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International Conference on Cyber-Enabled distributed Computing and Knowledge Discovery, CyberC

作者： Lixia Liu Gang Hu Ming Xu Yuxing Peng National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China Department of Networks Engineering School of Computer National University of Defense Technology Changsha China

In the relay-trading mode of wireless cognitive radio networks the secondary user (SU) can achieve a promised spectrum access opportunity by relaying for the primary user (PU). How to utilize the exchanged resource efficiently and fairly is an interesting and practical problem. In this paper we proposed a cooperative spectrum sharing strategy (RT-CSS) for the relay-trading mode from the fairness view. The cooperative SUs are gathered in a cooperative sharing group (CSG), and contribution metric (CM) is proposed to measure each CSG member's contribution to CSG as well as benefit from CSG. The adjustment of CM can guarantee the fairness and efficiency of spectrum sharing. The numerical simulation shows that RT-CSS can achieve better performance than the sense-uncooperative mode.

关键词： Relays Cognitive radio Measurement Qualifications Wireless sensor networks Radio networks

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A data-driven mechanism for large-scale data distribution

A data-driven mechanism for large-scale data distribution

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Proceedings of the Biannual World Automation Congress

作者： Peichang Shi Yiying Li Bo Ding Longquan Jiang Hui Liu Jie Zhang National Key Laboratory of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha Hunan CN National Key Laboratory of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 China National University of Defense Technology Changsha Hunan CN China Electr. Equip. & Syst. Eng. Co. Ltd. China

As The integration of Physical space and cyberspace, the large-scale data distributing to diversification terminal which is geographical distribution of mass has become a huge challenge. When the data size can't be processed by the technology for traditional scope, how to deal with the user quality of service and efficient use of system resources has become an important issue of concern, with the resources becoming limited. This paper presents a data-driven mechanism for large-scale data distribution which is consists of four core part of the data production, data collection and pre-processing, data analysis engine, data consumption, aims to excavate the valuable information to improve the efficiency of resource use and accurate fault location for the Large-scale data distribution system. At the same time, this paper studies the resource scheduling optimization with analyzing data driven for the system behavior and Fault location with analyzing data-driven environment, which proves the effectiveness for the operation of the Large-scale data distribution system optimization by the data-driven working.

关键词： Servers Monitoring distributed databases Big data Real-time systems Business

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Security Issues and Solutions in Cloud Computing

Security Issues and Solutions in Cloud Computing

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International Conference on distributed Computing Systems Workshop

作者： Pengfei You Yuxing Peng Weidong Liu Shoufu Xue National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China Jiangsu Automation Research Institute Jiangsu China CPLA Air Defence Forces Command Academy Zhengzhou Henan China

Recently, Cloud computing, as one of the hottest words in IT world, has drawn great attention. Many IT companies such as IBM, Google, Amazon, Microsoft, Yahoo and others vigorously develop cloud computing systems and related products to customers. However, there are still some difficulties for customers to adopt cloud computing, in which many security issues exist, because data for a customer is stored and processed in cloud, not in a local machine. This paper briefly introduces cloud computing and its key concepts. In particularly, we intend to discuss security requirements and security issues involving data, application and virtualization in cloud computing, as well as current solutions to these issues.

关键词： Cloud computing Computational modeling Virtual machine monitors Operating systems Cryptography

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Constrained Projective Non-negative Matrix Factorization for Semi-supervised Multi-label Learning

Constrained Projective Non-negative Matrix Factorization for...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Xiang Zhang Naiyang Guan Zhigang Luo Xuejun Yang College of Computer National University of Defense Technology Changsha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China The State key Laboratory of High Performance Computing National University of Defense Technology Changsha China

This paper formulates multi-label learning as a constrained projective non-negative matrix factorization (CPNMF) problem which concentrates on a variant of the original projective NMF (PNMF) and explicitly introduces an auxiliary basis to learn the semantic subspace and boosts its discriminating ability by exploiting labeled and unlabeled examples together. Particularly, it propagates labels of the labeled examples to the unlabeled ones by enforcing coefficients of examples sharing identical semantic contents to be identical based on a hard constraint, i.e., embedding the class indicator of labeled examples into their coefficients. CPNMF preserves the geometrical structure of dataset via manifold regularization meanwhile captures the inherent structure of labels by using label correlations. We developed a multiplicative update rule (MUR) based algorithm to optimize CPNMF and proved its convergence. Experiments of image annotation on Corel dataset, text categorization on Rcv1v2 dataset, and text clustering on two popular text corpuses suggest the effectiveness of CPNMF.

关键词： Conferences

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NR-MPI: A Non-stop and Fault Resilient MPI

NR-MPI: A Non-stop and Fault Resilient MPI

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International Conference on parallel and distributed Systems (ICPADS)

作者： Guang Suo Yutong Lu Xiangke Liao Min Xie Hongjia Cao State Key Laboratory of High Performance Computing National University of Defense Technology Changsha Hunan Province China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan Province China

Fault resilience has became a major issue for HPC systems, in particular in the perspective of future E-scale systems, which will consist of millions of CPU cores and other components. Fault tolerant MPI was proposed to offer support of software level fault tolerance approaches. However, the widely used MPI implementations, such as MPICH and Mvapich2, provide limited support for fault tolerance. This paper proposes NR-MPI, a Non-stop and Fault Resilient MPI. NR-MPI implements the semantics of FT-MPI based on MPICH. Specifically, this paper focuses on failure detection in MPI library, online failure recovery of communicators for multiple failures, friendly programming interface extending for NR-MPI. Furthermore, to support failure recovery of applications, NR-MPI implements data backup and restore interfaces based on double in-memory checkpoint/restart. We conduct experiments with NPB benchmarks on TH-1A supercomputer. Experimental results show that NR-MPI based fault tolerant programs can recover from failures online without restarting, and the overhead is small even for applications with tens of thousands of cores.

关键词： Fault tolerance Fault tolerant systems Libraries Resource management Context Programming Semantics

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Design of an Indoor Exploration and Multi-Objective Navigation System

Design of an Indoor Exploration and Multi-Objective Navigati...

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第37届中国控制会议

作者： Guoxian Zheng Lei Zhang Huaxi (Yulin) Zhang Bo Ding State Key Laboratory of Synthetical Automation for Process Industries Northeastern University MIS and INSSET Universite de Picardie Jules Verne National Laboratory for Parallel and Distributed Processing National University of Defense Technology

In this paper,we propose an indoor robot autonomous navigation *** robot firstly explores in an unknown environment,and then navigates autonomously by using the explored *** robot is equipped a 2 D laser scanner as the main *** laser scanner is used for path planning and frontier-based exploration.A 2 D global occupancy map is built for path planning,frontier-based exploration and multi-objective autonomous *** scans are transmitted into Simultaneous Localization and Mapping（SLAM） process in the exploration *** indoor environment,the exploration efficiency is improved by merging a heuristic *** using multi-threading technology and a 3 D perception approach proposed in this paper,the robot equipped with a low-cost RGBD sensor can detect all kinds of obstacles to achieve highly reliable navigation in complicated 3 D ***,we develop a multi-objective navigation application to make human-robot interaction more convenient and satisfy multi-task *** approaches are demonstrated by experimental results.

关键词： Exploration Heuristic algorithm multi-threading technology 3D Perception Multi-objective navigation

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Bi-Objective Scheduling Algorithm for Hybrid Workflow in JointCloud

Bi-Objective Scheduling Algorithm for Hybrid Workflow in Joi...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Rui Li Huaimin Wang Peichang Shi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China State Key Laboratory of Complex & Critica Software Environment College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Big data workflows are widely used in IoT, recommended systems, and real-time vision applications, and they continue to grow in complexity. These hybrid workflows consist of both resource-intensive batch jobs and latency-sensitive stream jobs. Examples include the data analytics workflow, which incorporates batch data transformations and low-latency querying, and the machine learning workflow, which processes stream data feature extraction before performing batch training and low-latency inference. However, existing research on workflow scheduling primarily focuses on either stream or batch workflows, neglecting the efficient scheduling of hybrid workflows that respect their diverse resource requirements and the costly data transfers between *** this article, we propose a hybrid workflow model that defines the optimal placement of hybrid workflows (OHWP) as a bi-objective optimization problem. Our proposed model takes into account parameters related to inter-communication between stream and batch jobs, as well as the heterogeneous resources in JointCloud environment. Additionally, we present OHWP-PS (OHWP on a Pruned Space), a scheduling algorithm for hybrid workflows that minimizes both cost and latency by improving the initial population and dynamically updating the search space. The results demonstrate that the proposed OHWP-PS algorithm is effective and competitive across all experiments.

关键词： Training Scheduling algorithms Machine vision Heuristic algorithms Machine learning Feature extraction Remote working

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High-performance Network Traffic Classification Based on Graph Neural Network

High-performance Network Traffic Classification Based on Gra...

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IEEE Information technology, Networking, Electronic and Automation Control Conference

作者： Bo Pang Yongquan Fu Siyuan Ren Yan Jia College of Computer Science and Technology Harbin Institute of Technology Shenzhen China National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changesha China Peng Cheng Laboratory Shen Zhen China

Network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to automatically extract features from packet streams. Unfortunately, current approaches fail to effectively combine the structural information of traffic packets with the content features of the packets, resulting in limited classification accuracy. In this paper, we propose a graph neural network model for network traffic classification, which can well perceive the interaction feature of packets in traffic. Firstly, we design a graph structure for packets’ flows to hold the interaction information between packets, which embeds both packet contents and sequence relationships into a unified graph. Secondly, we propose a graph neural network framework for graph classification to automatically learn the structural features of the packets’ flows together with the packets’ features. Extensive evaluation results on real-world traffic data show that the proposed model improves the prediction accuracy of improves the prediction accuracy by 2% to 37% for malicious traffic classification.

关键词： Deep learning Automation Telecommunication traffic Predictive models Network security Feature extraction Graph neural networks

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An improved algorithm for reconstructing singular connection in the multi-block cfd applications

arXiv

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arXiv 2017年

作者： Yong-Xian, Wang Li-Lun, Zhang Yong-Gang, Che Chuan-Fu, Xu Wei, Liu Hua-Yong, Liu Zheng-Hua, Wang College of Computer National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China State Key Laboratory of Aerodynamics Mianyang621000 China

In this paper, an improved algorithm is proposed for the reconstruction of singularity connectivity from the available pairwise connections during preprocessing phase. To evaluate the performance of our algorithm, an in-house CFD code, in which high-order finite-difference method for spatial discretization, running on the Tianhe-1A supercomputer is employed. Test cases with a varied amount of mesh points are chosen, and the test results indicate that the improved singular connection reconstruction algorithm can achieve a 2000× speedup at least compared with the naive search method adopt in the former version of our code. Moreover, the parallel efficiency can be benefited from the strategy of local communication based on the new algorithm. Copyright © 2017, The Authors. All rights reserved.

关键词： Finite difference method

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Memory service based memory sharing grid system

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Jisuanji Xuebao/Chinese Journal of Computers 2006年第7期29卷 1225-1233页

作者： Chu, Rui Xiao, Nong Lu, Xi-Cheng National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

Many scientific computing applications demand a great amount of memory, which are usually run on supercomputers with large physical memory. The applications whose complete time is affected by memory capacity, is usually called memory-intensive applications. Many disk 10 operations will be caused by memory-intensive applications due to insufficient physical memory of computing environment, which depress the system performance dramatically. Traditional network memory or memory server schemes try to share free memory of idle nodes in the cluster, while it is often influenced by heavy cluster loading or interior network congestion. Combining the network memory with current service computing and grid computing technology, a memory service based memory sharing grid system named RAM Grid, is proposed. RAM grid improve traditional network memory scheme, and extend the application area of grid computing. As one of the major computing resources, memory can be shared on the Internet through RAM grid, such as computing power and disk storage being shared through computational grid and data grid. The system criteria and properties are analyzed. One of the system schemes is also proposed in detail. Through the real trace based simulation, the performance of RAM Grid is evaluated and proved better than traditional network memory or disk IO obviously.

关键词： Random access storage

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