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Use of Network Latency Profiling and Redundancy for Cloud Server Selection

Use of Network Latency Profiling and Redundancy for Cloud Se...

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IEEE international conference on Cloud computing, CLOUD

作者： Minseok Kwon Zuochao Dou Wendi Heinzelman Tolga Soyata He Ba Jiye Shi Dept. of Computer Science Rochester Institute of Technology Rochester NY USA Dept. of Electrical and Computer Engineering University of Rochester Rochester NY USA UCB Pharma Slough UK

As servers are placed in diverse locations in networked services today, it becomes vital to direct a client's request to the best server(s) to achieve both high performance and reliability. In this distributed setting, non-negligible latency and server availability become two major concerns, especially for highly-interactive applications. Profiling latencies and sending redundant data have been investigated as solutions to these issues. the notion of a cloudlet in mobile-cloud computing is also relevant in this context, as the cloudlet can supply these solution approaches on behalf of the mobile. In this paper, we investigate the effects of profiling and redundancy on latency when a client has a choice of multiple servers to connect to, using measurements from real experiments and simulations. We devise and test different server selection and data partitioning strategies in terms of profiling and redundancy. Our key findings are summarized as follows. First, intelligent server selection algorithms help find the optimal group of servers that minimize latency with profiling. Second, we can achieve good performance with relatively simple approaches using redundancy. Our analysis of profiling and redundancy provides insight to help designers determine how many servers and which servers to select to reduce latency.

关键词： Servers Redundancy Extraterrestrial measurements Sea measurements Data models Partitioning algorithms Greedy algorithms

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JadeEE: an Enterprise-class MAS Application Development Framework

JadeEE: an Enterprise-class MAS Application Development Fram...

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international conference on Computer Supported Cooperative Work in Design

作者： Chaozhen Guo Zhihong Lin College of Mathematics and Computer Science Department of Computer Science and Technology Fuzhou University Fuzhou Fujian China

ISBN: (纸本)1424409624

this paper analyzes the architectures of JADE and J2EE: J2EE advances in developing enterprise-class applications, but it doesn't support Agent-oriented programming. JADE is suitable as a MAS development framework, but it considers less in maintainability, expansibility and stability. According to these, this paper proposes a MAS model -JadeEE (Java agent development framework enterprise extension) -which is based on JADE and J2EE. this model is an enterprise-class MAS application development framework which advances in stability, easily expansibility and maintainability. It is suitable for large-scale MAS development, has the same advantages as J2EE and keeps enterprise-class MAS running at an acceptable speed. this framework has been used in the development of GDSS (group decision support system) for typhoon comprehensive forecast effectively.

关键词： Large-scale systems Computer architecture Stability Java Computer science intelligent agent Application software distributed computing Multiagent systems Runtime

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FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training

FedCST: Federated Learning on Heterogeneous Resource-constra...

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IEEE international conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Zhilu Wang Haiyang Lin Qi Liu Yonghong Zhang Xiaodong Liu Nanjing University of Information Science and Technology School of Computer Science Nanjing Jiangsu China Nanjing University of Information Science and Technology School of Software Nanjing Jiangsu China Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing Jiangsu China Nanjing University of Information Science and Technology School of Automation Nanjing Jiangsu China Edinburgh Napier University School of Computing Edinburgh UK

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

With the rapid development of 5G and Internet of things (IoT) technologies, edge devices such as sensors, smartphones, and wearable devices have become increasingly prevalent. the massive amount of distributed data generated by these devices offers unprecedented opportunities for deep learning, especially in fields like computer vision and edge computing. However, this data often exists in isolated data silos, and due to its privacy-sensitive nature, it cannot be directly processed in a centralized manner. Additionally, the data heterogeneity and devices heterogeneity further exacer-bates the challenges in federated learning (FL). Moreover, with the implementation of data privacy protection regulations, user demand for privacy protection is growing significantly. therefore, how to effectively utilize dispersed data for distributed collaborative learning while ensuring data privacy is a pressing issue that needs to be addressed in the field of FL. In response to these challenges, this work has conducted research on clustering and segmentation FL algorithms for heterogeneous resource-constrained devices. A lightweight homogeneous device clustering strategy has been designed, which incorporates a split learning (SL) mechanism to enhance the accuracy and training efficiency of FL models. this approach reduces the load on resource-constrained devices and improves privacy security. Consequently, this method can meet the dual requirements of privacy protection and computational efficiency in real-world scenarios.

关键词： Training Data privacy Privacy Federated learning Computational modeling distributed databases Security Internet of things Protection Edge computing

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An Adaptive Spectrum Allocation Algorithm in Ultra-Dense Network

An Adaptive Spectrum Allocation Algorithm in Ultra-Dense Net...

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international conference on Communication Software and Networks, ICCSN

作者： Jun Zhu Chaochao Jia Chao Wang Kai Li Key laboratory of Intelligent Computing & Signal Processing (Anhui University) Ministry of Education Hefei China Technology and Innovation Department Shanghai Research Center for Wireless Communication Shanghai China

In this paper, we propose a novel method to adaptively adjust the parameter threshold used in overlapping frequency. We verify the performance of the proposed algorithm from the system simulation. Compared with the traditional network planning scheme which needs setting parameters manually, the optimal threshold is adaptively adjusted according to the distribution of the users. the simulation results show that the algorithm can improve the signal-to-interference-plus-noise-ratio (SINR) performance of users and the cell throughput when the users are distributed in the edge of cell coverage which owns the same optimization result as traditional optimization method. So the algorithm can effectively solve the decrease problem of spectral efficiency when the threshold in frequency overlap is set unreasonable and also save a lot of labour cost on setting parameters.

关键词： Interference Signal to noise ratio throughput Clustering algorithms Bandwidth Radio spectrum management Resource management

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A View from ORNL: Scientific Data Research Opportunities in the Big Data Age

A View from ORNL: Scientific Data Research Opportunities in ...

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international conference on distributed computing Systems

作者： Scott Klasky Matthew Wolf Mark Ainsworth Chuck Atkins Jong Choi Greg Eisenhauer Berk Geveci William Godoy Mark Kim James Kress Tahsin Kurc Qing Liu Jeremy Logan Arthur B. Maccabe Kshitij Mehta George Ostrouchov Manish Parashar Norbert Podhorszki David Pugmire Eric Suchyta Lipeng Wan Ruonan Wang School of Computer Science Georgia Institute of Technology Atlanta GA USA Oak Ridge National Laboratory Oak Ridge TN USA Kitware Inc. New York USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA The University of Tennessee Knoxville TN USA Computer Science Department Rutgers University New Brunswick USA

One of the core issues across computer and computational science today is adapting to, managing, and learning from the influx of "Big Data". In the commercial space, this problem has led to a huge investment in new technologies and capabilities that are well adapted to dealing with the sorts of human-generated logs, videos, texts, and other large-data artifacts that are processed and resulted in an explosion of useful platforms and languages (Hadoop, Spark, Pandas, etc.). However, translating this work from the enterprise space to the computational science and HPC community has proven somewhat difficult, in part because of some of the fundamental differences in type and scale of data and timescales surrounding its generation and use. We describe a forward-looking research and development plan which centers around the concept of making Input/Output (I/O) intelligent for users in the scientific community, whether they are accessing scalable storage or performing in situ workflow tasks. Much of our work is based on our experience with the Adaptable I/O System (ADIOS 1.X), and our next generation version of the software ADIOS 2.X [1].

关键词： Data visualization Task analysis Analytical models Big Data Data models Computational modeling Tomography

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Pedestrian Detection System in Low Illumination Conditions through Fusion of Image and Range Data

Pedestrian Detection System in Low Illumination Conditions t...

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17th international IEEE conference on intelligent Transportation Systems & the Asia-Pacific Council on Systems Engineering conference 2014

作者： Pang-Ting Huang Yi-Ming Chan Li-Chen Fu Shih-Shinh Huang Pei-Yung Hsiao Wei-Yu Wu Chun-Cheng Lin Kuo-Ching Chang Ping-Min Hsu the Department of Computer Science and Information Engineering National Taiwan University the Department of Computer and Communication Engineering National Kaohsiung First University of Science and Technology the Department of Electrical Engineering National University of Kaohsiung

the development of pedestrian detection techniques mainly focused on the research on suitable visual feature in the past ***,illumination is not only important to human visual ability to view the surroundings,but is also very critical to the choice of visual features in the vision-based detection *** the visual information can be greatly affected by different illuminations,the resulting detection methods can produce unreliable ***-Range Fusion System(IRFS) is proposed by applying the image data from a camera and the range data from a radar *** the image part,Logarithm Weighted Pattern(LWP) and a Dynamically Illuminated Object(DIO) detector is proposed to overcome the possible problem caused by the uncertain partial lighting condition within a low-illumination *** validate our results,several experiments have been conducted,and the overall system performance is shown to be 88.69%/ 82.81% of recall/ precision under real-time computing setting.

关键词： System Image Pedestrian Detection System in Low Illumination Conditions through Fusion of Image and Range Data

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Extreme computing on the distributed European infrastructure for supercomputing applications: DEISA 08

引用

Proceedings of the 15th international conference on High performance computing

作者： Wolfgang Gentzsch Distributed European Initiative for Supercomputing Applications Duke University

ISBN: (纸本)9783540898931

Scientists' dream of accessing any supercomputer in the world, independentlyfrom time and space, is currently coming true, to perform even largerand more accurate computer simulations, at their finger tip. Today, high-speed networks transport data at the speed of light, middleware manages distributedcomputing resources in an intelligent manner, portal technology enable secure,seemless, and remote access to resources, applications, and data, and sophisticatednumerical methods approximate the underlying mathematical equations ina highly accurate way. With the convergence of these core technologies into onecomplex service oriented architecture, we see the rise of large compute and datagrids currently being built and deployed by e-Infrastructure initiatives such asDEISA, EGEE, NAREGI, and *** the aid of one example, in this keynote presentation, we will elaborate onthe distributed European Infrastructure for Supercomputing Applications,DEISA, which recently entered its second phase. We will describe the system architecture,called the DEISA Common Production Environment (DCPE) and theDEISA Extreme computing Initiative DECI attracting scientists all overEurope to use the networked supercomputing environment, and we will highlighta few impressive success stories from scientists who achieved breakthroughresults so far which would not have been possible without such an ***, we will summarize main lessons learned and provide some usefulrecommendations.

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A RECOMMENDATION MODEL BASED ON COLLABORATIVE FILTERING AND FACTORIZATION MACHINES FOR SICIAL NETWORKS

A RECOMMENDATION MODEL BASED ON COLLABORATIVE FILTERING AND ...

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2013 5th IEEE international conference on Broadband Network & Multimedia technology

作者： Yu ZHANG Xiaomin ZHU Qiwei SHEN Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications

ISBN: (纸本)9781479900961

the communication of persons based on mobile Internet is more and more frequency as the widespread of social network media like Facebook, Weibo and so on. Faced with the vast amount of user groups and variety user features, the traditional single recommended algorithm turns to noneffective. In this paper, we use the collaborative filtering to select features in social network relationship which is used to build a vector space model. Also, we propose a distributed algorithm based on factorization machines and genetic algorithm, and use the vector space model as one part of input. We split the records into different sessions depend on the time sequence for the filter of train dataset. In the experiment part we proved that all the methods mentioned above remarkable improved the recommendation accuracy.

关键词： Social network Recommendation system Factorization machines Collaborative filtering

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Efficient Facial Emotion Recognition Using An Optimized Deep Learning Model Based On Quantum Gazelle Optimization Algorithm 28th

Efficient Facial Emotion Recognition Using An Optimized Deep...

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28th international conference on Knowledge Based and intelligent information and Engineering Systems, KES 2024

作者： Askri, Olfa Manita, Ghaith Hajjaji, Mohamed Ali ENET'Com L'Ecole Nationale d'Electronique et des Télécommunications de Sfax LR99ES30 Monastir5000 Tunisia Université de Monastir Laboratoire d'Electronique et de Microélectronique LR99ES30 Monastir5000 Tunisia Laboratory MARS LR17ES05 ISITCom University of Sousse Sousse Tunisia ESEN University of Manouba Manouba Tunisia Higher Institute of Applied Sciences and Technology Sousse University Sousse Tunisia

this study proposes a new approach for the Facial Expression Recognition (FER) system that combines the Quantum Gazelle Optimization Algorithm (QGOA), local binary patterns (LBP), and Histograms of Oriented Gradients (HOG) with an optimized Deep Neural Network (DNN) classifier. the system uses computer vision techniques and deep learning algorithms to identify emotions in facial expressions. the proposed technique first employs the HOG and LBP descriptors, crucial components with excellent pattern recognition capabilities. these descriptors provide features resilient to small local changes in posture and lighting. However, they also generate unimportant and obtrusive characteristics that hinder classification performance. the proposed approach uses a wrapper-based feature selector called QGOA to solve this issue, which decreases the training complexity and improves recognition performance. QGOA takes advantage of the properties of quantum computing to regulate the diversity of face features and make proper selections using quantum measurements and Q-bit superstitious states. Finally, the optimized DNN detects facial emotions based on the selected features. the proposed approach was tested on the widely adopted FER2013 dataset. the results of the extensive analysis demonstrate the effectiveness of the proposed approach over state-of-the-art systems. © 2024 the Authors.

关键词： Emotion Recognition

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Research on Internet of things Signals Acquisition and Processing based on Edge Computation

Research on Internet of Things Signals Acquisition and Proce...

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Computer Engineering and intelligent Control (ICCEIC) international conference on

作者： Xiaolong Zhang Wenheng Qiu Huaping Yang Aohua Hou College of Electrical Engineering and New Energy China Three Gorges University Hubei Yichang China Faculty of Automotive and Traffic Engineering Hubei University of Arts and Sciences Hubei Xiangyang China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

the proliferation of Internet of things (IoT) devices has led to the generation of vast quantities of data, placing considerable strain on conventional cloud computing infrastructure. Edge computing has emerged as a viable solution to address this challenge. In this study, we propose an Internet of things (IoT) signal processing framework that is combined with distributed edge nodes. the objective is to improve data transmission efficiency and real-time performance. Firstly, in consideration of the constrained resources of edge devices, we have devised lightweight preprocessing algorithms, including frequency domain filtering based on Fourier transform and a denoising algorithm based on wavelet transform, with the objective of reducing the noise and redundant information present in the original signal. Subsequently, the adaptive compressed sensing technology was employed to compress the preprocessed signal, which resulted in a notable reduction in the bandwidth requirement for data transmission. Subsequently, in order to fully utilise the computing capabilities of edge nodes, we introduce a multi-task learning collaborative computing model. the model permits the sharing of computing tasks between multiple edge nodes, facilitates data collaboration between edge nodes through federated learning, and reduces computing redundancy and communication overhead. the experimental phase employs the Google Edge TPU Dataset to evaluate the dataset pertinent to the Internet of things. the results indicate that the proposed methodology exhibits a notable enhancement in data processing latency and transmission efficacy in comparison to the conventional approach.

关键词： Cloud computing Adaptation models Computational modeling Collaboration Signal processing algorithms Bandwidth Data processing Internet of things Data communication Edge computing

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