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arXiv 2020年

作者： Wan, Yao He, Yang Zhang, Jian-Guo Sui, Yulei Jin, Hai Xu, Guandong Xiong, Caiming Yu, Philip S. 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 School of Computer Science University of Technology Sydney NSW Australia Department of Computer Science University of Illinois at Chicago IL United States Salesforce Research Palo Alto United States

We present NATURALCC, an efficient and extensible toolkit to bridge the gap between natural language and programming language, and facilitate the research on big code analysis. Using NATURALCC, researchers both from natural language or programming language communities can quickly and easily reproduce the state-of-the-art baselines and implement their approach. NATURALCC is built upon Fairseq and PyTorch, providing (1) an efficient computation with multi-GPU and mixed-precision data processing for fast model training, (2) a modular and extensible framework that makes it easy to reproduce or implement an approach for big code analysis, and (3) a command line interface and a graphical user interface to demonstrate each model’s performance. Currently, we have included several state-of-the-art baselines across different tasks (e.g., code completion, code comment generation, and code retrieval) for demonstration. The video of this demo is available at https://***/watch?v=q4W5VSI-u3E&t=25s. © 2020, CC BY.

关键词： Natural language processing systems

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Sledge: Towards Efficient Live Migration of Docker Containers

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

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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

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Minimum Spanning Tree clustering Based on Density Filtering 7th

Minimum Spanning Tree Clustering Based on Density Filtering

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7th CCF Academic Conference on BigData, CCF BigData 2019

作者： Wang, Ke Xie, Xia Sun, Jiayu Cao, Wenzhi 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 Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hunan University of Technology and Business Changsha410205 China

ISBN: (纸本)9789811518980

clustering analysis is an important method in data mining. In order to recognize clusters with arbitrary shapes as well as clusters with different density, we propose a new clustering approach: minimum spanning tree clustering based on density filtering. It masks the low-density points in the density filtering step, which reduces the interference of noise and makes the gap between clusters clearer. It uses relative values of adjacent distances to find mutations of density and changes between clusters to divide data sets. It is tested on multiple synthetic data sets and real-world data sets, the results of which show that the algorithm is able to detect clusters with arbitrary shape and it is insensitive to the imbalance of density between clusters. It has achieved great results on multiple data sets. © Springer Nature Singapore Pte Ltd 2019.

关键词： Data mining

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N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Improve Docker Performance 16th

N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Impro...

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16th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2019

作者： Gu, Lin Tang, Qizhi Wu, Song Jin, Hai Zhang, Yingxi Shi, Guoqiang Lin, Tingyu Rao, Jia 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 Wuhan430074 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China The University of Texas at Arlington ArlingtonTX76019 United States

ISBN: (纸本)9783030307080

Docker has been widely adopted in production environment, but unfortunately deployment and cold-start of container are limited by the low speed of disk. The emerging non-volatile memory (NVM) technology, which has high speed and can store data permanently, brings a new chance to accelerate the deployment and cold-start of container. However, it is expensive to replace the whole hard disk driver (HDD) with NVM. To achieve the fastest deployment and cold-start with lowest cost, we conduct in-depth analysis on the Top-134 images in Docker Hub and obtain two main insights as: (1) the storing latency of layered image has become the bottleneck of container deployment;(2) only a few image layers are required for container cold-start. Based on these two findings, we propose a NVM-HDD hybrid docker storage framework as N-Docker. It can effectively accelerate container cold-start by detecting the bottleneck layers as well as cold-start required layers and storing them into NVM for faster container startup with limited NVM capacity. Experimental results show that N-Docker can accelerate the container deployment by 1.21X and cold-start by 2.96X. Compared to NVM-Docker, which stores all images into NVM, N-Docker achieves the same performance improvements while reducing the usage of NVM by 88.22%. © 2019, IFIP International Federation for Information Processing.

关键词： Containers

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Shattered chain of trust: understanding security risks in cross-cloud IoT access delegation 20

Shattered chain of trust: understanding security risks in cr...

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Proceedings of the 29th USENIX Conference on Security Symposium

作者： Bin Yuan Yan Jia Luyi Xing Dongfang Zhao XiaoFeng Wang Deqing Zou Hai Jin Yuqing Zhang School of Cyber Science and Engineering Huazhong Univ. of Sci. & Tech. China and National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab and Big Data Security Engineering Research Center and Indiana University Bloomington and Shenzhen Huazhong University of Science and Technology Research Institute China School of Cyber Engineering Xidian University China and National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences China and Indiana University Bloomington Indiana University Bloomington School of Cyber Science and Engineering Huazhong Univ. of Sci. & Tech. China and National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab and Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong Univ. of Sci. & Tech. China and National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab and Big Data Security Engineering Research Center Huazhong Univ. of Sci. & Tech. China National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences China and School of Cyber Engineering Xidian University China

ISBN: (纸本)9781939133175

IoT clouds facilitate the communication between IoT devices and users, and authorize users' access to their devices. In this paradigm, an IoT device is usually managed under a particular IoT cloud designated by the device vendor, e.g., Philips bulbs are managed under Philips Hue cloud. Today's mainstream IoT clouds also support device access delegation across different vendors (e.g., Philips Hue, LIFX, etc.) and cloud providers (e.g., Google, IFTTT, etc.): for example, Philips Hue and SmartThings clouds support to delegate device access to another cloud such as Google Home, so a user can manage multiple devices from different vendors all through Google Home. Serving this purpose are the IoT delegation mechanisms developed and utilized by IoT clouds, which we found are heterogeneous and ad-hoc in the wild, in the absence of a standardized delegation protocol suited for IoT environments. In this paper, we report the first systematic study on real-world IoT access delegation, based upon a semi-automatic verification tool we developed. Our study brought to light the pervasiveness of security risks in these delegation mechanisms, allowing the adversary (e.g., Airbnb tenants, former employees) to gain unauthorized access to the victim's devices (e.g., smart locks) or impersonate the devices to trigger other devices. We confirmed the presence of critical security flaws in these mechanisms through end-to-end exploits on them, and further conducted a measurement study. Our research demonstrates the serious consequences of these exploits and the security implications of the practice today for building these mechanisms. We reported our findings to related parties, which acknowledged the problems. We further propose principles for developing more secure cross-cloud IoT delegation services, before a standardized solution can be widely deployed.

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China 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 Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Context aware 3d unet for brain tumor segmentation

arXiv

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arXiv 2020年

作者： Ahmad, Parvez Qamar, Saqib Shen, Linlin Saeed, Adnan 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 Wuhan430074 China Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China School of Hydropower and Information Technology Huazhong University of Science and Technology Wuhan430074 China

Deep convolutional neural network (CNN) achieves remarkable performance for medical image analysis. UNet is the primary source in the performance of 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates features from both encoder and decoder paths to extract multi-contexual information from image data. The multi-scaled features play an essential role in brain tumor segmentation. However, the limited use of features can degrade the performance of the UNet approach for segmentation. In this paper, we propose a modified UNet architecture for brain tumor segmentation. In the proposed architecture, we used densely connected blocks in both encoder and decoder paths to extract multi-contexual information from the concept of feature reusability. The proposed residual inception blocks (RIB) are used to extract local and global information by merging features of different kernel sizes. We validate the proposed architecture on the multimodal brain tumor segmentation challenges (BRATS) 2020 testing dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancement tumor (ET) are 89.12%, 84.74%, and 79.12%, respectively. Our proposed work is in the top ten methods based on the dice scores of the testing dataset. © 2020, CC-BY.

关键词： Tumors

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High performance RDF updates with triplebit + 13

High performance RDF updates with triplebit +

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13th International Conference on Digital Information Management, ICDIM 2018

作者： Yuan, Pingpeng Fan, Lijian Jin, Hai Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781538652435

The volume of RDF data continues to grow over the past decade and many known RDF datasets have billions of triples. A grant challenge of managing this huge RDF data is how to access this big RDF data efficiently. A popular approach to addressing the problem is to build a full set of permutations of (S, P, O) indexes. Although this approach has shown to accelerate joins by orders of magnitude, the large space overhead limits the scalability of this approach and makes it heavyweight. In this paper, we present TripleBit +, a fast and compact system for updating RDF data. The design of TripleBit + has two salient features. First, the efficient maintenance strategies of TripleBit + reduces both the overhead to update data and indexes. Second, effective maintenance technologies to handle online updates over RDF repositories are proposed. Our experiments show that TripleBit + outperforms RDF-3X, MonetDB, BitMat on LUBM, UniProt, and BTC 2012 benchmark queries and it offers orders of mangnitude performance improvement for some complex join queries. Our design also yields high task rates as high as 660,000 per second and fast average response time of task which is faster than x-RDF-3X and PostgreSQL. © 2018 IEEE.

关键词： Benchmarking

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Minimizing Overheads of Checkpoints in Distributed Stream Processing Systems 7

Minimizing Overheads of Checkpoints in Distributed Stream Pr...

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7th IEEE International Conference on Cloud Networking, CloudNet 2018

作者： Muhammad Abrar Akber, Syed Chen, Hanhua Wang, Yonghui Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab Services Computing Technology and System Lab Big Data Technology and System Lab Wuhan430074 China

ISBN: (纸本)9781538668313

Failure occurrence in large-scale systems is inevitable, which makes the resilience a key challenge for modern systems. Checkpoints with rollback recovery is a well-known approach to provide fault tolerance in distributed systems. The checkpoint based fault tolerance approach periodically persists the application state to reliable storage, which serves as a recovery point in case of failure. These periodic checkpoints are not inline with the failure rate of the systems as many studies conclude that failure occurrence is not periodic. The optimal size of checkpoint interval is a crucial decision, which directly determines the checkpoint overheads. To minimize the checkpoint overheads, we propose to reduce the number of checkpoints during the application execution. We suggest reducing the number of checkpoints by successively increasing the checkpoint intervals. We consider the failure probability of the underlying infrastructure and iteratively increase the checkpoint intervals. The proposed checkpoint approach tailors the checkpoint initializing based on the failure probability. If failure probability is low, it increases the checkpoint interval, and eventually reduces the total number of checkpoints triggered during application timespan. Reducing the total number of checkpoints during application execution results in decreasing the checkpoint overheads. The experiment results show that the proposed checkpoint policy considerably reduces the checkpoint overheads as compared to periodic checkpoints. © 2018 IEEE.

关键词： Fault tolerance

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BookChain: Library-Free Book Sharing Based on Blockchain Technology

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

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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

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