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Optimizing Multi-Dimensional Packet Classification for Multi-Core Systems

Journal of Computer Science & Technology 2018年第5期33卷 1056-1071页

作者： Tong Shen Da-Fang Zhang Gao-Gang Xie Xin-Yi Zhang College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Network Technology Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

Packet classification has been studied for decades; it classifies packets into specific flows based on a given rule set. As software-defined network was proposed, a recent trend of packet classification is to scale the five-tuple model to multi-tuple. In general, packet classification on multiple fields is a complex problem. Although most existing software-based algorithms have been proved extraordinary in practice, they are only suitable for the classic five-tuple model and difficult to be scaled up. Meanwhile, hardware-specific solutions are inflexible and expensive, and some of them are power consuming. In this paper, we propose a universal multi-dimensional packet classification approach for multi-core systems. In our approach, novel data structures and four decomposition-based algorithms are designed to optimize the classification and updating of rules. For multi-field rules, a rule set is cut into several parts according to the number of fields. Each part works independently. In this way, the fields are searched in parallel and all the partial results are merged together at last. To demonstrate the feasibility of our approach, we implement a prototype and evaluate its throughput and latency. Experimental results show that our approach achieves a 40% higher throughput than that of other decomposed-based algorithms and a 43% lower latency of rule incremental update than that of the other algorithms on average. Furthermore, our approach saves 39% memory consumption on average and has a good scalability.

关键词： multi-dimensional multi-core packet classification

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A novel combination method for conflicting evidence in open frame of discernment 17

A novel combination method for conflicting evidence in open ...

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2017 International Conference on Data Mining, Communications and Information Technology, DMCIT 2017

作者： Yang, Kehua Liu, Youfang Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronics Hunan University Changsha China

ISBN: (纸本)9781450352185

Dempster-Shafer theory is a useful method in data fusion. To avoid counter-intuitive result of classical Dempster-Shafer's combination rule dealing with highly conflict evidences, many improved combination methods have been proposed through modifying the classical Dempster-Shafer's rule or original evidence by using a certain measure of the degree of conflict or uncertain data. However, usually the frame of discernment is incomplete, the current methods have limitation in handling this *** order to solve this problem, an improved combination method for conflicting evidence in open frame of discernment is proposed. In this paper, according to information theory the information entropy of each evidence is calculated and assigned to the unknown proposition which is added into the frame of discernment. Then reliability degree of evidences obtained by the Jousselme distance is used to revise source evidences. Finally, Dempster-Shafer's rule is applied to combine the mean of revised evidences several times. One numerical example is employed and in comparison with the existing alternatives, the proposed approach can effectively solve the conflicting problem and the incompleteness of frame. © 2017 Association for computing Machinery.

关键词： Information theory

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Non-cooperative game approach for task offloading in edge clouds

arXiv

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arXiv 2018年

作者： Yang, Bo. Li, Zhiyong Liu, Wenbin College of Information and Management Hunan University of Finance and Economics Changsha410205 China College of Computer Science and Electronic Engineering of Hunan University National Supercomputing Center in Changsha Key Laboratory for Embedded and Network Computing of Hunan Province Changsha410082 China

—Task offloading provides a promising way to enhance the capability of the mobile terminal (also called terminal user) that is distributed on network edge and communicates edge clouds with wireless. Generally, there are multiple edge cloud nodes with distinct processing capability in a geographic area, which can offer computing service for various terminal users. Furthermore, the terminal users are competitive and selfish, i.e., each user takes into account only maximizing her own profit, while conducting task offloading strategies. In this paper, we focus on the resource management optimization for edge clouds, and formulate the problem of resource competition among terminal users as a non-cooperative game, in which the terminal user who acts as the player always pursues the minimization of the expected response time for her tasks by optimizing allocation strategies. We present the utility function of the user with queuing theory, and then prove the existence of Nash equilibrium for the formulated game. Using the concept of Nash bargaining solution to calculate the optimal task offloading scheme for the user, we propose a distributed task offloading algorithm with low computation complexity. The results of simulated experiments demonstrate that our method can quickly reach the Nash equilibrium point, and deliver satisfying performance at the expected response time of the user’s tasks. Copyright © 2018, The Authors. All rights reserved.

关键词： Game theory

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A rich ranking model based on the matthew effect optimization 7th

A rich ranking model based on the matthew effect optimizatio...

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7th International Conference on Computational Data and Social networks, CSoNet 2018

作者： Li, Jinzhong Liu, Guanjun Department of Computer Science and Technology College of Electronic and Information Engineering Jinggangshan University Ji’an343009 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610054 China Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030046477

Most existing approaches of learning to rank treat the effectiveness of each query equally which results in a relatively lower ratio of queries with high effectiveness (i.e. rich queries) in the produced ranking model. Such ranking models need to be further optimized to increase the number of rich queries. In this paper, queries with different effectiveness are distinguished, and the queries with higher effectiveness are given higher weights. We modify the gradient in the LambdaMART algorithm based on a new perspective of Matthew effect to highlight the optimization of the rich queries and to produce the rich ranking model, and we present a consistency theorem for the modified optimization objective. Based on the effectiveness evaluation criteria for information retrieval, we introduce the Gini coefficient, mean-variance and quantity statistics to measure the performances of the ranking models. Experimental results show that the ranking models produced by the gradient-modified LambdaMART algorithm based on Matthew effect exhibit a stronger Matthew effect compared to the original LambdaMART algorithm. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

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A high-performance CNN method for offline handwritten Chinese character recognition and visualization

arXiv

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arXiv 2018年

作者： Melnyk, Pavlo You, Zhiqiang Li, Keqin Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha410082 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Department of Computer Science State University of New York at New Paltz New PaltzNY12561 United States

Recent researches introduced fast, compact and efficient convolutional neural networks (CNNs) for offline handwritten Chinese character recognition (HCCR). However, many of them did not address the problem of network interpretability. We propose a new architecture of a deep CNN with high recognition performance which is capable of learning deep features for visualization. A special characteristic of our model is the bottleneck layers which enable us to retain its expressiveness while reducing the number of multiply-accumulate operations and the required storage. We introduce a modification of global weighted average pooling (GWAP) - global weighted output average pooling (GWOAP). This paper demonstrates how they allow us to calculate class activation maps (CAMs) in order to indicate the most relevant input character image regions used by our CNN to identify a certain class. Evaluating on the ICDAR-2013 offline HCCR competition dataset, we show that our model enables a relative 0.83% error reduction while having 49% fewer parameters and the same computational cost compared to the current state-of-the-art single-network method trained only on handwritten data. Our solution outperforms even recent residual learning approaches. Copyright © 2018, The Authors. All rights reserved.

关键词： Visualization

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A Multi-label Classifier for Human Protein Subcellular Localization Based on LSTM networks

A Multi-label Classifier for Human Protein Subcellular Local...

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2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI2018)

作者： Zhiying Gao Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Research Center of Big Data and Network Security Tongji University Key Laboratory of Embedded System and Service Computing Tongji University

Nowadays, with the increasing number of protein sequences all over the world, more and more people are paying their attention to predicting protein subcellular location. Since wet experiment is costly and time-consuming, the automatic computational methods are urgent. In this paper, we propose a variant model based on Long Short-Term Memory(LSTM) to predict protein subcellular location. In this model, we employ LSTM to capture long distance dependency features of the sequence data. Moreover, we adjust the loss function of the loss layer to solve multi-label classification problem. Experimental results demonstrate that, compared with the traditional machine learning methods, our method achieves the best performance in various evaluation metrics.

关键词： LSTM multi-label classification protein subcellular localization

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Accurate Recovery of Internet Traffic Data: A Sequential Tensor Completion Approach

Accurate Recovery of Internet Traffic Data: A Sequential Ten...

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作者： Xie, Kun Wang, Lele Wang, Xin Xie, Gaogang Wen, Jigang Zhang, Guangxing Cao, Jiannong Zhang, Dafang College of Computer Science and Electronics Engineering Hunan University Changsha410006 China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Department of Electrical and Computer Engineering State University of New York at Stony Brook Stony BrookNY11794 United States Network Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong

The inference of traffic volume of the whole network from partial traffic measurements becomes increasingly critical for various network engineering tasks, such as capacity planning and anomaly detection. Previous studies indicate that the matrix completion is a possible solution for this problem. However, as a 2-D matrix cannot sufficiently capture the spatial-temporal features of traffic data, these approaches fail to work when the data missing ratio is high. To fully exploit hidden spatial-temporal structures of the traffic data, this paper models the traffic data as a 3-way traffic tensor and formulates the traffic data recovery problem as a low-rank tensor completion problem. However, the high computation complexity incurred by the conventional tensor completion algorithms prevents its practical application for the traffic data recovery. To reduce the computation cost, we propose a novel sequential tensor completion algorithm, which can efficiently exploit the tensor decomposition result based on the previous traffic data to derive the tensor decomposition upon arriving of new data. Furthermore, to better capture the changes of data correlation over time, we propose a dynamic sequential tensor completion algorithm. To the best of our knowledge, we are the first to propose sequential tensor completion algorithms to significantly speed up the traffic data recovery process. This facilitates the modeling of Internet traffic with the tensor to well exploit the hidden structures of traffic data for more accurate missing data inference. We have done extensive simulations with the real traffic trace as the input. The simulation results demonstrate that our algorithms can achieve significantly better performance compared with the literature tensor and matrix completion algorithms even when the data missing ratio is high. © 1993-2012 IEEE.

关键词： Tensors

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Schedule length minimization of parallel applications with energy consumption constraints using heuristics on heterogeneous distributed systems

Schedule length minimization of parallel applications with e...

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作者： Xie, Guoqi Xiao, Xiongren Li, Renfa Li, Keqin College of Computer Science and Electronic Engineering Hunan University Changsha China Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China Department of Computer Science State University of New York AlbanyNY United States

Energy consumption is one of the primary design constraints in heterogeneous parallel and distributed systems ranging from small embedded devices to large-scale data centers. The problem of minimizing the schedule length of an energy consumption-constrained parallel application has been studied recently in homogeneous systems with a shared memory. To adopt the heterogeneity and distribution of high-performance computing systems, this study solves the problem of minimizing the schedule length of an energy consumption-constrained parallel application in heterogeneous distributed systems based on a dynamic voltage and frequency scaling energy-efficient design technique. The aforementioned problem is divided into 2 subproblems in this study, namely, satisfying energy consumption constraint and minimizing schedule length. The first subproblem is solved by transferring the energy consumption constraint of the application to that of each task, whereas the second subproblem is solved by heuristically scheduling each task with low time complexity. Experiments using both fast Fourier transform and Gaussian elimination parallel applications show that the actual energy consumption values do not always exceed but are close to the given energy consumption constraints. In addition, the minimum schedule lengths are generated using the proposed algorithm. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Energy utilization

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An Improved Convolutional Neural network Algorithm for Multi-Label Classification

An Improved Convolutional Neural Network Algorithm for Multi...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： Xinsheng Wang Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Research Center of Big Data and Network Security Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Recent years conventional neural network(CNN) has been applied to different natural language processing(NLP) tasks such as sentence classification, sentence modeling, etc. Some researchers use CNN to do multi-label classification but their work mainly focus on image rather than text. In this paper, we propose an improved CNN via hierarchical dirichlet process(HDP) model to deal with the multi-label classification problem in NLP. We first apply an HDP model to discard some words which are less important semantically. Then we use word embedding methods to transform words to vectors. Finally, we train CNN based on word vectors. Experimental results demonstrate that our method is superior to most traditional multi-label classification methods and TextCNN in terms of performance.

关键词： Feature extraction Training Convolutional neural networks Natural language processing Vocabulary Transforms

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The Impact of Word Segmentation on CCG-based Arabic-English SMT

The Impact of Word Segmentation on CCG-based Arabic-English ...

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2017 2nd International Conference on Artificial Intelligence: Techniques and Applications (AITA 2017)

作者： Hamdi Ahmed Rajeh Zhi-yong LI Al-Ghaili Mohammed College of Computer Science and Electronic Engineering Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province

This paper presents a comparative study of two approaches to statistical machine translation(SMT).We present a study on Factored Machine Translation for the Arabic–English pair of *** illustrate pre-processing step for the Arabic source language and the new factors which added to the English target *** experiments that injected English side by part-ofspeech(POS) tags,Combinatory Categorical Grammar(CCG) supertags and segmented Arabic sentences displayed a considerable progress in terms of the BLEU *** experiments gained the premier examine of the Baseline phrase-based models during two approaches,Segmented(#S1) and non-segmented(#S2).In both approaches,CCG models acquired the greatest BLEU *** results show that the segmented approach which consists of CCG produce the highest accuracy at 30.91%(1.84% superior than baseline).As shown,the results presented considerable improvement in translating the segmented Arabic rather than the non-segmented into English language.

关键词： Phrase-based translation model Combinatory Categorial Grammar Part-of-speech Factored translation model Word segmentation

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