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Sledge: Towards Efficient Live Migration of Docker Containers

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Sledge: Towards Efficient Live Migration of Docker Container...

IEEE International Conference on Cloud Computing, CLOUD

作者： Bo Xu Song Wu Jiang Xiao Hai Jin Yingxi Zhang Guoqiang Shi Tingyu Lin Jia Rao Li Yi Jizhong Jiang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Intelligent Manufacturing System Technology Beijing China The University of Texas at Arlington Arlington TX USA Alibaba Cloud Computing Co. Ltd. Hangzhou China

ISBN: (数字)9781728187808

ISBN: (纸本)9781728187815

Modern large-scale cloud platforms require live migration technique on Docker containers with stateful workload to support load balancing, host maintenance, and Quality of Service (QoS) improvement. Efficient and scalable Docker live migration is expected to guarantee the component-integrity (image, runtime, and management context) with negligible downtime. In this paper, we present a highly efficient live migration system called Sledge, which ensures the component-integrity by integrating both images and management context during runtime migration. The key insight is that the layered image can be leveraged to reduce the migration overhead, and appropriately selective migration of management context will effectively improve QoS with negligible downtime. To achieve good scalability, a lightweight container registry mechanism for end-to-end image migration is designed to avoid the redundant layers transmission. In addition, a dynamic context loading scheme is proposed to precisely load the management context into the running daemon, which can significantly reduce downtime. Experiments show that, compared with the state-of-the-art, Sledge reduces 57% of total migration time, 55% of image migration time, and 70% downtime.

关键词： Cloud computing Runtime Scalability Loading Quality of service Containers Maintenance engineering

Incorporating Hidden Layer representation into Adversarial Attacks and Defences

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arXiv 2020年

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

In this paper, we propose a defence strategy to improves adversarial robustness incorporating hidden layer representation. The key of this defence strategy aims to compress or filter input’s information including adversarial perturbation. And this defence strategy can be regarded as an activation function which can be applied any kind of neural networks. We also prove theoretically the effectiveness of this defense strategy under certain conditions. Besides, incorporating hidden layer representation we propose three types of adversarial attacks to generate three types of adversarial examples, respectively. The experiments show that our defence method can significantly improve the adversarial robustness of deep neural networks which achieves the state-of-the-art performance even though we do not adopt adversarial training. © 2020, CC BY.

关键词： Deep neural networks

Hierarchical clustering supported by reciprocal nearest neighbors

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arXiv 2019年

作者： Xie, Wen-Bo Lee, Yan-Li Wang, Cong Chen, Duan-Bing Zhou, Tao Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Union Big Data Tech. Inc. Chengdu610041 China CompleX Lab University of Electronic Science and Technology of China Chengdu611731 China The Center for Digitized Culture and Media University of Electronic Science and Technology of China Chengdu611731 China

Clustering is a fundamental analysis tool aiming at classifying data points into groups based on their similarity or distance. It has found successful applications in all natural and social sciences, including biology, physics, economics, chemistry, astronomy, psychology, and so on. Among numerous existent algorithms, hierarchical clustering algorithms are of a particular advantage as they can provide results under different resolutions without any predetermined number of clusters and unfold the organization of resulted clusters. At the same time, they suffer a variety of drawbacks and thus are either time-consuming or inaccurate. We propose a novel hierarchical clustering approach on the basis of a simple hypothesis that two reciprocal nearest data points should be grouped in one cluster. Extensive tests on data sets across multiple domains show that our method is much faster and more accurate than the state-of-the-art benchmarks. We further extend our method to deal with the community detection problem in real networks, achieving remarkably better results in comparison with the well-known Girvan-Newman algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Adversarial multimodal network for movie question answering

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arXiv 2019年

作者： Yuan, Zhaoquan Sun, Siyuan Duany, Lixin Wu, Xiao Xu, Changsheng School of Information Science and Technology Southwest Jiaotong University Big Data Research Center University of Electronic Science and Technology of China National Lab of Pattern Recognition Institute of Automation CAS

Visual question answering by using information from multiple modalities has attracted more and more attention in recent years. However, it is a very challenging task, as the visual content and natural language have quite different statistical properties. In this work, we present a method called Adversarial Multimodal Network (AMN) to better understand video stories for question answering. In AMN, as inspired by generative adversarial networks, we propose to learn multimodal feature representations by finding a more coherent subspace for video clips and the corresponding texts (e.g., subtitles and questions). Moreover, we introduce a self-attention mechanism to enforce the so-called consistency constraints in order to preserve the selfcorrelation of visual cues of the original video clips in the learned multimodal representations. Extensive experiments on the MovieQA dataset show the effectiveness of our proposed AMN over other published state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Video cameras

Aftershock Detection with Multi-scale Description Based Neural Network

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Aftershock Detection with Multi-scale Description Based Neur...

IEEE International Conference on data Mining (ICDM)

作者： Qi Zhang Tong Xu Hengshu Zhu Lifu Zhang Hui Xiong Enhong Chen Qi Liu Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Baidu Talent Intelligence Center Baidu Inc Business Intelligence Lab Baidu Research

Aftershocks refer to the smaller earthquakes that occur following large earthquakes, in the same area of the main shock. The task of aftershocks detection, as a crucial and challenging issue in disaster monitoring, has attracted wide research attention in relevant fields. Compared with the traditional detection methods like STA/LTA algorithms or heuristic matching, neural network techniques are regarded as an advanced choice with better pattern recognition ability. However, current neural network-based solutions mainly formulate the seismic wave as ordinary time series, where existing techniques are directly deployed without adaption, and thus fail to obtain competitive performance on the intensive and highly-noise waveforms of aftershocks. To that end, in this paper, we propose a novel framework named Multi-Scale Description based Neural Network (MSDNN) for enhancing aftershock detection. Specifically, MSDNN contains a delicately-designed network structure for capturing both short-term scale and long-term scale seismic features. Therefore, the unique characteristics of seismic waveforms can be fully-exploited for aftershock detection. Furthermore, a multi-task learning strategy is introduced to model the seismic waveforms of multiple monitoring stations simultaneously, which can not only refine the detection performance but also provide additionally quantitative clues for discovering homologous earthquakes. Finally, comprehensive experiments on the data set from aftershocks of the Wenchuan M8.0 Earthquake have clearly validated the effectiveness of our framework compared with several state-of-the-art baselines.

关键词：

FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On

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FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On

International Conference on Computer Vision (ICCV)

作者： Haoye Dong Xiaodan Liang Xiaohui Shen Bowen Wu Bing-Cheng Chen Jian Yin School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R.China School of Intelligent Systems Engineering Sun Yat-sen University ByteDance AI Lab

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Beyond current image-based virtual try-on systems that have attracted increasing attention, we move a step forward to developing a video virtual try-on system that precisely transfers clothes onto the person and generates visually realistic videos conditioned on arbitrary poses. Besides the challenges in image-based virtual try-on (e.g., clothes fidelity, image synthesis), video virtual try-on further requires spatiotemporal consistency. Directly adopting existing image-based approaches often fails to generate coherent video with natural and realistic textures. In this work, we propose Flow-navigated Warping Generative Adversarial Network (FW-GAN), a novel framework that learns to synthesize the video of virtual try-on based on a person image, the desired clothes image, and a series of target poses. FW-GAN aims to synthesize the coherent and natural video while manipulating the pose and clothes. It consists of: (i) a flow-guided fusion module that warps the past frames to assist synthesis, which is also adopted in the discriminator to help enhance the coherence and quality of the synthesized video; (ii) a warping net that is designed to warp clothes image for the refinement of clothes textures; (iii) a parsing constraint loss that alleviates the problem caused by the misalignment of segmentation maps from images with different poses and various clothes. Experiments on our newly collected dataset show that FW-GAN can synthesize high-quality video of virtual try-on and significantly outperforms other methods both qualitatively and quantitatively.

关键词： Integrated circuits Generators Gallium nitride Three-dimensional displays Image generation Computational modeling Visualization

Cost-efficient and skew-aware data scheduling for incremental learning in 5G network

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arXiv 2020年

作者： Pu, Lingjun Yuan, Xinjing Xu, Xiaohang Chen, Xu Zhou, Pan Xu, Jingdong The College of Computer Science Nankai University Tianjin300071 China The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710126 China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The Pazhou Lab. Guangzhou510335 China The Hubei Engineering Research Center on Big Data Security The School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

To facilitate the emerging applications in 5G networks, mobile network operators will provide many network functions in terms of control and prediction. Recently, they have recognized the power of machine learning (ML) and started to explore its potential to facilitate those network functions. Nevertheless, the current ML models for network functions are often derived in an offline manner, which is inefficient due to the excessive overhead for transmitting a huge volume of dataset to remote ML training clouds and failing to provide the incremental learning capability for the continuous model updating. As an alternative solution, we propose Cocktail, an incremental learning framework within a reference 5G network architecture. To achieve cost efficiency while increasing trained model accuracy, an efficient online data scheduling policy is essential. To this end, we formulate an online data scheduling problem to optimize the framework cost while alleviating the data skew issue caused by the capacity heterogeneity of training workers from the long-term perspective. We exploit the stochastic gradient descent to devise an online asymptotically optimal algorithm, including two optimal policies based on novel graph constructions for skew-aware data collection and data training. Small-scale testbed and large-scale simulations validate the superior performance of our proposed framework. © 2020, CC BY.

关键词： 5G mobile communication systems

BookChain: Library-Free Book Sharing Based on Blockchain Technology

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BookChain: Library-Free Book Sharing Based on Blockchain Tec...

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Jiajie Zeng Xiaohai Dai Jiang Xiao Wenhui Yang Weifeng Hao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728152127

ISBN: (纸本)9781728152134

Modern bookcrossing leverages the mobile networks to help readers share books via convenient connection, and thus expedites the dissemination of information. However, the lack of traceability has significantly hindered the wide adoption of mobile bookcrossing, leading to loss of books. Meanwhile, the managerial inefficiency of current mobile bookcrossing systems becomes another obstacle for advancing the book circulation. To overcome these problems, we propose to leverage the promising blockchain technology with the merits of its immutability and cryptographic smart contract. In this paper, we present BookChain, a traceable and efficient blockchain-based innercampus book sharing system. BookChain stores the complete sharing data of an interested book permanently on blockchain, such that every reader can trace the borrowing history, which reduces the potential for loss of book. BookChain also introduces the use of smart contract to automate the circulation of books with minimal human intervention, resulting in the improvement of efficiency. The experimental results show the effectiveness and low-cost of the proposed system under high concurrent users.

关键词： Contracts Web servers Web pages

Aligning cross-lingual entities with multi-aspect information

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arXiv 2019年

作者： Yang, Hsiu-Wei Zou, Yanyan Shi, Peng Lu, Wei Lin, Jimmy Sun, Xu David R. Cheriton School of Computer Science University of Waterloo StatNLP Research Group Singapore University of Technology and Design MOE Key Lab of Computational Linguistics School of EECS Peking University Center for Data Science Beijing Institute of Big Data Research Peking University

Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages. The task of cross-lingual entity alignment is to match entities in a source language with their counterparts in target languages. In this work, we investigate embedding-based approaches to encode entities from multilingual KGs into the same vector space, where equivalent entities are close to each other. Specifically, we apply graph convolutional networks (GCNs) to combine multi-aspect information of entities, including topological connections, relations, and attributes of entities, to learn entity embeddings. To exploit the literal descriptions of entities expressed in different languages, we propose two uses of a pretrained multilingual BERT model to bridge cross-lingual gaps. We further propose two strategies to integrate GCN-based and BERT-based modules to boost performance. Extensive experiments on two benchmark datasets demonstrate that our method significantly outperforms existing systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Knowledge graph