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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Bing Tian Haikun Liu Yuhang Tang Shihai Xiao Zhuohui Duan Xiaofei Liao Hai Jin Xuecang Zhang Junhua Zhu Yu Zhang National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Huawei Technologies Co. Ltd Towards high-throughput and low-latency billion-scale vector search via CPU/GPU collaborative filtering and re-ranking

ISBN: (纸本)9781939133458

Approximate nearest neighbor search (ANNS) has emerged as a crucial component of database and AI infrastructure. Ever-increasing vector datasets pose significant challenges in terms of performance, cost, and accuracy for ANNS services. None of modern ANNS systems can address these issues simultaneously. In this paper, we present Fusion-ANNS, a high-throughput, low-latency, cost-efficient, and high-accuracy ANNS system for billion-scale datasets using SSDs and only one entry-level GPU. The key idea of Fusion-ANNS lies in CPU/GPU collaborative filtering and reranking mechanisms, which significantly reduce I/O operations across CPUs, GPU, and SSDs to break through the I/O performance bottleneck. Specifically, we propose three novel designs: (1) multi-tiered indexing to avoid data swapping between CPUs and GPU, (2) heuristic re-ranking to eliminate unnecessary I/Os and computations while guaranteeing high accuracy, and (3) redundant-aware I/O deduplication to further improve I/O efficiency. We implement FusionANNS and compare it with the state-of-the-art SSD-based ANNS system-SPANN and GPU-accelerated in-memory ANNS system-RUMMY. Experimental results show that FusionANNS achieves 1) 9.4-13.1× higher query per second (QPS) and 5.7-8.8× higher cost efficiency compared with SPANN; 2) and 2-4.9× higher QPS and 2.3-6.8× higher cost efficiency compared with RUMMY, while guaranteeing low latency and high accuracy.

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FedPHE: A Secure and Efficient Federated Learning via Packed Homomorphic Encryption

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Li, Yuqing Yan, Nan Chen, Jing Wang, Xiong Hong, Jianan He, Kun Wang, Wei Li, Bo Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University RiZhao Information Technology Institute Rizhao276800 China Huazhong University of Science and Technology 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 Wuhan430074 China Shanghai Jiao Tong University School of Cyber Science and Engineering Shanghai200240 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

Cross-silo federated learning (FL) enables multiple institutions (clients) to collaboratively build a global model without sharing private data. To prevent privacy leakage during aggregation, homomorphic encryption (HE) is widely used to encrypt model updates, yet incurs high computation and communication overheads. To reduce these overheads, packed HE (PHE) has been proposed to encrypt multiple plaintexts into a single ciphertext. However, the original design of PHE assumes all clients share a single private key, making the system vulnerable to security threats of ciphertexts being intercepted and decrypted by honest-but-curious clients. Also, it does not consider the heterogeneity among different clients, resulting in undermined training efficiency with slow convergence and stragglers. To address these challenges, we propose FedPHE, a secure and efficient FL framework with PHE by jointly exploiting contribution-aware secure aggregation and straggler-resistant client selection. Using CKKS with sparsification and blinding, FedPHE achieves efficient secure aggregation that allows clients to only provide obscured encrypted updates while the server can perform aggregation by accounting for contributions of local updates. To mitigate the straggler effect, we devise a perturbed sketch-based selection to cherry-pick representative clients with heterogeneous models and computing capabilities in a communication-efficient and privacy-preserving manner. We show, through rigorous security analysis and extensive experiments, that FedPHE can efficiently safeguard clients' privacy, achieve 2.45-6.56× training speedup, cut the communication overhead by 1.32-24.85×, and reduce straggler effects by 1.89-2.78×. © 2004-2012 IEEE.

关键词： Privacy by design

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Comprehensive Architecture Search for Deep Graph Neural Networks

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IEEE Transactions on Big Data 2025年

作者： Dong, Yukang Pan, Fanxing Gui, Yi Jiang, Wenbin Wan, Yao Zheng, Ran Jin, Hai National Engineering Research Center for Big Data Technology Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Service Computing Technology and System Laboratory Wuhan430074 China Huazhong University of Science and Technology Cluster and Grid Computing Laboratory Wuhan430074 China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

In recent years, Neural Architecture Search (NAS) has emerged as a promising approach for automatically discovering superior model architectures for deep Graph Neural Networks (GNNs). Different methods have paid attention to different types of search spaces. However, due to the time-consuming nature of training deep GNNs, existing NAS methods often fail to explore diverse search spaces sufficiently, which constrains their effectiveness. To crack this hard nut, we propose CAS-DGNN, a novel comprehensive architecture search method for deep GNNs. It encompasses four kinds of search spaces that are the composition of aggregate and update operators, different types of aggregate operators, residual connections, and hyper-parameters. To meet the needs of such a complex situation, a phased and hybrid search strategy is proposed to accommodate the diverse characteristics of different search spaces. Specifically, we divide the search process into four phases, utilizing evolutionary algorithms and Bayesian optimization. Meanwhile, we design two distinct search methods for residual connections (All-connected search and Initial Residual search) to streamline the search space, which enhances the scalability of CAS-DGNN. The experimental results show that CAS-DGNN achieves higher accuracy with competitive search costs across ten public datasets compared to existing methods. © 2015 IEEE.

关键词： Neural network models

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Discourage free riding in Peer-to-Peer file sharing systems with file migration and workload balancing approach

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中国高等学校学术文摘·计算机科学 2007年第4期1卷 436-449页

作者： YU Yijiao JIN Hai Services Computing Technology and System Lab Cluster and Grid Computing LabHuazhong University of Science and TechnologyWuhan 430074China

Free riding is a great challenge to the development and maintenance of Peer-to-Peer(P2P)networks.A file migration and workload balancing based approach (FMWBBA)to discourage free riding is proposed in this *** heart of our mechanism is to migrate some shared files from the overloaded peers to the neighboring free riders automatically and transparently,which enforces free riders to offer services when altruistic peers are heavily *** migration is a key issue in our approach,and some related strategies are discussed.A simulation is designed to verify this approach,and the results show that it can not only alleviate free riding,but also improve the Quality of Service(QoS)and robustness of P2P networks efficiently.

关键词： free riding free rider Peer-to-Peer(P2P)network file migration workload balancing Quality of Service(QoS)

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

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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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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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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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