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2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA 2021

作者： Wang, Qinggang Zheng, Long Huang, Yu Yao, Pengcheng Gui, Chuangyi Liao, Xiaofei Jin, Hai Jiang, Wenbin Mao, Fubing National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology China

ISBN: (纸本)9781450382182

Existing FPGA-based graph accelerators, typically designed for static graphs, rarely handle dynamic graphs that often involve substantial graph updates (e.g., edge/node insertion and deletion) over time. In this paper, we aim to fill this gap. The key innovation of this work is to build an FPGA-based dynamic graph accelerator easily from any off-the-shelf static graph accelerator with minimal hardware engineering efforts (rather than from scratch). We observe\em spatial similarity of dynamic graph updates in the sense that most of graph updates get involved with only a small fraction of vertices. We therefore propose an FPGA library, called GraSU, to exploit spatial similarity for fast graph updates. GraSU uses a differential data management, which retains the high-value data (that will be frequently accessed) in the specialized on-chip UltraRAM while the overwhelming majority of low-value ones reside in the off-chip memory. Thus, GraSU can transform most of off-chip communications arising in dynamic graph updates into fast on-chip memory accesses. Our experiences show that GraSU can be easily integrated into existing state-of-the-art static graph accelerators with only 11 lines of code modifications. Our implementation atop AccuGraph using a Xilinx Alveo#8482;\U250 board outperforms two state-of-the-art CPU-based dynamic graph systems, Stinger and Aspen, by an average of 34.24× and 4.42× in terms of update throughput, improving further overall efficiency by 9.80× and 3.07× on average. © 2021 ACM.

关键词： Field programmable gate arrays (FPGA)

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Robust image document authentication code with autonomous biometrie key generation, selection, and updating in cloud environment 11

Robust image document authentication code with autonomous bi...

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11th International Conference on Information Assurance and Security, IAS 2015

作者： Abduljabbar, Zaid Ameen Jin, Hai Hussien, Zaid Alaa Yassin, Ali A. Hussain, Mohammed Abdulridha Abbdal, Salah H. Zou, Deqing Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China University of Basrah Basrah Iraq Southern Technical University Basrah Iraq

ISBN: (纸本)9781467387156

Recently, security issues are obstructing the development and using of cloud computing services. Authentication and integrity play an important role in the cloud security, and numerous concerns have been raised to recognize any tampering with exchanges of the image document between two entities (sender and receiver) within the cloud environment. However, none of the existing solutions reduce the probability of known attacks by combining cryptographic hash function with a strong factor that should be periodically changed. For this reason, in this paper we propose a robust one-time image document authentication scheme based on combining non-interactive onetime biometric key and a robust wavelet-based cryptographic hashing scheme. The result of the combination is one-time image document authentication code (OMAC). OMAC is hidden in an image document as a cover image through reversible data embedding steganography. The proposed scheme has several important security attributes, such as key agreement, biometric key management, robust OMAC, invulnerability, and efficiency. In biometric key management, key generation, key selection, and key update algorithms are performed autonomously by the sender and the receiver;thus, no interaction between them is needed. © 2015 IEEE.

关键词： Authentication

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AutoCSD: Automatic cloud system deployment in data centers 2nd

AutoCSD: Automatic cloud system deployment in data centers

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2nd International Conference on Cloud computing and Big Data, CloudCom-Asia 2015

作者： Xie, Tao Chen, Haibao 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 and Information Engineering Chuzhou University Chuzhou239000 China

ISBN: (纸本)9783319284293

It is a huge challenge to deploy a cloud computing system in large-scale data centers. In order to help resolve this issue, we propose an automatic cloud system deployment approach with the characteristics of reliability, availability, and load balance. Specifically, we use workflow to deal with the dependencies among the automatic deployment processes of a cloud system. We also design a failover mechanism to avoid the single point failure of the deployment server. Besides, we adopt a load balancing algorithm to solve the bottleneck problem of deploying a cloud system. We implement a prototype, and evaluate it with 16 physical nodes as well as a virtualized environment with 160 virtual machines. Experimental results show that the average deployment time under our approach is lower than that with traditional deployment methods. In addition, it achieves a cloud system deployment success ratio of up to 90%, even in the high-concurrency environment. © Springer International Publishing Switzerland 2015.

关键词： Cloud computing

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Resource allocation in contending virtualized environments through stochastic virtual machine performance modeling and feedback

Resource allocation in contending virtualized environments t...

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作者： Jiang, Cong-Feng Wan, Jian Xu, Xiang-Hua Zhang, Ji-Lin You, Xin-Dong Grid and Services Computing Technology Lab Hangzhou Dianzi University Hangzhou 310037 China

In virtualized systems, allocation and scheduling of resources shared among multiple virtual machines faces challenges such as autonomy, isolation and high workload dynamics. The multiplexing and consolidation nature of virtualized systems also raise issues such as interference and conflicts among various virtual machine instances. Therefore traditional resource allocation strategy can't achieve good performance without modifications according to these particular characteristics in virtualized systems. In this paper we use a stochastic model to characterize the resources (especially CPU) and workload dynamics. Then we use a weighted priority based service differentiation strategy to allocate resources in contending conditions to provide performance guarantees as well as load balance and fairness. In the proposed algorithms user behavior and workloads are characterized through the historical and real time performance profiling and estimation from hosted agents within individual Virtual Machines. The resources are allocated according to the demand as well as the performance of the targeted Virtual Machines based on the Sufferage aggregation and performance feedback. Experiments on a real Xen based virtualization environment with 20 Virtual Machines are conducted and evaluated for accuracy, efficiency, sensitivity, and overhead. The results show that the performance feedback based allocation can achieve a higher SLA satisfaction rate as 97.1%, a lower load imbalance index as 18.7%. The performance feedback based allocator uses 14.06% less CPU time for CPU-intensive applications and reduces 45.59% I/O wait time in disk contention environments. The results also show that the feedback based algorithm is valid, effective and scalable for implementation in real virtualized environments.

关键词： Virtual machine

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Demystifying Ethereum account diversity:observations,models and analysis

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Frontiers of Computer Science 2022年第4期16卷 119-130页

作者： Chaofan Wang Xiaohai Dai Jiang Xiao Chenchen Li Ming Wen Bingbing Zhou Hai Jin Clusters and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Centre for Distributed and High Performance Computing School of Information TechnologiesThe University of SydneyCamperdownNew South Wales2006Australia

Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart *** massive accounts can be divided into diverse categories,such as miners,tokens,and exchanges,which is termed as account diversity in this *** benefit of investigating diversity are multi-fold,including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal ***,the exploration of blockchain account diversity remains *** the most relevant studies,which focus on the deanonymization of the accounts on Bitcoin,can hardly be applied on Ethereum since their underlying protocols and user idioms are *** this end,we present the first attempt to demystify the account diversity on *** key observation is that different accounts exhibit diverse behavior patterns,leading us to propose the heuristics for classification as the *** then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation(MLE).We collect real-world data through extensive efforts to evaluate our proposed method and show its ***,we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage *** for the former,we validate two sweeping statements reliably:(1)standalone miners are gradually replaced by the mining pools and cooperative miners;(2)transactions related to the mining pool and exchanges take up a large share of the total *** latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.

关键词： blockchain Ethereum classification account diversity network analysis

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Efficient subgraph search on large anonymized graphs

Efficient subgraph search on large anonymized graphs

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作者： Ding, Xiaofeng Ou, Yangling Jia, Jianhong Jin, Hai Liu, Jixue 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 Information Technology & Mathematical Sciences University of South Australia Adelaide Australia

Graph is one of the most important data structures to model social networks and becomes popular to find interesting relationships between individuals. Since graphs may contain sensitive information, data curators usually need to anonymize the graph before publication to prevent individual re-identification, which thus leads to plenty of anonymized graphs for data sharing and exploration. However, the new structures and properties of anonymized graphs make the traditional graph indexing method inefficient or even invalid for query processing. To address the subgraph query problem over anonymized graph database, in this paper, we first introduce basic concepts about anonymized graphs and subgraph queries, then propose an index structure named Closure+-tree to process the subgraph query efficiently. In particular, graphs were organized hierarchically that each node is an union of its child nodes under some specified mapping functions. During the processing of subgraph queries, the whole graph descendants will be pruned if their union does not contain the query graph. To evaluate the performance of our proposed Closure+-tree, extensive experiments are performed on both real and synthetic graph data sets. The experimental results revealed that our index structure can prune up to 80% unqualified graphs with variable size of queries. Furthermore, the size of our index structure is only around a quarter of the entire anonymized graph data set, which indicates good scalability over large data sets. © 2018 John Wiley & Sons, Ltd.

关键词： Query processing

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CNLoc: Channel state information assisted indoor WLAN localization using nomadic access points 1

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15th IFIP International Conference on Network and Parallel computing, NPC 2018

作者： Xiao, Jiang Li, Huichuwu Li, He Jin, Hai 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 Department of Information and Electronic Engineering Muroran Institute of Technology MuroranHokkaido Japan

ISBN: (数字)9783030056773

ISBN: (纸本)9783030056766

Wireless local area network (WLAN) based indoor localization is expanding its fast-paced adoption to facilitate a variety of indoor location-based services (ILBS). Unfortunately, the performance of current WLAN localization systems relying on fixed access points (APs) deployment is constrained by the spatial localizability variance (SLV) problem that different locations may exhibit significantly distinct localization resolution. Prior approaches tackle this problem through nomadic APs with favorable mobility to dynamically adjust the network topology. However, the lack of prior knowledge of nomadic AP’s position has been a challenge for location distinction and will lead to prohibitive performance degradation. In this paper, we propose and develop CNLoc, a novel CSI-based (Channel State Information) indoor WLAN localization framework to overcome the location uncertainty of nomadic APs. Our implementation and evaluation show that CNLoc can improve the accuracy with unknown location information of nomadic APs. We also discuss some open issues and new possibilities in future nomadic AP based indoor localization. © IFIP International Federation for Information Processing 2018.

关键词： Channel state information

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FedRKG: A Privacy-Preserving Federated Recommendation Framework via Knowledge Graph Enhancement 18th

FedRKG: A Privacy-Preserving Federated Recommendation Framew...

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18th International Conference on Green, Pervasive, and Cloud computing, GPC 2023

作者： Yao, Dezhong Liu, Tongtong Cao, Qi Jin, Hai 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 Computing Science University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9789819998951

Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recently, Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture high-order interactions between users and items. However, privacy concerns prevent the global sharing of the entire user-item graph. To address this limitation, some methods create pseudo-interacted items or users in the graph to compensate for missing information for each client. Unfortunately, these methods introduce random noise and raise privacy concerns. In this paper, we propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information, enabling higher-order user-item interactions. On the client side, a relation-aware GNN model leverages diverse KG relationships. To protect local interaction items and obscure gradients, we employ pseudo-labeling and Local Differential Privacy (LDP). Extensive experiments conducted on three real-world datasets demonstrate the competitive performance of our approach compared to centralized algorithms while ensuring privacy preservation. Moreover, FedRKG achieves an average accuracy improvement of 4% compared to existing federated learning baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Recommender systems

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Public auditing for secure data storage in cloud through a third party auditor using modern ciphertext 11

Public auditing for secure data storage in cloud through a t...

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11th International Conference on Information Assurance and Security, IAS 2015

作者： Hussien, Zaid Alaa Jin, Hai Abduljabbar, Zaid Ameen Yassin, Ali A. Hussain, Mohammed Abdulridha Abbdal, Salah H. Zou, Deqing Cluster and Grid Computing Laboratory Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China University of Basrah Basrah Iraq

ISBN: (纸本)9781467387156

Outsourced data in cloud and computation results are not always trustworthy because data owners lack physical possession and control over the data as a result of virtualization, replication, and migration techniques. Protecting outsourced data from security threats has become a challenging and potentially formidable task in cloud computing;hence, many schemes have focused on ameliorating this problem and on enabling public auditability for cloud data storage security. These schemes drop into two categories: total computation cost and burden on client side. Researchers have used bilinear map technology with public key cryptography. Although this technology is highly efficient, computation time is long and overhead cost is high. The client needs to perform numerous computations to ensure the integrity of data storage. To reduce auditing cost, we propose an efficient and robust scheme to maintain data integrity in cases that involve public auditing. Our scheme adopts modern cipher cryptography with a cryptographic hash function. We consider allowing a third party auditor to preprocess data on behalf of cloud users before uploading them to cloud service providers and then verifying data integrity afterward. Our proposed scheme has important security characteristics, such as privacy, key management, low cost computation, key exchange, low overhead cost, no burden on client side, inability of cloud service providers to create correct verifier respond without data, and one-time key. Finally, efficiency analysis shows that our scheme is faster and more cost-efficient than the bilinear map-based scheme. © 2015 IEEE.

关键词： Public key cryptography

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HCOpt: An automatic optimizer for configuration parameters of Hadoop 2nd

HCOpt: An automatic optimizer for configuration parameters o...

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2nd International Conference on Human Centered computing, HCC 2016

作者： Zhang, Xiong Zeng, Linxi Shi, Xuanhua Wu, Song Xie, Xia Jin, Hai Services Computing Technology and System Laboratory Big Data Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783319318530

MapReduce is an efficient tool for data-intensive applications. Hadoop, an open-source implementation of MapReduce, has been widely adopted and experienced by some enterprises and scientific computing communities. However, when users intend to run a MapReduce program in Hadoop, they have to set a number of configuration parameters to make sure the program runs efficiently. Users often run into performance problems because they are unaware of how to set these parameters. To address these performance problems, we focus on the optimization opportunities presented by the high configurability of Hadoop, and propose an automation tool named HCOpt for performance optimization of Hadoop configuration parameters. HCOpt uses a Profile Engine to collect monitoring information from running MapReduce programs, a Prediction Engine to estimate the performance of a given Hadoop configuration and a genetic-based search algorithm to find an optimized configuration in the large search space. Our evaluation shows that HCOpt reduces the job completion time of Hadoop applications by up to 20 % when compared to applications run with configuration parameters that suggested by the rule-based optimization. © Springer International Publishing Switzerland 2016.

关键词： MapReduce

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