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

共 34256 条记录，以下是4481-4490 订阅

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Reliability Maximization of grid Transaction Processing System using Cuckoo Search-Ant Colony Optimization 20

Reliability Maximization of Grid Transaction Processing Syst...

引用

21st international conference on distributed computing and Networking (ICDCN)

作者： Mahato, Dharmendra Prasad Natl Inst Technol Hamirpur Hamirpur Himachal Prades India

ISBN: (纸本)9781450377515

This paper discusses the task allocation problem in the grid transaction processing system. The task allocation solution is known to be NP-hard. In order to solve this problem, this paper presents task allocation based on cuckoo search-ant colony optimization (LBTA_CSACO) method. The proposed LBTA_CSACO algorithm is based on the cooperative behavior of cuckoo search and ant colony optimization to find a collection of task allocation solutions. The motivation of this work is to maximize the reliability of the system. Six existing algorithms are taken for comparison of results;Social Spider Optimization (SSO), Honey Bee Optimization (HBO), Ant Colony Optimization (ACO), Hierarchical Load Balanced Algorithm (HLBA), Dynamic and Decentralized Load Balancing (DLB), and Randomized respectively.

关键词： grid Transaction Processing System Reliability Cuckoo Search Ant Colony Optimization

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Experimental Comparison of Control Strategies in a Single-Phase grid-Feeding Converter for Microgrid Applications 17

Experimental Comparison of Control Strategies in a Single-Ph...

引用

17th international conference on Electrical Engineering, computing Science and Automatic Control, CCE 2020

作者： Tobias, Agustin Cardenas, Victor Quiroz-Vazquez, Fernando Gonzalez-Rivera, Juan Martinez-Padron, Daniel Universidad Autonoma de San Luis Potosi Engineering Department San Luis Potosi78290 Mexico

ISBN: (纸本)9781728189871

Nowadays, power electronics converters used in distributed generation have became a common tool to exploit the benefits of renewable energy sources, like solar energy via photovoltaic panels the increase of using this kind of systems, working together in parallel operations as a microgrid (MG) in low voltage distribution networks, makes necessary accomplish certain control tasks in order to inject the available power to the grid, achieving a proper power sharing as long as maintaining the grid voltage and frequency among suitable levels for the whole operation system the above mentioned is possible through the use of control strategies that allow the system obtain an adequate transient and steady state performance. This paper presents an experimental comparison between the performance of current decoupled control and passivity based control strategy, to inject active a reactive power using a power electronics converter working as a grid-feeding interconnected to the main grid focused to MG applications. © 2020 IEEE.

关键词： Feeding

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MobiEdge: Mobile Service Provisioning for Edge Clouds with Time-varying Service Demands 27

MobiEdge: Mobile Service Provisioning for Edge Clouds with T...

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

作者： Wu, Tao Fan, Xiaochen Qu, Yuben Yang, Panlong School of Electronic Engineering National University of Defense Technology China Centre for Assessment and Demonstration Research Academy of Military Science China Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China School of Computer Science and Technology University of Science and Technology of China China

ISBN: (纸本)9781665408783

With the proliferation of mobile and Internet of Things (IoT) devices, there has been an unprecedented growth of data consumption and computation requests at the network edge. To support latency-sensitive and resource-intensive mobile services, cellular base stations can be integrated with Mobile Edge computing (MEC) technologies for service provisioning. MEC prompts flexible and configurable provisions of applications or services to make more efficient responses to mobile users' demands. Nevertheless, the time-varying nature of service demands inevitably becomes a vital challenge for existing service provisioning solutions. In this work, we propose MobiEdge, a multi-frame service provisioning scheme across two distinct timescales for MEC networks under various constraints of computation, communication and storage resources. We show that the large timescale indicated by 'frame' is more suitable for adjusting edge server activation and service placement, while the small timescale indicated by 'time slot' is more feasible to schedule users' requests. By leveraging the submodular techniques, we formulate a joint optimization problem and further propose an approximation algorithm with theoretical analysis and proofs. Synthetic and trace-driven evaluation results validate that MobiEdge can benefit both service providers and mobile users in MEC with high profits (e.g., 94% of the optimal) and a relatively low complexity. © 2021 IEEE.

关键词： Mobile edge computing

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A password-authentication scheme based on ciphertext random sampling transmission

A password-authentication scheme based on ciphertext random ...

引用

IEEE international Symposium on parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Lizhu Yang Yong Qin Xuwen Qin Dongcheng Zhang School of Computer Science and Technology Dongguan University of Technology Dongguan China School of Cyberspace Security Dongguan University of Technology Dongguan China G2 Grade Shenzhen College of International Education Shenzhen China

The traditional identity authentication approach of username and password has the security risk of password leakage in the transmission process. For the security of password transmission in the blockchain education architecture, an asymmetric encryption algorithm is used to encrypt the transmitted password. Then use an improved random sampling algorithm to randomly extract part of the ciphertext and transmit those selected ciphertexts. The authentication credentials are sampled ciphertext and the location index. At the same time, the authentication factor is generated through the Time-Stamp technology, and mutual authentication is realized based on the Response-Challenge protocol. Through security analysis and experimental analysis, the proposed scheme is proved to be secure.

关键词： Protocols Education Authentication Focusing Passwords Ubiquitous computing Blockchains

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Federated Fine-Tuning Performance on Edge Devices 20

Federated Fine-Tuning Performance on Edge Devices

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20th IEEE international conference on Machine Learning and Applications (ICMLA)

作者： Orescanin, Marko Ergezer, Mehmet Singh, Gurminder Baxter, Matthew Naval Postgrad Sch Dept Comp Sci Monterey CA 93943 USA Wentworth Inst Technol Sch Comp & Data Sci Boston MA 02115 USA

ISBN: (纸本)9781665443371

In this work, we introduce and evaluate federated fine-tuning (FFT) toward developing decentralized systems for IoT applications using edge computing. We demonstrate a deployed, off-grid, FFT network composed of embedded hardware and assess the system and its performance. The federated averaging algorithm has become the popular approach in decentralized systems with multiple nodes due its low computational costs and simplicity. However, it is commonly implemented using a workstation or a cloud as its server node, typically demonstrated with unrealistic (small) neural network models and may have high communication cost for embedded applications. To address these challenges, we present two main contributions by: (1) Improving the federated averaging algorithm's weight initialization step and by limiting percentage of weights being averaged to enhance system security and model performance, and (2) Demonstrating the proposed system's effectiveness via deployment of realistic model using an edge device as the server node (first time for a federated system). For our evaluation, we use a centrally pre-trained MobileNetV2 model on the CelebA dataset. We record the transmitted model parameters across the network with the modified federated averaging algorithm and FFT, and capture metrics related to memory, power consumption, CPU load, and communication on the device. Overall, results demonstrate that FFT can improve federated system performance and model accuracy while providing stronger privacy, protection of intellectual property, and security against adversarial attacks on federated learning.

关键词： federated learning edge computing distributed machine learning fine-tuning transfer learning

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7th Latin American High Performance computing conference, CARLA 2020

7th Latin American High Performance Computing Conference, CA...

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7th Latin American High Performance computing conference, CARLA 2020

ISBN: (纸本)9783030680343

The proceedings contain 15 papers. The special focus in this conference is on Latin American High Performance computing. The topics include: Electricity Demand Forecasting Using Computational Intelligence and High Performance computing;parallel/distributed Generative Adversarial Neural Networks for Data Augmentation of COVID-19 Training Images;analysis of Regularization in Deep Learning Models on Testbed Architectures;computer Application for the Detection of Skin Diseases in Photographic Images Using Convolutional Neural Networks;Neocortex and Bridges-2: A High Performance AI+HPC Ecosystem for Science, Discovery, and Societal Good;Fostering Remote Visualization: Experiences in Two Different HPC Sites;high Performance computing Simulations of Granular Media in Silos;performance Analysis of Main Public Cloud Big Data Services Processing Brazilian Government Data;accelerating Machine Learning Algorithms with TensorFlow Using Thread Mapping Policies;Methodology for Design and Implementation an Efficient HPC Cluster;estimating the Execution Time of the Coupled Stage in Multiscale Numerical Simulations;Using HPC as a Competitive Advantage in an international Robotics Challenge;a Survey on Privacy-Preserving Machine Learning with Fully Homomorphic Encryption.

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G-Morph: Induced Subgraph Isomorphism Search of Labeled Graphs on a GPU 27th

G-Morph: Induced Subgraph Isomorphism Search of Labeled Grap...

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

作者： Rowe, Bryan Gupta, Rajiv Univ Calif Riverside CSE Dept Riverside CA 92521 USA

ISBN: (纸本)9783030856656;9783030856649

Induced subgraph isomorphism search finds the occurrences of embedded subgraphs within a single large data graph that are strictly isomorphic to a given query graph. Labeled graphs contain object types and are a primary input source to this core search problem, which applies to systems like graph databases for answering queries. In recent years, researchers have employed GPU parallel solutions to this problem to help accelerate runtimes by utilizing the filtering-and-joining framework, which first filters vertices that cannot be part of the solution then joins partial solutions with candidate edges until full isomorphisms are determined. However, the performance of current GPU-based solutions is hindered by limited filtering effectiveness and presence of extraneous computations. This paper presents G-Morph, a fast GPU-based induced subgraph isomorphism search system for labeled graphs. Our filtering-and-joining system parallelizes both phases by upfront eliminating irrelevant vertices via a novel space-efficient vertex signature hashing strategy and efficiently joining partial solutions through use of a novel sliding window algorithm (slide-join) that provides overflow protection for larger input. Together these techniques greatly reduce extraneous computations by reducing Cartesian products and improving edge verification while supporting large scan operations (split-scan). G-Morph outperforms the state-of-the-art GPU-based GSI and CPU-based VF3 systems on labeled real-world graphs achieving speedups of up to 15.78x and 43.56x respectively.

关键词： Graph databases Subgraph isomorphism Breadth-first search GPU programming parallel computing

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RCC: A distributed Data Streaming Software Library for Robot-Cloud Platforms

RCC: A Distributed Data Streaming Software Library for Robot...

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19th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Zhang, Yuhong Sun, Jingtao Ouyang, Chen Chen, Mingkang Wang, Xinyao Wang, Qiang Wuhan Univ Technol Sch Logist Engn Wuhan Peoples R China Natl Inst Informat Tokyo Japan Tokyo Univ Agr & Technol Grad Sch Engn Tokyo Japan Grad Univ Adv Studies Sokendai Dept Informat Tokyo Japan Univ Calif Riverside Dept Mech Engn Riverside CA 92521 USA

ISBN: (纸本)9781665404242

Driven by new technologies such as cloud computing and AI, the cloud robot has entered a golden period of rapid development. The problem that restricts robot developers in current cloud robot research is the lack of a safe way to transmit messages between edge and cloud. Therefore, in this paper, we propose the RCC (Robot Cloud Communication), a Python-based software library, which can help swarm intelligence robots data to be efficiently and securely transferred to different types of business clouds or private cloud platforms. In addition, we integrate the RCC function library and can also provide high-quality data security guarantees for development users when sending and receiving data. Through our approach, we successfully implemented the development of an indoor 3D map application which is difficult to achieve on a single robot platform. Our experimental results show that our RCC is not only very stable in performance of reading and writing, but also shows great performance in data encryption and decryption.

关键词： Smart Edge Cloud computing Robot IoT Kafka Security

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DarkSAM: fooling segment anything model to segment nothing 24

DarkSAM: fooling segment anything model to segment nothing

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Proceedings of the 38th international conference on Neural Information Processing Systems

作者： Ziqi Zhou Yufei Song Minghui Li Shengshan Hu Xianlong Wang Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (纸本)9798331314385

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM.

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Multi-user Edge-assisted Video Analytics Task Offloading Game based on Deep Reinforcement Learning 26

Multi-user Edge-assisted Video Analytics Task Offloading Gam...

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26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Chen, Yu Zhang, Sheng Xiao, Mingjun Qian, Zhuzhong Wu, Jie Lu, Sanglu Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Suzhou Inst Adv Study Hefei Peoples R China Temple Univ Ctr Networked Comp Philadelphia PA 19122 USA

ISBN: (纸本)9781728190747

With the development of deep learning, artificial intelligence applications and services have boomed in the recent years, including recommendation systems, personal assistant and video analytics. Similar to other services in the edge computing environment, artificial intelligence computing tasks are pushed to the network edge. In this paper, we consider the multi-user edge-assisted video analytics task offloading (MEVAO) problem, where users have video analytics tasks with various accuracy requirements. All users independently choose their accuracy decisions, satisfying the accuracy requirement, and offload the video data to the edge server. With the utility function designed based on the features of video analytics, we model MEVAO as a game theory problem and achieve the Nash equilibrium, For the flexibility of making accuracy decisions under different circumstances, a deep reinforcement learning approach is applied to our problem. Our proposed design has much better performance compared with some other approaches in the extensive simulations.

关键词： edge computing video analytics task offloading decentralized algorithm game theory Markov decision process deep reinforcement learning

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