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12th IEEE International Conference on Joint Cloud Computing (IEEE JCC) / 9th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (IEEE MobileCloud)

作者： Guo, Jinyan Jiang, Zigui Li, Li Bian, Jing Sun Yat Sen Univ Sch Math Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Software Engn Guangzhou 510006 Peoples R China Guangdong Inst Sci & Technol Informat Guangzhou 510030 Peoples R China Parallel & Distributed Proc Lab PDL Sci & Technol Changsha 410073 Peoples R China

ISBN: (纸本)9781665434799

Applications on blockchain are currently limited by the relatively poor performance of the blockchain network such as low TPS, high latency and the resulting high transaction fees. Hence, performance optimization is one crucial problem of blockchain. Focusing on the most prosperous public blockchain Ethereum, we model the on-chain transaction confirmation process with the knowledge of Poisson process and queueing theory, derives the mean transaction-confirmation time, and explores the effect of different transaction fees on latency. We also conduct a numeric simulation of the model, which indicates that the model fits in well with the real world blockchain.

关键词： Blockchain Ethereum Mathematical modeling Stochastic process Queueing theory

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Excitement surfeited turns to errors: Deep learning testing framework based on excitable neurons

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INFORMATION SCIENCES 2023年第1期637卷

作者： Jin, Haibo Chen, Ruoxi Zheng, Haibin Chen, Jinyin Cheng, Yao Yu, Yue Chen, Tieming Liu, Xianglong Zhejiang Univ Technol Coll Informat Engn Hangzhou Peoples R China Zhejiang Univ Technol Inst Cyberspace Secur Hangzhou Peoples R China Huawei Int Singapore Singapore Natl Univ Def Technol Coll Comp Natl Lab Parallel & Distributed Proc Changsha Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China

Despite impressive capabilities and outstanding performance, deep neural networks (DNNs) have become a growing concern among the public due to their frequently-appearing erroneous behaviors. As a result, it is urgent to test the security issues with DNNs systematically before they are deployed to the real world. Fine-grained metrics based on neuron coverage have been provided by existing testing approaches, and various approaches have been proposed to enhance these metrics. However, it has been gradually realized that a higher neuron coverage does not necessarily represent better capabilities in identifying defects that lead to errors. Besides, coverage-guided methods cannot hunt errors due to faulty training procedure. So the robustness improvement of DNNs via retraining by these testing examples are unsatisfactory. To tackle this challenge, we introduce the concept of excitable neurons based on Shapley value and design a white-box testing framework for DNNs, namely DeepSensor. It is motivated by our observation that neurons with larger responsibility towards model loss changes due to small perturbations are more likely related to incorrect corner cases due to potential defects. By maximizing the number of excitable neurons that correspond to various incorrect behaviors of models, DeepSensor can generate testing examples that effectively trigger more erroneous security issues caused by malicious inputs (both adversarial and polluted data) and incomplete training. Extensive experiments implemented on both image classification models and speaker recognition models have demonstrated the superiority of DeepSensor. Compared to the state-of-the-art testing methods, DeepSensor has the ability to find more wrong model behaviors due to malicious inputs (i.e., similar to x1.2 for adversarial and similar to x4.7 for polluted data) and incompletely-trained DNNs. Additionally, it can help DNNs build larger l(2)-norm robustness bound (similar to x3) via retraining according to C

关键词： Deep neural networks Deep learning testing Cooperative game theory Excitable neurons

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud Computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud Computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud Computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

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distributed and parallel Sparse Computing for Very Large Graph Neural Networks

Distributed and Parallel Sparse Computing for Very Large Gra...

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IEEE International Conference on Big Data

作者： Quentin R. Petit Chong Li Nahid Emad Huawei Paris Research Center & Université Paris-Saclay Boulogne-Billancourt France Distributed and Parallel Software Lab Huawei Paris Research Center Boulogne-Billancourt France Maison de la Simulation & LI-PaRAD Université Versailles Saint-Quentin Saclay France

ISBN: (纸本)9781665480468

Deep learning (DL) requires high-performance processing on big data. Graph Neural Networks, a challenging topic in DL using linear algebra methods, need algorithmic solutions to efficiently assign and process graph data on modern distributed and parallel machines, which are considered with mixed arithmetic and various types of tensor/matrix accelerators. Determining compression techniques for the graph’s sparse data structures is one of the key *** first objective is to design and implement a reusable parallel numerical library to resolve large neural network graphs. Our design strategy is drawn on a component-based approach and targets maximum code reuse in various parallel contexts while allowing for performance optimization. The solution could be later integrated into a DL framework like MindSpore.

关键词： Deep learning distributed databases Linear algebra Big Data parallel machines Graph neural networks Libraries

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Feature and Performance Comparison of FaaS Platforms

Feature and Performance Comparison of FaaS Platforms

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IEEE International Conference on software Engineering and Service Sciences (ICSESS)

作者： Penghui Ma Peichang Shi Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs.

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Large-scale homo- and heterogeneous parallel paradigm design based on CFD application PHengLEI

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第5期36卷 e7933-e7933页

作者： Wan, Yunbo Zhao, Zhong Liu, Jie Zhang, Laiping Zhang, Yong Chen, Jianqiang Natl Univ Def Technol Coll Comp Sci & Technol Changsha Peoples R China China Aerodynam Res & Dev Ctr Computat Aerodynam Inst Mianyang Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha Peoples R China Natl Innovat Inst Def Technol Unmanned Syst Res Ctr Beijing Peoples R China China Aerodynam Res & Dev Ctr Computat Aerodynam Inst Mianyang 621000 Peoples R China

The development of computational fluid dynamics (CFD) highly depends on high-performance computers. Computer hardware has evolved rapidly, yet scalable CFD parallel software remains scarce. In this article, we design a highly scalable CFD parallel paradigm for both homogeneous and heterogeneous supercomputers. The paradigm achieves the separation of communication and computation and automatically adapts to various solvers and hardware environments, thus reducing programming difficulties and increasing automatic parallelization. Meanwhile, the number of communications is greatly reduced and the scalability of the program is improved through implementing centralized communication and two-level partitioning techniques. Complex flow problems for real aircraft were then computed on different hardware platforms with a grid size of ten billion. The homogeneous computer hardware includes Intel Xeon Gold 6258R and Phytium 2000+ processors, and the heterogeneous computer platforms include NVIDIA Tesla V100 and SW26010 processors. High parallel efficiency was obtained on all computer platforms, verifying that the paradigm has good automatic parallelization, scalability, and stability. The paradigm in this article has an important reference value for CFD massively parallel computing and can promote the development and application of CFD technology.

关键词： Automatic parallelization CFD Heterogeneous parallel paradigm Homogeneous parallel paradigm Large scale parallelization PHengLEI

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A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks 20

A Framework for Recommending Accurate and Diverse Items Usin...

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26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Sun, Jianing Guo, Wei Zhang, Dengcheng Zhang, Yingxue Regol, Florence Hu, Yaochen Guo, Huifeng Tang, Ruiming Yuan, Han He, Xiuqiang Coates, Mark Huawei Noahs Ark Lab Montreal Res Ctr Montreal PQ Canada Huawei Noahs Ark Lab Shenzhen Peoples R China Huawei Distributed & Parallel Software Lab Hangzhou Peoples R China McGill Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9781450379984

Personalized recommender systems are playing an increasingly important role for online consumption platforms. Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences. Previous graph-based recommendation approaches process the observed user-item interaction graph as a ground-truth depiction of the relationships between users and items. However, especially in the implicit recommendation setting, all the unobserved user-item interactions are usually assumed to be negative samples. There are missing links that represent a user's future actions. In addition, there may be spurious or misleading positive interactions. To alleviate the above issue, in this work, we take a first step to introduce a principled way to model the uncertainty in the user-item interaction graph using the Bayesian Graph Convolutional Neural Network framework. We discuss how inference can be performed under our framework and provide a concrete formulation using the Bayesian Probabilistic Ranking training loss. We demonstrate the effectiveness of our proposed framework on four benchmark recommendation datasets. The proposed method outperforms state-of-the-art graph-based recommendation models. Furthermore, we conducted an offline evaluation on one industrial large-scale dataset. It shows that our proposed method outperforms the baselines, with the potential gain being more significant for cold-start users. This illustrates the potential practical benefit in real-world recommender systems.

关键词： Graph Convolution Networks Recommendation Collaborative Filtering Bayesian Graph Neural Networks

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FCloudless: A Performance-Aware Collaborative Mechanism for JointCloud Serverless

FCloudless: A Performance-Aware Collaborative Mechanism for ...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jianfei Liu Huaimin Wang Peichang Shi Yaojie Li Penghui Ma Guodong Yi National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha 410073 China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha 410073 China Xiangjiang Lab Changsha 410073 China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha 410073 China

As a new stage in the development of the cloud computing paradigm, serverless computing has the high-level abstraction characteristic of shielding underlying details. This makes it extremely challenging for users to choose a suitable serverless platform. To address this, targeting the jointcloud computing scenario of heterogeneous serverless platforms across multiple clouds, this paper presents a jointcloud collaborative mechanism called FCloudless with cross-cloud detection of the full lifecycle performance of serverless platforms. Based on the benchmark metrics set that probe performance critical stages of the full lifecycle, this paper proposes a performance optimization algorithm based on detected performance data that takes into account all key stages that affect the performance during the lifecycle of a function and predicts the overall performance by combining the scores of local stages and dynamic weights. We evaluate FCloudless on AWS, AliYun, and Azure. The experimental results show that FCloudless can detect the underlying performance of serverless platforms hidden in the black box and its optimization algorithm can select the optimal scheduling strategy for various applications in a jointcloud environment. FCloudless reduces the runtime by 23.3% and 24.7% for cold and warm invocations respectively under cost constraints.

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Contrastive learning of user behavior sequence for context-aware document ranking

arXiv

引用

arXiv 2021年

作者： Zhu, Yutao Nie, Jian-Yun Dou, Zhicheng Ma, Zhengyi Zhang, Xinyu Du, Pan Zuo, Xiaochen Jiang, Hao Université de Montréal MontréalQC Canada Gaoling School of Artificial Intelligence Renmin University of China Beijing China Distributed and Parallel Software Lab Huawei Zhejiang Hangzhou China

Context information in search sessions has proven to be useful for capturing user search intent. Existing studies explored user behavior sequences in sessions in different ways to enhance query suggestion or document ranking. However, a user behavior sequence has often been viewed as a definite and exact signal reflecting a user's behavior. In reality, it is highly variable: user's queries for the same intent can vary, and different documents can be clicked. To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences. Specifically, we propose three data augmentation strategies to generate similar variants of user behavior sequences and contrast them with other sequences. In so doing, the model is forced to be more robust regarding the possible variations. The optimized sequence representation is incorporated into document ranking. Experiments on two real query log datasets show that our proposed model outperforms the state-of-the-art methods significantly, which demonstrates the effectiveness of our method for context-aware document ranking. Copyright © 2021, The Authors. All rights reserved.

关键词： Search engines

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Enhancing Graph Convolutional Networks by Topology Sampling

Enhancing Graph Convolutional Networks by Topology Sampling

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IEEE International Conference on Big Data

作者： Quentin R. Petit Chong Li Serge G. Petiton Kelun Chai Nahid Emad Huawei Paris Research Center & Université Paris-Saclay Boulogne-Billancourt France Distributed and Parallel Software Lab Huawei Paris Research Center Boulogne-Billancourt France Université de Lille Lille France Maison de la Simulation & LI-PaRAD Université Versailles Saint-Quentin Saclay France

ISBN: (纸本)9781665480468

Graph Neural Networks (GNNs) play a very important role today. It does analyze not only the graph data itself, but also the data connectivity of the graph. The quality of a GNN is thus altered by the result of extracted graph structure information. The extraction could be enhanced by GNN model design or directly from the training dataset with a GNN-decoupled method. In this paper, we propose RankedDrop, a new sampling method to improve the extraction of graph structure information. This approach is based on droppingout technique, and it adopts a spatial-aware selection of edges to drop. It takes into account structure information of the graph to control the dropping-out, and its random selection of edges to be dropped is under the control of a probability generated with respect to graph’s topological importance. Our experiments point out that RankedDrop provides high-quality and robust training results compared to the leading solutions. Furthermore, RankedDrop could be a framework plugin and combined with GNN model improvements to maximize GNN quality. Furthermore, RankedDrop could be a plugin for AI frameworks like MindSpore and combined with GNN model improvements to maximize GNN quality.

关键词： Training Network topology Big Data Sampling methods Graph neural networks Topology Data mining

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