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URMG: Enhanced CBMG-Based Method for Automatically Testing Web Applications in the Cloud

Tsinghua Science and technology 2014年第1期19卷 65-75页

作者： Xiaolin Xu Hai Jin Song Wu Lixiang Tang Yihong Wang Services Computing Technology and System Lab and Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

To satisfy the rapid growth of cloud technologies, a large number of web applications have been developed and deployed, and these applications are being run in clouds. Due to the scalability provided by clouds, a single web application may be concurrently visited by several millions or billions of users. Thus, the testing and performance evaluations of these applications are increasingly important. User model based evaluations can significantly reduce the manual work required, and can enable us to determine the performance of applications under real runtime environments. Hence, it has become one of the most popular evaluation methods in both industry and academia. Significant efforts have focused on building different kinds of models using mining web access logs, such as Markov models and Customer Behavior Model Graph （CBMG）. This paper proposes a new kind of model, named the User Representation Model Graph （URMG）, which is built based on CBMG. It uses an algorithm to refine CBMG and optimizes the evaluations execution process. Based on this model, an automatic testing and evaluation system for web applications is designed, implemented, and deployed in our test cloud, which is able to execute all of the analysis and testing operations using only web access logs. In our system, the error rate caused by random access to applications in the execution phase is also reduced, and the results show that the error rate of the evaluation that depends on URMG is 50% less than that which depends on CBMG.

关键词： cloud web application performance evaluation customer behavior user representation

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Sponge:An Oversubscription Strategy Supporting Performance Interference Management in Cloud

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China Communications 2015年第11期12卷 196-209页

作者： LIU Ying Services Computing Technology and System Lab Cluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Resource oversubscription optimizes the utilization of the computing resources. Many well-known virtual machine monitors(VMMs)such as Xen and KVM,adopt this approach to help maximize the yield of the cloud datacenters That is,with proper resource oversubscription strategies,more virtual machines(VMs) can be supported by limited resources. However performance interference among VMs hosting in the same physical machines(PMs) exists in cloud environment,and probably aggravated by resource oversubscription strategies,which aims to put more VMs into the same PM. In this paper,we present a resource oversubscription strategy called Sponge targeting cloud platforms Sponge mitigates the issue of performance interference among the oversubscribed co-hosting VMs. Sponge also provides a VM association strategy for each PM to handle with its besteffort. We performed our evaluation on a virtua datacenter simulated by Xen. Our evaluation results show that Sponge improves the resources utilization and manages to make each VM mee its performance requirement even hosting with other VMs in the same PM.

关键词： resources oversubscription performance interference cloud computing resource management

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Ciphertext-Policy Attribute-Based Encryption with Short Keys

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Chinese Journal of Electronics 2014年第4期23卷 655-660页

作者： XU Peng TANG Yong JIANG Wenbin JIN Hai ZOU Deqing Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

Ciphertext-policy attribute-based encryption(CP-ABE)allows a user with some attributes to decrypt the ciphertexts associated with these *** several CP-ABE schemes with the constant size ciphertext were proposed to reduce the communication cost,their master public and secret keys still have the size linear in the total number of *** schemes are unpractical for the attribute-scalable and many-attributes scenario.A new CP-ABE scheme is *** attribute is mapped to a mathematical value by a combination *** master public and secret keys of the proposed CP-ABE scheme have the size linear in the binary size of a hash function’s *** has the comparable performance with existing schemes in the aspects like the time costs of encryption and decryption,the expressiveness of access policy and the provable security.

关键词： Ciphertext-policy attribute-based encryption Expressiveness policy Provable security

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FunctionFlow:coordinating parallel tasks

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Frontiers of Computer Science 2019年第1期13卷 73-85页

作者： Xuepeng FAN Xiaofei LIAO Hai JIN Services Computing Technology and System Lab (SCTS)&Cluster and Grid Computing Lab (CGCL) School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

With the growing allel programming,nowadays popularity of task-based partask-parallel programming libraries and languages are still with limited support for coordinating parallel *** limitation forces programmers to use additional independent components to coordinate the parallel tasks -the components can be third-party libraries or additional components in the same programming library or ***,mixing tasks and coordination components increase the difficulty of task-based programming,and blind schedulers for understanding tasks'dependencies. In this paper,we propose a task-based parallel programming library,Function Flow,which coordinates tasks in the purpose of avoiding additional independent coordination ***,we use dependency expression to represent ubiquitous tasks'*** key idea behind dependency expression is to use && for both task's termination and Ⅱ for any task termination,along with the combination of dependency ***,as runtime support,we use a lightweight representation for dependency expression. Also,we use suspended-task queue to schedule tasks that still have prerequisites to run. Finally,we demonstrate Function Flow's effectiveness in two aspects,case study about implementing popular parallel patterns with FunctionFIow,and performance comparision with state-of-the-art practice,*** demonstration shows that FunctionFIow can generally coordinate parallel tasks without involving additional components,along with comparable performance with TBB.

关键词： task parallel programming tasks dependency FunctionFlow coordination patterns

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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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UCat: heterogeneous memory management for unikernels

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Frontiers of Computer Science 2023年第1期17卷 51-61页

作者： Chong TIAN Haikun LIU Xiaofei LIAO Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing LabSchool of Computing Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Unikernels provide an efficient and lightweight way to deploy cloud computing services in application-specialized and single-address-space virtual machines (VMs). They can efficiently deploy hundreds of unikernel-based VMs in a single physical server. In such a cloud computing platform, main memory is the primary bottleneck resource for high-density application deployment. Recently, non-volatile memory (NVM) technologies has become increasingly popular in cloud data centers because they can offer extremely large memory capacity at a low expense. However, there still remain many challenges to utilize NVMs for unikernel-based VMs, such as the difficulty of heterogeneous memory allocation and high performance overhead of address *** this paper, we present UCat, a heterogeneous memory management mechanism that support multi-grained memory allocation for unikernels. We propose front-end/back-end cooperative address space mapping to expose the host memory heterogeneity to unikernels. UCat exploits large pages to reduce the cost of two-layer address translation in virtualization environments, and leverages slab allocation to reduce memory waste due to internal memory fragmentation. We implement UCat based on a popular unikernel--OSv and conduct extensive experiments to evaluate its efficiency. Experimental results show that UCat can reduce the memory consumption of unikernels by 50% and TLB miss rate by 41%, and improve the throughput of real-world benchmarks such as memslap and YCSB by up to 18.5% and 14.8%, respectively.

关键词： unikernel virtualization non-volatile memory heterogeneous memory large page slab allocation

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Privacy preserving search over multiple servers

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International Journal of Autonomous and Adaptive Communications Systems 2017年第4期10卷 426-442页

作者： Ibrahim, Ayad Jin, Hai Yassin, Ali A. Zou, Deqing Cluster and Grid Computing Lab Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan430074 China

Search over encrypted data is a hot topic. In this paper, we propose a secure scheme for searching the encrypted servers. Such scheme enables the authorised user to search multiple servers with multi-keyword queries and retrieve the most relevant documents in a descending order with respect to their relevance to the query, without compromising the privacy of his query and the contents of documents he retrieved. In such a setting, each data owner builds his own searchable index, and associates with each document in that index its weight score, which facilitates document ranking. To protect the privacy of these weights, we utilise the privacy preserving mapping (PPM) primitive, while preserving their capability to perform the ranking process. We have conducted several empirical analyses on a real dataset to demonstrate the performance of our proposed scheme. © 2017 Inderscience Enterprises Ltd.

关键词： Search engines

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CMM :Credential Migration Management system based on trusted computing in CGSP

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中国高等学校学术文摘·计算机科学 2007年第2期1卷 200-207页

作者： JIN Hai YI Chuanjiang Services Computing Technology and System Lab Cluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

In grid computing environment,grid users often *** the original user may be under the risk of information leakage and identity abused for sending his credential to remote computing *** grid security practice has few means to enforce the security of credential *** computing (TC) technology can be added to grid computing environment to enhance the grid *** TC using an essential in-platform (trusted)third party,Trusted Platform Module (TPM),we can use TC to protect the user *** this paper we present credential migration management (CMM) system,which is a part of Daonity project,to manifest migrating credential in security between different computers with TPM.

关键词： credential migration trusted computing grid computing Chinagrid Daonity

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Exploring Semantic Association Rules Between Keyword and Visual Feature clusters for Web Image Retrieval

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电子学报(英文版) 2008年第1期17卷 59-65页

作者： JIN Hai HE Ruhan TAO Wenbing Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan 430074 China

The semantic gap is a big challenge in image retrieval area. Previous studies in web image retrieval have mainly focused on Relevance feedback (RF) and Latent semantic indexing (LSI) to alleviate the gap. This paper proposes an approach base on Frequent itemset mining (FIM) and Association rule (AR) techniques, which explores the semantic association rule between the two modalities that are represented by keyword and visual feature clusters. The rules are obtained offline based on the inverted file, and utilized in query process online to realize the integration of the two modalities of web images. Our approach improves the retrieval performance and is scalable well, as well as satisfies the requirement of the web users with no additional interactions. The experiments are carried out in our web image retrieval system named VAST (VisuAl & SemanTic image search), and the results show the effectiveness of the proposed approach.

关键词：图像处理自适应算法图像恢复语义联合规则视觉特征

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Text-augmented long-term relation dependency learning for knowledge graph representation

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High-Confidence computing 2025年

作者： Quntao Zhu Mengfan Li Yuanjun Gao Yao Wan Xuanhua Shi 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 430074 China

Knowledge graph (KG) representation learning aims to map entities and relations into a low-dimensional representation space, showing significant potential in many tasks. Existing approaches follow two categories: (1) Graph-based approaches encode KG elements into vectors using structural score functions. (2) Text-based approaches embed text descriptions of entities and relations via pre-trained language models (PLMs), further fine-tuned with triples. We argue that graph-based approaches struggle with sparse data, while text-based approaches face challenges with complex relations. To address these limitations, we propose a unified Text-Augmented Attention-based Recurrent Network, bridging the gap between graph and natural language. Specifically, we employ a graph attention network based on local influence weights to model local structural information and utilize a PLM based prompt learning to learn textual information, enhanced by a mask-reconstruction strategy based on global influence weights and textual contrastive learning for improved robustness and generalizability. Besides, to effectively model multi-hop relations, we propose a novel semantic-depth guided path extraction algorithm and integrate cross-attention layers into recurrent neural networks to facilitate learning the long-term relation dependency and offer an adaptive attention mechanism for varied-length information. Extensive experiments demonstrate that our model exhibits superiority over existing models across KG completion and question-answering tasks.

关键词： Knowledge graph representation Graph attention network Pre-trained language model Attention-based recurrent network Masked autoencoder Contrastive learning

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