demands for managing large datasets. Physics data need to be stored, accessed, and properly understood. This paper deals with the increasing resource requirements of the Large Hadron Collider (LHC) experiments at European Laboratory for Particle Physics (CERN). We analyze the unique requirements of the A Large Ion Collider Experiment (ALICE). This paper proposes a software-defined storage (SDS) model based on cloud and edge computing to optimize and organize the data storage of the current ALICE grid computing model. Also, it analyses how software-defined storage and distributed computing technologies could potentially be used to address the challenges and unique requirements faced by the ALICE experiment. The proposed model represents the basis for future research in which the parameters will be further examined, with an emphasis on the scalability of storage and computing resources.

关键词： software-defined storage ALICE distributed computing cloud computing edge computing big data

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