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arXiv 2023年

作者： Xue, Runzhen Han, Dengke Yan, Mingyu Zou, Mo Yang, Xiaocheng Wang, Duo Li, Wenming Tang, Zhimin Kim, John Ye, Xiaochun Fan, Dongrui The State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100045 China The University of Chinese Academy of Sciences Beijing101408 China The Shanghai Innovation Center for Processor Technologies Shanghai201203 China The School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon305701 Korea Republic of

Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both structural and semantic information in HetGs, HGNNs first aggregate the neighboring feature vectors for each vertex in each semantic graph and then fuse the aggregated results across all semantic graphs for each vertex. Unfortunately, existing graph neural network accelerators are ill-suited to accelerate HGNNs. This is because they fail to efficiently tackle the specific execution patterns and exploit the high-degree parallelism as well as data reusability inside and across the processing of semantic graphs in HGNNs. In this work, we first quantitatively characterize a set of representative HGNN models on GPU to disclose the execution bound of each stage, inter-semantic-graph parallelism, and inter-semantic-graph data reusability in HGNNs. Guided by our findings, we propose a high-performance HGNN accelerator, HiHGNN, to alleviate the execution bound and exploit the newfound parallelism and data reusability in HGNNs. Specifically, we first propose a bound-aware stage-fusion methodology that tailors to HGNN acceleration, to fuse and pipeline the execution stages being aware of their execution bounds. Second, we design an independency-aware parallel execution design to exploit the inter-semantic-graph parallelism. Finally, we present a similarity-aware execution scheduling to exploit the inter-semantic-graph data reusability. Compared to the state-of-the-art software framework running on NVIDIA GPU T4 and GPU A100, HiHGNN respectively achieves an average 40.0× and 8.3× speedup as well as 99.59% and 99.74% energy reduction with quintile the memory bandwidth of GPU A100. Copyright © 2023, The Authors. All rights reserved.

关键词： Reusability

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Secure key-aggregation authorized searchable encryption

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science China(Information sciences) 2019年第3期62卷 220-222页

作者： Haijiang WANG Xiaolei DONG Zhenfu CAO Dongmei LI Nanyuan CAO School of Information and Electronic Engineering Zhejiang University of Science and Technology Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Lab for Trustworthy Computing East China Normal University

Dear editor,Selective sharing of encrypted documents with other users makes the cloud storage system more flexible and effective [1]. For safety reasons, data owners use different keys to encrypt different documents. ... 详细信息

关键词： MSK ID length

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Joint Task Scheduling and Container Image Caching in Edge computing

Joint Task Scheduling and Container Image Caching in Edge Co...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Fangyi Mou Zhiqing Tang Jiong Lou Jianxiong Guo Wenhua Wang Tian Wang Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China Institute of Artificial Intelligence and Future Networks Beijing Normal University China Yunnan Key Laboratory of Software Engineering Kunming Yunnan China Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

In Edge computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration; 2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively.

关键词： Processor scheduling Computational modeling Markov decision processes Image edge detection Containers Dynamic scheduling Delays

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Joint Task Scheduling and Container Image Caching in Edge computing

arXiv

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arXiv 2023年

作者： Mou, Fangyi Tang, Zhiqing Lou, Jiong Guo, Jianxiong Wang, Wenhua Wang, Tian Institute of Artificial Intelligence and Future Networks Beijing Normal University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China

In Edge computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration;2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Edge computing

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Accelerating Homomorphic Encryption by Using Representation of Lower Hamming Weight

SSRN

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SSRN 2022年

作者： Wu, Junfei Meng, Dan Cao, Guitao Wei, Tongquan Cao, Wenming Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China MoE Engineering Research Center of SH/HW Co-design Technology and Application East China Normal University Shanghai200062 China OPPO Research Institute Shenzhen China School of Computer Science and Technology China College of Information Engineering Shenzhen University Shenzhen518060 China

How to simultaneously guarantee data processing and data hiding has become a topical issue because of the increasing needs for enhancing privacy protection. Homomorphic encryption is a privacy computing technology that can satisfy the above requirements and strike a balance between security and operability. However, homomorphic encryption suffers from severe computational inefficiency. Although many works attempt to address the problem, few works has noticed that the computational bottleneck of homomorphic encryption is closely related to the Hamming weight of the exponents. In this paper, we investigate the computational efficiency issue of homomorphic encryption and propose a signed binary representation based solution that significantly improves the state of the art Paillier schemes. We first use signed binary representation to reduce the Hamming weight of the exponent, thereby decreasing the number of modular multiplications in homomorphic encryption. We then combine signed binary representation with precomputation techniques to further accelerate homomorphic encryption. Our proposed approach can be integrated into all Paillier schemes and computing platforms without significant modification. The experimental results exhibit that our technique can considerably improve the encryption speed, in particular, it can accelerate the state of the arts by 5.25\times when signed binary representation and precomputation are jointly utilized. © 2022, The Authors. All rights reserved.

关键词： Cryptography

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TradeFL: A Trading Mechanism for Cross-Silo Federated Learning

TradeFL: A Trading Mechanism for Cross-Silo Federated Learni...

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International Conference on Distributed computing Systems

作者： Shijing Yuan Hongtao Lv Hongze Liu Chentao Wu Song Guo Zhi Liu Hongyang Chen Jie Li Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Software & Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Shandong University Department of Computing The Hong Kong Polytechnic University Hong Kong The University of Electro-Communications Tokyo Japan Zhejiang Lab Research Center for Graph Computing China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Yancheng Blockchain Research Institute China

Cross-silo federated learning (CFL) is a distributed learning paradigm that allows organizations (e.g., financial or medical entities) to train a global model on siloed data. Recent studies on mechanisms designed for CFL, however, rarely jointly consider the potential inter-organizational competition and the lack of credibility between organizations, which may discourage organizational participation. In this paper, we investigate the problem of inter-organizational competition and credibility assurance. We propose a distributed trading mechanism, called $TradeFL$ , to incentivize organizations to contribute data and computational resources through mutual trading among organizations. Technically, TradeFL characterizes the competition among organizations and compensates for their damage incurred by competition. TradeFL runs on distributed organizations and provides credibility guarantees for compensation through a customized smart contract 1 1 Illustration of the prototype: https://***/user10963.. We prove that the interaction among organizations that contribute resources to maximize personal payoffs is a weighted potential game. Then, we propose a centralized algorithm and a distributed algorithm to determine the optimal resource contribution. Simulation results and evaluations based on real-world datasets demonstrate that our scheme achieves higher social welfare, increases the amount of contributed data by up to 64%, and improves the accuracy of the global model by at most 23.2%.

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Online Container Scheduling for Low-Latency IoT Services in Edge Cluster Upgrade: A Reinforcement Learning Approach

Online Container Scheduling for Low-Latency IoT Services in ...

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IEEE International Conference on Communications in China (ICCC)

作者： Hanshuai Cui Zhiqing Tang Jiong Lou Weijia Jia Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China School of Artificial Intelligence Beijing Normal University Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai China

In Mobile Edge computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Frequent up-grades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers and upgrade edge clusters to minimize the impact on running tasks. In this paper, we propose a low-latency container scheduling algorithm for edge cluster upgrades. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm to address this problem, considering the unique characteristics of MEC. 3) Experimental results demonstrate that our algorithm reduces total task latency by approximately 27% compared to baseline algorithms.

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UAV-Assisted Over-the-Air Computation

arXiv

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arXiv 2021年

作者： Fu, Min Zhou, Yong Shi, Yuanming Wang, Ting Chen, Wei School of Information Science and Technology ShanghaiTech University Shanghai201210 China Shanghai Key Lab. of Trustworthy Computing School of Software Engineering East China Normal University China Department of Electronic Engineering Tsinghua University Beijing100084 China

Over-the-air computation (AirComp) provides a promising way to support ultrafast aggregation of distributed data. However, its performance cannot be guaranteed in long-distance transmission due to the distortion induced by the channel fading and noise. To unleash the full potential of AirComp, this paper proposes to use a low-cost unmanned aerial vehicle (UAV) acting as a mobile base station to assist AirComp systems. Specifically, due to its controllable high-mobility and high-altitude, the UAV can move sufficiently close to the sensors to enable line-of-sight transmission and adaptively adjust all the links’ distances, thereby enhancing the signal magnitude alignment and noise suppression. Our goal is to minimize the time-averaging mean-square error for AirComp by jointly optimizing the UAV trajectory, the scaling factor at the UAV, and the transmit power at the sensors, under constraints on the UAV’s predetermined locations and flying speed, sensors’ average and peak power limits. However, due to the highly coupled optimization variables and time-dependent constraints, the resulting problem is non-convex and challenging. We thus propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization techniques. Simulation results verify the convergence of the proposed algorithm and demonstrate the performance gains and robustness of the proposed design compared with benchmarks. © 2021, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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Low-Latency Layer-Aware Proactive and Passive Container Migration in Meta computing

arXiv

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arXiv 2024年

作者： Liu, Mengjie Li, Yihua Mou, Fangyi Tang, Zhiqing Lou, Jiong Guo, Jianxiong Jia, Weijia Institute of Artificial Intelligence and Future Networks Beijing Normal University China Faculty of Arts and Sciences Beijing Normal University China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Informatics University of Zurich Zurich Switzerland

关键词： Containers

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Byzantine-resilient federated machine learning via over-the-air computation

arXiv

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arXiv 2021年

作者： Huang, Shaoming Zhou, Yong Wang, Ting Shi, Yuanming School of Information Science and Technology ShanghaiTech University Shanghai201210 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing100049 China Shanghai Key Lab. of Trustworthy Computing School of Software Engineering East China Normal University

Federated learning (FL) is recognized as a key enabling technology to provide intelligent services for future wireless networks and industrial systems with delay and privacy guarantees. However, the performance of wireless FL can be significantly degraded by Byzantine attack, such as data poisoning attack, model poisoning attack and free-riding attack. To design the Byzantine-resilient FL paradigm in wireless networks with limited radio resources, we propose a novel communication-efficient robust model aggregation scheme via over-the-air computation (AirComp). This is achieved by applying the Weiszfeld algorithm to obtain the smoothed geometric median aggregation against Byzantine attack. The additive structure of the Weiszfeld algorithm is further leveraged to match the signal superposition property of multiple-access channels via AirComp, thereby expediting the communication-efficient secure aggregation process of FL. Numerical results demonstrate the robustness against Byzantine devices and good learning performance of the proposed approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Wireless networks

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