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共 4336 条记录，以下是1281-1290 订阅

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Machine learning based performance analysis and prediction of jobs on a HPC cluster 20

Machine learning based performance analysis and prediction o...

引用

20th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Hou, Zhengxiong Zhao, Shuxin Yin, Chao Wang, Yunlan Gu, Jianhua Zhou, Xingshe High-performance Computing Center School of Computer Science Northwestern Polytechnical University Xi'an China School of Software Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781728126166

There are a lot of middle-class or small-class high-performance computing clusters at universities and research institutes, etc. Large volumes of job logs have been accumulated after many years of operation. In this paper, on the basis of accumulated job logs on a high-performance computing cluster, we examine and analyze the job logs. Then, we study machine learning based performance analysis and prediction methods for parallel jobs. Various machine learning methods such as multivariate linear fitting, artificial neural network are used to build performance prediction models. We compare the errors of each model, and select the optimal prediction model for different users. The experimental results show that we can obtain reasonable prediction accuracy using the selected machine learning algorithms. © 2019 IEEE.

关键词： Neural networks

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RNC: Reliable network property classifier based on graph embedding 20

RNC: Reliable network property classifier based on graph emb...

引用

20th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Liao, Hao Liu, Qixin Vidmer, Alexandre Zhou, Mingyang Mao, Rui National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126166

In the past two decades, analyzing the information network has been intensively studied from various disciplines. Small world property and scale-free property prevail in network science research. The comparison and classification of different kinds of graphs are extremely important. However, how to design a robust and accurate classification with deep learning techniques for network property still lack enough attention, which is a vital task in various application scenarios. In this paper, we proposed the reliable network property classifier based on graph embedding(RNC) to classify the network property (scale free or small world property). In order to process non-euclidean data, we embedded each network into an image and use dimensional reduction, rasterization, and convolutional neural networks to complete the classification problem. The method can effectively accomplish classification tasks in not only artificial networks but also real networks. Besides, RNC wins greatly in terms of robustness on real networks, showing the robustness of RNC against the incomplete structure of the network. © 2019 IEEE.

关键词： Deep learning

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App-Net: A Hybrid Neural network for Encrypted Mobile Traffic Classification

App-Net: A Hybrid Neural Network for Encrypted Mobile Traffi...

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IEEE conference on Computer Communications Workshops, INFOCOM Wksps

作者： Xin Wang Shuhui Chen Jinshu Su College of Computer Science and Technology National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

With the exponential growth of mobile traffic data, mobile traffic classification is in a great need. It is an essential step to improve the performance of network services such as QoS and security monitoring. However, the widespread use of encrypted protocols, especially the TLS protocol, has posed great challenges to traditional traffic classification techniques. As the rule-based deep packet inspection approaches are ineffective for encrypted traffic classification, various machine learning methods have been studied and used. Recently, deep learning solutions which enable automatic feature extraction are also proposed to classify encrypted traffic. In this paper we propose App-Net, an end-to-end hybrid neural network, to learn effective features from raw TLS flows for mobile app identification. App-Net is designed by combining RNN and CNN in a parallel way. So that it can learn a joint flow-app embedding to characterize both flow sequence patterns and unique app signatures. We evaluate App-Net on a real-world dataset that covering 80 apps. The results show that our method can achieve an excellent performance and outperform the state-of-the-art methods.

关键词： Cryptography Feature extraction Payloads Recurrent neural networks Machine learning Convolutional neural networks

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Clustering-preserving network Flow Sketching

Clustering-preserving Network Flow Sketching

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IEEE Annual Joint conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yongquan Fu Dongsheng Li Siqi Shen Yiming Zhang Kai Chen Science and Technology Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology SING Lab Hong Kong University of Science and Technology

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

network monitoring is vital in modern clouds and data center networks that need diverse traffic statistics ranging from flow size distributions to heavy hitters. To cope with increasing network rates and massive traffic volumes, sketch based approximate measurement has been extensively studied to trade the accuracy for memory and computation cost, which unfortunately, is sensitive to hash *** paper presents a clustering-preserving sketch method to be resilient to hash collisions. We provide an equivalence analysis of the sketch in terms of the K-means clustering. based on the analysis result, we cluster similar network flows to the same bucket array to reduce the estimation variance and use the average to obtain unbiased estimation. Testbed shows that the framework adapts to line rates and provides accurate query results. Real-world trace-driven simulations show that LSS remains stable performance under wide ranges of parameters and dramatically outperforms state-of-the-art sketching structures, with over 10 3 to 10 5 times reduction in relative errors for per-flow queries as the ratio of the number of buckets to the number of network flows reduces from 10% to 0.1%.

关键词： Monitoring Arrays Estimation error Adaptation models Approximation error Data centers

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A hybrid trust management model for secure and resource efficient vehicular ad hoc networks 20

A hybrid trust management model for secure and resource effi...

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20th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Mahmood, Adnan Siddiqui, Sarah Ali Zhang, Wei Emma Sheng, Quan Z. Department of Computing Macquarie University SydneyNSW2109 Australia School of Computer Science University of Adelaide North Terrace AdelaideSA5005 Australia

ISBN: (纸本)9781728126166

Considerable innovative advancements in vehicular communication have led to the emerging yet promising paradigm of Internet-of-Vehicles, wherein vehicles exchange safety-critical information with minimal delay for ensuring road safety as well as efficacious traffic flows. It is, therefore, indispensable that these safety messages are authentic and reliable, and have originated from a legitimate vehicle. This demands establishing trust among vehicles such that the malicious and dishonest vehicles (and their malicious content) could be flagged and subsequently eliminated from the network. The risk manifolds if a malicious vehicle gets elected as the cluster head of other vehicles thereby compromising the safety of vehicular passengers and pedestrians on the road. Hence, intelligent algorithms should be in place to opt for the trusted and resource efficient cluster heads which could enhance the overall security and efficiency of their respective clusters. To this end, in this paper, we have proposed a scalable hybrid trust model which takes into account a composite metric encompassing the weighted trust score and available resources of each vehicle for identification of multiple malicious vehicles in real-time, and for meeting the stringent performance requirements of vehicular safety applications. Moreover, an optimal role assignment scheme based on the Hungarian algorithm has been proposed for electing the optimal cluster head, proxy cluster head, and followers among the members of a vehicular cluster so as to maximize its overall efficiency. Preliminary simulations have been carried out and are also presented in this paper. © 2019 IEEE.

关键词： network security

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International conference on parallel and distributed Systems (ICPADS)

作者： Saiqin Long Zhetao Li Zihao Liu Qingyong Deng Sangyoon Oh Nobuyoshi Komuro Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China Ajou University Suwon South Korea Graduate School of Advanced Integration Science Chiba University Chiba Japan

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： Cloud computing Clustering algorithms Estimation Fingerprint recognition Throughput Partitioning algorithms Classification algorithms

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LoCEC: Local Community-based Edge Classification in Large Online Social networks

LoCEC: Local Community-based Edge Classification in Large On...

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International conference on Data Engineering

作者： Chonggang Song Qian Lin Guohui Ling Zongyi Zhang Hongzhao Chen Jun Liao Chuan Chen Tencent Inc National University of Singapore

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Relationships in online social networks often imply social connections in real life. An accurate understanding of relationship types benefits many applications, e.g. social advertising and recommendation. Some recent attempts have been proposed to classify user relationships into predefined types with the help of pre-labeled relationships or abundant interaction features on relationships. Unfortunately, both relationship feature data and label data are very sparse in real social platforms like WeChat, rendering existing methods inapplicable. In this paper, we present an in-depth analysis of WeChat relationships to identify the major challenges for the relationship classification task. To tackle the challenges, we propose a Local Community-based Edge Classification (LoCEC) framework that classifies user relationships in a social network into real-world social connection types. LoCEC enforces a three-phase processing, namely local community detection, community classification and relationship classification, to address the sparsity issue of relationship features and relationship labels. Moreover, LoCEC is designed to handle large-scale networks by allowing parallel and distributed processing. We conduct extensive experiments on the real-world WeChat network with hundreds of billions of edges to validate the effectiveness and efficiency of LoCEC.

关键词： Social network services Image edge detection Games Task analysis Feature extraction Advertising Rendering (computer graphics)

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Blockchain Dividing based on Node Community Clustering in Intelligent Manufacturing CPS 2

Blockchain Dividing Based on Node Community Clustering in In...

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2nd IEEE International conference on Blockchain (Blockchain)

作者： Li, Suisheng Xiao, Hong Wang, Hao Wang, Tao Qiao, Jingwei Liu, Shaofeng Guangdong Univ Technol Coll Comp Guangzhou Peoples R China Norwegian Univ Sci & Tech Dept Comp Sci Trondheim Norway Guangdong Univ Technol Coll Automat Guangzhou Peoples R China

ISBN: (纸本)9781728146935

The blockchain technology becomes a key facilitator for Intelligent Manufacturing as it enables intelligent nodes to participate in global manufacturing networks with secure ledgers and smart contracts features. However, Traditional centralized storage cannot meet performance and security requirements and fully distributed storage consumes a large amount of computing, storage and network resources, which is inefficient and difficult to implement. In this paper, we propose a clustering strategy on node community clustering by constructing a trust model based on the decentralization of blockchain technology. We introduce a multi-chain storage structure. Our experiments show that the proposed strategy reduces data synchronization time and storage space, improves system performance by enabling efficient parallel processing.

关键词： Intelligent Manufacturing Blockchain Multi-chain Strategy of Blockchain Dividing

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A modified community-level diffusion extraction in social network 20

A modified community-level diffusion extraction in social ne...

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20th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Chang, Huajian Shen, Hong School of Data and Computer Science Sun Yat-Sen University China School of Computer Science University of Adelaide Australia

ISBN: (纸本)9781728126166

Equipped with more convenient facilities and features, online social networks have become the most popular platform for people's communication. It is increasingly important to model information propagation in such networks. Most of the state-of-the-art algorithms of information diffusion model focus on individual-level diffusion and does not consider the impact of social relations on user's expression, making them either unable to uncover diffusion patterns accurately or unable to capture dynamically changing topics of text stream in social networks. To address these issues, we proposed a dynamic community-level diffusion model (DCDM) in this paper to capture diffusion patterns based on coordinated dynamic semantic analysis by multiple topic-word distribution and structure analysis. Comparative experiments are conducted on the real dataset from Tweet. Experimental results show our diffusion model outperforms the state-of-the-art methods. © 2019 IEEE.

关键词： Diffusion

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A gird-based joint routing and energy replenish scheme for rechargeable wireless sensor networks 20

A gird-based joint routing and energy replenish scheme for r...

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20th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Chen, Zhansheng Shen, Hong School of Computer and Information Technology Beijing Jiaotong University Beijing China School of Applied Science and Technology Beijing Union University Beijing China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi China School of Data and Computer Science Sun Yat-sen University Guangzhou China School of Computer Science University of Adelaide Adelaide Australia

ISBN: (纸本)9781728126166

To maintain the durability of data collection and improve charging efficiency for a mobile wireless charging vehicle (WCV) in Wireless Rechargeable Sensor networks (WRSNs), a grid-based joint routing and energy replenish scheme (GRER) is proposed in this paper, aiming to achieve energy balance and maximize recharging benefit. based on WCV charging service, network is firstly divided into several virtual grids with the help of minimum coverage area idea and a simple concurrent multi-hop chain-based routing protocol is proposed for forming data transmission links. Then, WCV visits and charges nodes using an angle expansion-based breadth-first strategy (AEBF) within the limited battery capacity. Simulation results demonstrate that GRER outperforms FCFS, NJNP, LADP and RCSS schemes in regards to network lifetime, energy balance and charging efficiency. © 2019 IEEE.

关键词： Routing protocols

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