检索结果-内蒙古大学图书馆

4th Annual Symposium on Cloud computing, SoCC 2013

作者： Dong, Yaozu Ye, Wei Jiang, Yunhong Pratt, Ian Ma, Shiqing Li, Jian Guan, Haibing Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China Intel Asia-Pacific R and D Ltd. China School of Software Shanghai Jiao Tong University China Bromium Inc. United States

ISBN: (纸本)9781450324281

Virtual machine (VM) replication provides a software solution of for business continuity and disaster recovery through application-agnostic hardware fault tolerance by replicating the state of primary VM (PVM) to secondary VM (SVM) on a different physical node. Unfortunately, current VM replication approaches suffer from excessive overhead, which severely limit their applicability and suitability. In this paper, we leverage the practical effect of networked server-client system that PVM and SVM are considered as in the same state only if they can generate the same response from the clients' point of view, and this is exploited to optimize performance. To this end, we propose a generic and highly efficient non-stop service solution, named as "COLO" (COarse-grained LOck-stepping virtual machine) utilizing on-demand VM replication. COLO monitors the output responses of the PVM and SVM, and rules the SVM as a valid replica of the PVM according to the output similarity between PVM and SVM. If the responses do not match, the commit of network response is withheld until PVM's state has been synchronized to SVM. Hence, we ensure that the system is always capable of failover by SVM. Although non-determinism may mean a different internal state of SVM from that of the PVM, it is equally valid and remains consistent from external observations. Unlike earlier instruction level lock-stepping deterministic execution approaches, COLO can easily support Multi-Processors (MP) involving workloads with the satisfying performance. Results show that COLO significantly outperforms existing approaches, particularly on server-client workloads such as online databases and web server applications. Copyright © 2013 by the Association for computing Machinery, Inc.

关键词： Virtual machine

来源：评论

学校读者我要写书评

暂无评论

A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

A multi-objective ant colony system algorithm for virtual ma...

引用

作者： Gao, Yongqiang Guan, Haibing Qi, Zhengwei Hou, Yang Liu, Liang Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai 200240 China IBM Research - China Beijing 100193 China

Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing multi-objective genetic algorithm and two single-objective algorithms, a well-known bin-packing algorithm and a max-min ant system (MMAS) algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to. © 2013 Elsevier Inc.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Exploiting mobility patterns for inter-technology handover in mobile environments

引用

Computer Communications 2013年第2期36卷 203-210页

作者： Yanmin Zhu Lei Ni Bo Li Shanghai Key Lab of Scalable Computing and Systems Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Hong Kong University of Science and Technology China

Mobile terminals with multi-radio devices have become increasingly prevalent. This makes it possible for Internet applications to be supported by heterogeneous wireless networks while the terminal is on the move. As the user is constantly moving, it is highly desirable that the terminal connects to the best network and retains high performance of network connections. Handovers can be made within the same type of network (horizontal handover) or different types of networks (vertical handover). This paper focuses on link-layer inter-technology vertical handovers . Vertical handovers present several great challenges, such as user mobility randomness, high handover overhead and optimality requirement. Existing work often focuses only on the current network condition when making handover decisions , ignoring future performance of the terminal. As a result, a handover decision good for the current moment may soon become poor when the user moves to another place. This paper is motivated by the observation that users in a given mobile environment, such as university or enterprise campus, exhibit clear mobility patterns. We propose an approach for making handover decisions, which explicitly exploits user mobility patterns. This approach can produce high-performance handover decisions in the long run. Employing a comprehensive framework for preference customization , the approach supports user customization caring for different user preferences. Extensive real trace driven simulations and comparative study show our algorithm is better than the conventional vertical handover algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Energy-efficient scheduling on multi-FPGA reconfigurable systems

引用

Microprocessors and Microsystems 2013年第6-7期37卷 590-600页

作者： Chao Jing Yanmin Zhu Minglu Li Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai China

With the growing demand in high performance computing , reconfigurable computing systems built with Field Programmable Gate Array (FPGA) have become increasingly popular for its reconfigurability and adaptability to applications. Although such systems promise high processing performance, their energy efficiency has become a critical issue. This paper studies the crucial problem of energy-efficient scheduling for reconfigurable systems with multiple FPGAs. Several factors make the energy efficient scheduling particularly challenging, including spatial allocation constraint, reconfiguration overhead, limited reconfiguration ports, and deadline satisfaction. These unique characteristics make energy efficient scheduling in multi-FPGA reconfigurable systems particularly challenging and none of existing solutions can be directly applied. This paper takes on this challenge and proposes an energy-efficient scheduling algorithm called AEE based on ant colony optimization for multi-FPGA reconfigurable systems. A task placement scheme is devised which serves as the heuristic function that derives the minimum global makespan, which is important to the ant colony algorithm based proposed in the paper . The scheme takes into account reconfiguration overhead and places tasks for reducing the overall overhead. Then, based on AEE, an enhanced algorithm (eAEE) is devised to deal with the tasks with precedence and interdependencies . To evaluate the effectiveness of the two proposed algorithms, comprehensive trace-driven simulations have been conducted and compared with other state-of-art algorithms. Experimental results demonstrate that AEE can successfully complete tasks without violating deadline constraints and the energy dissipation is largely reduced, no more than 10.65% higher than the optimum when the problem scale is relatively small. Also, eAEE consumes energy 58.17% less than an improved simulated annealing algorithm (iSA) with a large problem scale .

关键词：

来源：评论

学校读者我要写书评

暂无评论

Compressive data retrieval with tunable accuracy in vehicular sensor networks

Compressive data retrieval with tunable accuracy in vehicula...

引用

8th International Conference on Wireless Algorithms, systems, and Applications, WASA 2013

作者： Jiang, Ruobing Zhu, Yanmin Wang, Hongjian Gao, Min Ni, Lionel M. Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Lab of Scalable Computing and Systems China Guangzhou HKUST Fok Ying Tung Graduate School Hong Kong Hong Kong Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9783642397004

On-demand data retrieval is a crucial routine operation in a vehicular sensor network. However, on-demand data retrieval in a vehicular environment is particularly challenging because of frequent network disruption, large number of data readings and limited transmission opportunities. Real world vehicular datasets usually contain a lot of data redundancy. Motivated by this important observation, we propose an approach called CDR with compressive sensing for on-demand data retrieval in the highly dynamic vehicular environment. The distinctive feature of CDR is that it supports tunable accuracy of data collection. There are two major challenges for the design of CDR. First, the sparsity level of the vehicular dataset is typically unknown beforehand. Second, it is even worse that the sparsity level of the dataset is changing over time. To combat the challenge posed by time-varying data sparsity, CDR can terminate from further collection of measurements, based on an adaptive condition on which only localized measurements and computation are needed. Extensive simulations with real datasets and real vehicular GPS traces show that our approach achieves good performance of data retrieval with user-customized accuracy. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Sensor networks

来源：评论

学校读者我要写书评

暂无评论

Distributed Spectrum Sharing in Cognitive Radio Networks: A Pricing-Based Decomposition Approach

引用

International Journal of Distributed Sensor Networks 2014年第11期10卷 1-1页

作者： Yanmin Zhu Wei Sun Jiadi Yu Tong Liu Bo Li Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai 200240 China Hong Kong University of Science and Technology Sai Kung Hong Kong

The limited radio spectrum has become a bottleneck for various wireless communications. To better utilize the scare radio spectrum, cognitive radios have recently attracted increasing attention, which makes spectrum sharing more viable. Sharing radio spectrum from primary users to secondary users is of great importance. A licensed primary user (PU) can lease its spectrum to secondary users (SUs) for wireless communications. This paper studies the problem of social welfare maximization of distributed spectrum sharing among a PU and SUs. We first formulate the problem of social welfare maximization which takes into account both the cost of the PU and the utility gained by each SU. The social welfare maximization is a convex optimization problem and thus can be solved by a centralized algorithm. However, the utility function of each SU may contain the private information. To avoid privacy leakage of SUs, we propose an iterative distributed algorithm based on a pricing-based decomposition framework. It is theoretically proved that our algorithm converges to the optimal solution. Simulation results are presented to show that our algorithm achieves the optimal social welfare and converges quickly in a practical setting.

关键词： COGNITIVE radio (Wireless communication systems) INFORMATION sharing RADIO networks DECOMPOSITION (Mathematics) MATRICES WIRELESS communication systems

来源：评论

学校读者我要写书评

暂无评论

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

引用

BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

来源：评论

学校读者我要写书评

暂无评论

Robust crowdsourced learning

Robust crowdsourced learning

引用

IEEE International Conference on Big Data

作者： Zhiquan Liu Luo Luo Wu-Jun Li Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China

In general, a large amount of labels are needed for supervised learning algorithms to achieve satisfactory performance. It's typically very time-consuming and money-consuming to get such kind of labeled data. Recently, crowdsourcing services provide an effective way to collect labeled data with much lower cost. Hence, crowdsourced learning (CL), which performs learning with labeled data collected from crowdsourcing services, has become a very hot and interesting research topic in recent years. Most existing CL methods exploit only the labels from different workers (annotators) for learning while ignoring the attributes of the instances. In many real applications, the attributes of the instances are actually the most discriminative information for learning. Hence, CL methods with attributes have attracted more and more attention from CL researchers. One representative model of such kind is the personal classifier (PC) model, which has achieved the state-of-the-art performance. However, the PC model makes an unreasonable assumption that all the workers contribute equally to the final classification. This contradicts the fact that different workers have different quality (ability) for data labeling. In this paper, we propose a novel model, called robust personal classifier (RPC), for robust crowdsourced learning. Our model can automatically learn an expertise score for each worker. This expertise score reflects the inherent quality of each worker. The final classifier of our RPC model gives high weights for good workers and low weights for poor workers or spammers, which is more reasonable than PC model with equal weights for all workers. Furthermore, the learned expertise score can be used to eliminate spammers or low-quality workers. Experiments on simulated datasets and UCI datasets show that the proposed model can dramatically outperform the baseline models such as PC model in terms of classification accuracy and ability to detect spammers.

关键词： Training Robustness Logistics Noise measurement labeling Optimization Accuracy

来源：评论

学校读者我要写书评

暂无评论

A sociality-aware approach to computing backbone in mobile opportunistic networks

A sociality-aware approach to computing backbone in mobile o...

引用

2013 IEEE Global Communications Conference (GLOBECOM)

作者： Tong Liu Yanmin Zhu Ruobing Jiang Bo Li Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Lab of Scalable Computing and Systems Hong Kong University of Science and Technology

ISBN: (纸本)9781479913510

There are increasing interests on mobile opportunistic networks which have promising applications. Constructing a mobile backbone can effectively improve the packet delivery performance of a mobile opportunistic network by excluding poor relay nodes and reducing packet collisions. However, it is highly challenging to construct an effective mobile backbone because of the absence of the quantitative relationship between the network performance and the selection of backbone nodes, and expositive search space. As nodes exhibit clear sociality observed in previous studies, We explicitly take such node sociality into account when computing the backbone for mobile opportunistic networks and we incrementally propose three algorithms for computing the mobile backbone. Trace-driven simulations have been conducted and simulation results demonstrate that the sociality-aware algorithms can achieve low delivery delay and high delivery ratio.

关键词： Mobile communication Mobile computing Delays Algorithm design and analysis Relays Time complexity Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

An evaluation of vehicular networks with real vehicular GPS traces

引用

Eurasip Journal on Wireless Communications and Networking 2013年第1期2013卷 1页

作者： Zhu, Yanmin Chen, Chao Gao, Min Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Shanghai Key Lab of Scalable Computing and Systems Shanghai 200240 China HKUST Fok Ying Tung Graduate School Guangzhou 510730 China

Vehicular networks have attracted increasing attention from both the academy and industry. Applications of vehicular networks require efficient data communications between vehicles, whose performance is concerned with delivery ratio, delivery delay, and routing cost. The most previous work of routing in vehicular networks assumes oversimplified node mobility when evaluating the performance of vehicular networks, e.g., random mobility or artificial movement traces, which fails to reflect the inherent complexity of real vehicular networks. To understand the achievable performance of vehicular networks under real and complex environments, we first comprehensively analyze the affecting factors that may influence the performance of vehicular networks and then introduce four representative routing algorithms of vehicular networks, i.e., Epidemic, AODV, GPSR, and MaxProp. Next, we develop an NS-2 simulation framework incorporating a large dataset of real taxi GPS traces collected from around 2,600 taxis in shanghai, China. With this framework, we have implemented the four routing protocols. Extensive trace-driven simulations have been performed to explore the achievable performance of real vehicular networks. The impact of the controllable affecting factors is investigated, such as number of nodes, traffic load, packet TTL, transmission range, and propagation model. Simulation results show that a real vehicular network has surprisingly poor data delivery performance under a wide range of network configurations for all the routing protocols. This strongly suggests that the challenging characteristics of vehicular networks, such as unique node mobility, constraints of road topology, need further exploration. © 2013 Liao et al.;licensee Springer.

关键词： Routing protocols

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：