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25th IEEE International Conference on Network Protocols, ICNP 2017

作者： Chen, Hanhua Zhang, Fan Jin, Hai Cluster and Grid Computing Lab Services Computing Technology and System Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781509065011

Real-world stream data with skewed distribution raises unique challenges to distributed stream processing systems. Existing stream workload partitioning schemes usually use a 'one size fits all' design, which leverage either a shuffle grouping or a key grouping strategy for partitioning the stream workloads among multiple processing units, leading to notable problems of unsatisfied system throughput and processing latency. In this paper, we show that the key grouping based schemes result in serious load imbalance and low computation efficiency in the presence of data skewness while the shuffle grouping schemes are not scalable in terms of memory space. We argue that the key to efficient stream scheduling is the popularity of the stream data. We propose and implement a differentiated distributed stream processing system, call DStream, which assigns the popular keys using shuffle grouping while assigns unpopular ones using key grouping. We design a novel efficient and light-weighted probabilistic counting scheme for identifying the current hot keys in dynamic real-time streams. Two factors contribute to the power of this design: 1) the probabilistic counting scheme is extremely computation and memory efficient, so that it can be well integrated in processing instances in the system;2) the scheme can adapt to the popularity changes in the dynamic stream processing environment. We implement the DStream system on top of Apache Storm. Experiment results using large-scale traces from real-world systems show that DStream achieves a 2.3× improvement in terms of processing throughput and reduces the processing latency by 64% compared to state-of-the-art designs. © 2017 IEEE.

关键词： Higher order statistics

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Hardware/Software Cooperative Caching for hybrid DRAM/NVM memory architectures 17

Hardware/Software Cooperative Caching for hybrid DRAM/NVM me...

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31st ACM International Conference on Supercomputing, ICS 2017

作者： Liu, Haikun Chen, Yujie Liao, Xiaofei Jin, Hai He, Bingsheng Zheng, Long Guo, Rentong Services Computing Technology and System Lab/Big Data Technology System Lab/Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computing National University of Singapore Singapore117418 Singapore

ISBN: (纸本)9781450350204

Non-Volatile Memory (NVM) has recently emerged for its nonvolatility, high density and energy efficiency. Hybrid memory systems composed of DRAM and NVM have the best of both worlds, because NVM can offer larger capacity and have near-zero standby power consumption while DRAM provides higher performance. Many studies have advocated to use DRAM as a cache to NVM. However, it is still an open problem on how to manage the DRAM cache effectively and efficiently. In this paper, we propose a novel Hardware/Software Cooperative Caching (HSCC) mechanism that organizes NVM and DRAM in a flat address space while logically supporting a cache/memory hierarchy. HSCC maintains the NVMto-DRAM address mapping and tracks the access counts of NVM pages through a moderate extension to page tables and TLBs. It significantly simplifies the hardware design and offers several optimization opportunities for cache management in software layers. We thus propose utility-based cache filtering policies to improve the efficiency of DRAM cache. Experimental results show that HSCC improves system performance by up to 9.6X (77.2% on average) and reduces energy consumption by 34.3% on average, compared to a hardware-assisted DRAM/NVM memory system. HSCC also presents 15.4% and 14.5% performance improvement against a .ataddressable memory architecture and a Row *** Locality Aware (RBLA) caching policy for hybrid memories, respectively. © 2017 Association for computing Machinery.

关键词： Dynamic random access storage

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare 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 Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University 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 Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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DroidAuditor: A framework for auditing covert communication on Android

DroidAuditor: A framework for auditing covert communication ...

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作者： Qiang, Weizhong Xin, Shifan Jin, Hai Sun, Guozhong Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Technology and System Lab Huazhong University of Science and Technology Wuhan430074 China Co. Ltd. Beijing100193 China

Exploitation of covert channels in smartphone operating systems may lead to furtive data transmission between applications with different permissions, which might threaten users' privacy. Restricting the access to shared system resources can effectively prevent the exploitation of known covert channels. However, it inevitably limits the normal usage of those resources. In this paper, we propose a general method that detects covert channel attack at runtime without impacting the accessibility of shared resources in the system. The main idea of the method is to track and audit the use of system resources known as potential covert channel variables and impose interferences on those channels to reduce their capacity once violations are detected. We implement a prototype framework, which is able to audit and interfere covert communication both in the application layer and in the native layer of Android. The experimental results demonstrate that our method can effectively reduce the data rate of user-defined covert channels while the overhead is negligible. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Android (operating system)

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VulDeePecker: A deep learning-based system for vulnerability detection

arXiv

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arXiv 2018年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Ou, Xinyu Jin, Hai Wang, Sujuan Deng, Zhijun Zhong, Yuyi Services Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Security and Computer Hebei University Shenzhen Huazhong University of Science and Technology Research Institute Department of Computer Science University of Texas at San Antonio

The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability Database but were "silently" patched by the vendors when releasing later versions of these products;in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with. Copyright © 2018, The Authors. All rights reserved.

关键词： Computer software

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Sysevr: A framework for using deep learning to detect software vulnerabilities

arXiv

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arXiv 2018年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Jin, Hai Zhu, Yawei Chen, Zhaoxuan Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by many vulnerabilities reported on a daily basis. This calls for machine learning methods to automate vulnerability detection. Deep learning is attractive for this purpose because it does not require human experts to manually define features. Despite the tremendous success of deep learning in other domains, its applicability to vulnerability detection is not systematically understood. In order to fill this void, we propose the first systematic framework for using deep learning to detect vulnerabilities. The framework, dubbed Syntax-based, Semantics-based, and Vector Representations (SySeVR), focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities. Our experiments with 4 software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability Database. Among these 15 vulnerabilities, 7 are unknown and have been reported to the vendors, and the other 8 have been "silently" patched by the vendors when releasing newer versions of the products. Copyright © 2018, The Authors. All rights reserved.

关键词： Syntactics

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Theory and methodology of research on cloud security

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Science China(Information Sciences) 2016年第5期59卷 15-17页

作者： Hai JIN Weiqi DAI Deqing ZOU Services Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

Cloud computing is a model for enabling ubiquitous,convenient,on-demand access to a shared pool of configurable computing *** to Gartner’s survey,cloud computing has been at the head of a list of the top ten IT strategic technologies for some *** computing is currently undergoing vigorous development and promotion by many leading Chinese and foreign enterprises,and it has become the basis for a new

关键词： TPM Theory and methodology of research on cloud security

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A Protective Mechanism for the Access Control System in the Virtual Domain

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China Communications 2016年第11期13卷 129-142页

作者： Jinan Shen Deqing Zou Hai Jin Kai Yang Bin Yuan Weiming Li Services Computing Technology and System Laboratory Cluster and Grid Computing LaboratorySchool of Computing Science and TechnologyHuazhong University of Science and Technology School of Information Engineering Hubei University for Nationalities

In traditional framework,mandatory access control(MAC) system and malicious software are run in kernel mode. Malicious software can stop MAC systems to be started and make it do invalid. This problem cannot be solved under the traditional framework if the operating system(OS) is comprised since malwares are running in ring 0 level. In this paper,we propose a novel way to use hypervisors to protect kernel integrity and the access control system in commodity operating systems. We separate the access control system into three parts: policy management(PM),security server(SS) and policy enforcement(PE). Policy management and the security server reside in the security domain to protect them against malware and the isolation feather of the hypervisor can protect them from attacks. We add an access vector cache(AVC) between SS and PE in the guest OS,in order to speed up communication between the guest OS and the security domain. The policy enforcement module is retained in the guest OS for performance. The security of AVC and PE can be ensured by using a memory protection mechanism. The goal of protecting the OS kernel is to ensure the security of the execution path. We implementthe system by a modified Xen hypervisor. The result shows that we can secure the security of the access control system in the guest OS with no overhead compared with modules in the latter. Our system offers a centralized security policy for virtual domains in virtual machine ***: hypervisor; virtualization; memo-

关键词： hypervisor virtualization memory protection guest OS access control system

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Exploring the Impact of Processing Guarantees on Performance of Stream Data Processing

Exploring the Impact of Processing Guarantees on Performance...

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2017 17th IEEE International Conference on Communication technology (ICCT 2017)

作者： Syed Muhammad Abrar Akber Changfu Lin Hanhua Chen Fan Zhang Hai Jin Cluster and Grid Computing Lab Services Computing Technology and System Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology

Processing semantics ensure tuple processing guarantees during system recovery from failures. It determines whether particular tuples are re-processed or not upon system recovery. Processing semantics facilitate to maintain real-time response as well as correctness of results in stream data processing. Existing literature is in real deficit to provide sufficient resources exclusively on processing semantics. This work focuses on reviewing processing semantics in stream data processing *** article aims at exploring the impact of processing guaran-tees on performance of stream data processing. We summarize current research directions to guarantee processing semantics,based on prominent stream processing engines. Furthermore,we compare throughput for a couple of representative stream processing engines under all possible processing *** experiments show that at-most once processing guarantee provides the highest throughput among other guarantees. Lastly,challenges for further research are also discussed in this paper.

关键词： stream data processing processing semantic processing guarantee at-least once at-most once exactly-once

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Scalable Hash Ripple Join on Spark

Scalable Hash Ripple Join on Spark

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Hao Liu Jiang Xiao Fengchao Peng The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Services Computing Technology and System Lab Cluster and Grid Computing Lab Shool of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (纸本)9781538632086

Hash Ripple join is an online aggregation algorithm that can rapidly give good approximate join results increases with the progress of the join operation and converges to the real result when the join finishes. Luo et al. proposed a parallel hash ripple join (PHRJ) that runs in a distributed setting. However, the PHRJ has two drawbacks when handling large-scale data: 1) PHRJ updates approximate results in fine grain which induces extra communication cost in a distributed environment 2) When data is out of memory, PHRJ cannot provide unbiased approximate result. In this thesis, a scalable hash ripple join is proposed that 1) runs on a distributed framework that can process distributed data in coarse-grain to speed up the join performance; 2) continuously gives unbiased and consistent approximate join results even in the presence of memory overflow; and 3) has good scalability handling growing amount of data. We have implemented a prototype of the scalable hash ripple join (SHRJ) algorithm on Spark. Experiment results show that SHRJ can give good approximate join result while taking less than 10% of the time of Spark's own join operator.

关键词： Sparks Distributed databases Approximation algorithms clustering algorithms Indexes Standards Computer science

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