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Proceedings - 2011 6th Annual Chinagrid Conference, Chinagrid 2011 2011年 108-115页

作者： Traore, Moussa Djiriba Jin, Hai Zou, Deqing Qiang, Weizhong Xiang, Guofu Services Computing Technology and System Lab. Cluster and Grid Computing Lab. Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769544724

Exploits sequencing is a typical way by which an attacker breaks into a network. In such a scenario, each exploit lays as an atomic proposition for subsequent exploits. An attack path is seen as a succession of exploits which take an attacker right to his/her final goal. The set of all possible attack paths form an attack graph. Researchers have proposed a multitude of techniques to generate attack graph which grows exponentially in the size of the network. Hence it is preferable to optimize the choice of solutions which avoid the cost of scalability and cumbersome. In this paper, we propose a comprehensive approach to network vulnerability analysis by ranking access Petri net graph and utilizing a penetration tester's perspective of maximal level of access possible on a host. Our approach has the following benefits: it provides a simple model in which an analyst can work, its algorithmic complexity is polynomial in the size of the network, and has the ability of scaling well to large size networks. Nevertheless, it has some drawback as in place of all possible attack paths, we seek only good attack paths. An analyst may make suboptimal choices when repairing the network. © 2011 IEEE.

关键词： Petri nets

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Ripple: Toward a substrate middleware for peer-to-peer applications

Ripple: Toward a substrate middleware for peer-to-peer appli...

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3rd International Conference on Innovative computing Information and Control, ICICIC'08

作者： Yao, Hong Yu, Linchen Jin, Hai Yang, Sirui Huang, Qi Jiang, Wenjian School of Computer Science and Technology China University of Geosciences Wuhan 430074 China Cluster and Grid Computing Lab. Services Computing Technology and System Lab. Huazhong University of Science and Technology Wuhan 430074 China France Telecom R and D China Beijing 100190 China

ISBN: (纸本)9780769531618

Peers in multiple P2P applications execute overlapped operations with different implementation, like neighbour management, resource publish/lookup, delay investigation, which leads to poor interoperability and unnecessary network load. Peers contribute their resources only when they stay in same application. We propose a P2P middleware, called Ripple, to accomplish common functions in a layered architecture. Ripple manages all peers running various applications in a uniform substrate overlay network. The substrate overlay is designed with a hybrid topology. This hybrid topology does not aim at only a particular type of applications, but also enables the flexibility for various applications. Our work is an attempt to address common needs of P2P applications, which may simplify the development and improve the performance of P2P networks, as well as mitigate the sampling stress to physical networks. © 2008 IEEE.

关键词： Middleware

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PipeEdge: A Trusted Pipelining Collaborative Edge Training based on Blockchain 23

PipeEdge: A Trusted Pipelining Collaborative Edge Training b...

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2023 World Wide Web Conference, WWW 2023

作者： Yuan, Liang He, Qiang Chen, Feifei Dou, Ruihan Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Mathematics University of Waterloo Canada National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads. © 2023 ACM.

关键词： Deep neural networks

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Optimizing data shuffling in data-parallel computation by understanding user-defined functions 9

Optimizing data shuffling in data-parallel computation by un...

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9th USENIX Symposium on Networked systems Design and Implementation, NSDI 2012

作者： Zhang, Jiaxing Zhou, Hucheng Chen, Rishan Fan, Xuepeng Guo, Zhenyu Lin, Haoxiang Li, Jack Y. Lin, Wei Zhou, Jingren Zhou, Lidong Microsoft Research Asia China Microsoft Bing United States Peking University China Services Computing Technologyand System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Georgia Institute of Technology United States

Map/Reduce style data-parallel computation is characterized by the extensive use of user-defined functions for data processing and relies on data-shuffling stages to prepare data partitions for parallel computation. Instead of treating user-defined functions as "black boxes", we propose to analyze those functions to turn them into "gray boxes" that expose opportunities to optimize data shuffling. We identify useful functional properties for userdefined functions, and propose SUDO, an optimization framework that reasons about data-partition properties, functional properties, and data shuffling. We have assessed this optimization opportunity on over 10,000 dataparallel programs used in production SCOPE clusters, and designed a framework that is incorporated it into the production system. Experiments with real SCOPE programs on real production data have shown that this optimization can save up to 47% in terms of disk and network I/O for shuffling, and up to 48% in terms of cross-pod network traffic. © 2012 by The USENIX Association. All Rights Reserved.

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xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction 23

xGCN: An Extreme Graph Convolutional Network for Large-scale...

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2023 World Wide Web Conference, WWW 2023

作者： Song, Xiran Lian, Jianxun Huang, Hong Luo, Zihan Zhou, Wei Lin, Xue Wu, Mingqi Li, Chaozhuo Xie, Xing Jin, Hai Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China Microsoft Gaming Redmond United States 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 China

ISBN: (纸本)9781450394161

Graph neural networks (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and scalability, due to the extensive number of trainable parameters in the embedding table and the paradigm of stacking neighborhood aggregations. This paper presents a novel model called xGCN for large-scale network embedding, which is a practical solution for link predictions. xGCN addresses these issues by encoding graph-structure data in an extreme convolutional manner, and has the potential to push the performance of network embedding-based link predictions to a new record. Specifically, instead of assigning each node with a directly learnable embedding vector, xGCN regards node embeddings as static features. It uses a propagation operation to smooth node embeddings and relies on a Refinement neural Network (RefNet) to transform the coarse embeddings derived from the unsupervised propagation into new ones that optimize a training objective. The output of RefNet, which are well-refined embeddings, will replace the original node embeddings. This process is repeated iteratively until the model converges to a satisfying status. Experiments on three social network datasets with link prediction tasks show that xGCN not only achieves the best accuracy compared with a series of competitive baselines but also is highly efficient and scalable. © 2023 ACM.

关键词： Graph neural networks

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Vehicle Tracking with Non-overlapping Views for Multi-camera Surveillance system

Vehicle Tracking with Non-overlapping Views for Multi-camera...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Wenbin Jiang Chang Xiao Hai Jin Shuo Zhu Zhiwei Lu Services Computing Technology and System Lab Cluster and Grid Computing Huazhong University of Science and Technology Wuhan China

With the rapid development of intelligent video surveillance system for transportation, traditional single-camera-based video analysis has become insufficient. Many researches have focused on the non-overlapping multi-camera target tracking. However, the tracking precision and the computing overhead are still big obstacles. This paper proposes a novel vehicle tracking approach for non-overlapping multi-camera targets with data fusion by using minimum cost and maximum flow method. Structured information of moving targets is extracted and associated with other information such as motion time, the topology of camera network to solve targeted vehicle tracks. Besides, to improve the performance of the process, a parallelization algorithm for camera network topology partitioning is presented, which makes it possible for each sub-graph to track target independently in parallel. The experiment results show that the presented approach is able to perform target vehicle tracking analysis with high efficiency and accuracy.

关键词： Cameras Target tracking Vehicles Feature extraction Network topology Topology

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A Semantic Double-Buffer Based Approach to Enhance Semantic Web Search

A Semantic Double-Buffer Based Approach to Enhance Semantic ...

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International Conference on the Digital Society

作者： Ling Chen Hai Jin Sheng Di Cluster and Grid Computing Lab Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

With the development of semantic Web, search efficiency is becoming a challenging issue. To address this problem, we design an approach which mainly has two features. First, we explore potential semantic relationship between different objects and make full use of them to build a semantic buffer on server end, enhancing search speed. Second, we put another relatively small semantic buffer on each client so as to adapt to each individual user's interests, further improving the whole search efficiency and reducing server's load. Through testing on a practical platform, we testified that this semantic double-buffer based search approach can not only effectively reduce user's request response time, but considerably release server's load, bringing a high scalability.

关键词： Semantic Web Testing Resource description framework Algorithm design and analysis Indexing grid computing Computers Delay Scalability Information retrieval

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POSTER: Layrub: Layer-centric GPU memory reuse and data migration in extreme-scale deep learning systems

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ACM SIGPLAN Notices 2018年第1期53卷 405-406页

作者： Liu, Bo Jiang, Wenbin Jin, Hai Shi, Xuanhua Ma, Yang 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

ISBN: (纸本)9781450349116

Growing accuracy and robustness of Deep Neural Networks (DNN) models are accompanied by growing model capacity (going deeper or wider). However, high memory requirements of those models make it difficult to execute the training process in one GPU. To address it, we first identify the memory usage characteristics for deep and wide convolutional networks, and demonstrate the opportunities of memory reuse on both intra-layer and inter-layer levels. We then present Layrub, a runtime data placement strategy that orchestrates the execution of training process. It achieves layer-centric reuse to reduce memory consumption for extreme-scale deep learning that cannot be run on one single GPU. © 2018 Owner/Author.

关键词： Deep neural networks

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VMgrid: A Virtual Machine supported grid middleware

VMGrid: A Virtual Machine supported grid middleware

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IEEE International Conference on Granular computing (GRC)

作者： Song Wu Wei Zhu Wenbin Jiang Hai Jin Cluster and Grid Computing Lab Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424425129

Many grid middleware such as Globus Toolkit and GPE have started to support virtual machines. They implement the remote control of virtual machine by grid service and make use of virtual machinespsila function of quick deployment to construct the execution environment of grid application fast. In order to support virtual machine in grid environment, VMgrid (Virtual Machine grid) is presented. All services in VMgrid are implemented based on secure shell host protocol (SSH) while Globus Toolkitpsilas Virtual Workspace is on WSRF. This paper gives a clear description of VMgrid. Some comparisons with Globus Toolkitpsilas Virtual Workspace in performance are made in this paper. Performance results show that VMgrid virtual workspace atomic services are more effective than that of Globus.

关键词： Virtual machining Middleware grid computing Operating systems Software Libraries Computers

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Adaptive multi-round scheduling strategy for divisible workloads in grid environments

Adaptive multi-round scheduling strategy for divisible workl...

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International Conference on Information Networking

作者： Yongcai Tao Hai Jin Song Wu Xuanhua Shi Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424445264

Scheduling is the key to divisible workload execution. UMR (uniform multi-round) algorithm potentially performs near optimal by improving overlap of communication and computation. However, it is questioned how a static schedule works effectively in dynamic grid environment. The paper proposes an adaptive divisible workload scheduling system, which can adjust the schedule in a proactive way. An adaptive UMR-based multi-round algorithm (called AUMR) is presented and evaluated. In AUMR, if the run-time resource monitor notifies the scheduler of any resource changes, the scheduler will evaluate its impact and adjust the schedule if necessary. The experiment results show a considerable performance improvement by AUMR in dynamic grid environment.

关键词： Adaptive scheduling Processor scheduling grid computing Dynamic scheduling Monitoring Scheduling algorithm Robustness Delay Computers clustering algorithms

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