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IEEE International Conference on Utility and Cloud computing (UCC)

作者： Saurabh Kumar Garg Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia

As interest in adopting Cloud computing for various applications is rapidly growing, it is important to understand how these applications and systems will perform when deployed on Clouds. Due to the scale and complexity of shared resources, it is often hard to analyze the performance of new scheduling and provisioning algorithms on actual Cloud test beds. Therefore, simulation tools are becoming more and more important in the evaluation of the Cloud computing model. Simulation tools allow researchers to rapidly evaluate the efficiency, performance and reliability of their new algorithms on a large heterogeneous Cloud infrastructure. However, current solutions lack either advanced application models such as message passing applications and workflows or scalable network model of data center. To fill this gap, we have extended a popular Cloud simulator (CloudSim) with a scalable network and generalized application model, which allows more accurate evaluation of scheduling and resource provisioning policies to optimize the performance of a Cloud infrastructure.

关键词： Computational modeling Data models Cloud computing Network topology Bandwidth Processor scheduling Virtual machining

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Resource Provisioning Policies to Increase IaaS Provider's Profit in a Federated Cloud Environment

Resource Provisioning Policies to Increase IaaS Provider's P...

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

作者： Adel Nadjaran Toosi Rodrigo N. Calheiros Ruppa K. Thulasiram Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia Department of Computer Science University of Manitoba Canada

Cloud Federation is a recent paradigm that helps Infrastructure as a Service (IaaS) providers to overcome resource limitation during spikes in demand for Virtual Machines (VMs) by outsourcing requests to other federation members. IaaS providers also have the option of terminating spot VMs, i.e, cheaper VMs that can be canceled to free resources for more profitable VM requests. By both approaches, providers can expect to reject less profitable requests. For IaaS providers, pricing and profit are two important factors, in addition to maintaining a high Quality of Service (QoS) and utilization of their resources to remain in the business. For this, a clear understanding of the usage pattern, types of requests, and infrastructure costs are necessary while making decisions to terminate spot VMs, outsourcing or contributing to the federation. In this paper, we propose policies that help in the decision-making process to increase resources utilization and profit. Simulation results indicate that the proposed policies enhance the profit, utilization, and QoS (smaller number of rejected VM requests) in a Cloud federation environment.

关键词： Outsourcing Quality of service Mathematical model Equations Pricing Computational modeling Availability

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Partitioning networks into communities by message passing

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Physical Review E 2011年第1期83卷 016115-016115页

作者： Darong Lai Christine Nardini Hongtao Lu MOE-Microsoft Laboratory for Intelligent Computing and Intelligent Systems Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dong Chuan Road 200240 Shanghai China

Community structures are found to exist ubiquitously in a number of systems conveniently represented as complex networks. Partitioning networks into communities is thus important and crucial to both capture and simplify these systems’ complexity. The prevalent and standard approach to meet this goal is related to the maximization of a quality function, modularity, which measures the goodness of a partition of a network into communities. However, it has recently been found that modularity maximization suffers from a resolution limit, which prevents its effectiveness and range of applications. Even when neglecting the resolution limit, methods designed for detecting communities in undirected networks cannot always be easily extended, and even less directly applied, to directed networks (for which specifically designed community detection methods are very limited). Furthermore, real-world networks are frequently found to possess hierarchical structure and the problem of revealing such type of structure is far from being addressed. In this paper, we propose a scheme that partitions networks into communities by electing community leaders via message passing between nodes. Using random walk on networks, this scheme derives an effective similarity measure between nodes, which is closely related to community memberships of nodes. Importantly, this approach can be applied to a very broad range of networks types. In fact, the successful validation of the proposed scheme on real and synthetic networks shows that this approach can effectively (i) address the problem of resolution limit and (ii) find communities in both directed and undirected networks within a unified framework, including revealing multiple levels of robust community partitions.

关键词： PARALLEL algorithms COMMUNITY organization WIRELESS sensor nodes MODULARITY (engineering) RANDOM walks (Mathematics)

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Rank-SIFT: Learning to rank repeatable local interest points

Rank-SIFT: Learning to rank repeatable local interest points

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作者： Li, Bing Xiao, Rong Li, Zhiwei Cai, Rui Lu, Bao-Liang Zhang, Lei Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Microsoft Research Asia Beijing 100190 China MOE Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781457703942

Scale-invariant feature transform (SIFT) has been well studied in recent years. Most related research efforts focused on designing and learning effective descriptors to characterize a local interest point. However, how to identify stable local interest points is still a very challenging problem. In this paper, we propose a set of differential features, and based on them we adopt a data-driven approach to learn a ranking function to sort local interest points according to their stabilities across images containing the same visual objects. Compared with the handcrafted rule-based method used by the standard SIFT algorithm, our algorithm substantially improves the stability of detected local interest point on a very challenging benchmark dataset, in which images were generated under very different imaging conditions. Experimental results on the Oxford and PASCAL databases further demonstrate the superior performance of the proposed algorithm on both object image retrieval and category recognition. © 2011 IEEE.

关键词： Image retrieval

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SOS: A Safe, Ordered, and Speedy Emergency Navigation Algorithm in Wireless Sensor Networks

SOS: A Safe, Ordered, and Speedy Emergency Navigation Algori...

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International Conference on computer Communications and Networks (ICCCN)

作者： Andong Zhan Fan Wu Guihai Chen Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiaotong University China Department of Computer Science Johns Hopkins University USA

Wireless sensor networks can play an important role in detecting disasters and navigating the personnel out of dangerous areas. In emergency navigation applications, personal safety and evacuation time of human beings are commonly considered criteria. Consequently, existing works mainly focus on finding the shortest safe path for each person. However, in practice, the number of people may be much larger than the safety capacities of the computed evacuation paths. This may lead to congestion on the evacuation paths, and result in longer evacuation time and even casualty increment. In this paper, we model the problem of emergency navigation as a network flow scheduling problem and present a distributed algorithm, namely SOS, to solve it. We also conduct extensive and large-scale simulations to evaluate the performance of SOS. Numerical results show that SOS achieves superior performance to existing approaches in terms of average and last evacuation time, with low overhead.

关键词： Navigation Safety Schedules Wireless sensor networks Logic gates Algorithm design and analysis Distributed algorithms

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Private Cloud System Based on BOINC with Support for Parallel and Distributed Simulation

Private Cloud System Based on BOINC with Support for Paralle...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Yihua Wu Jian Cao Minglu Li Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Cloud computing provides an efficient way to expose computing capabilities as sophisticated services that can be accessed remotely. It eliminates the need for organizations to maintain extensive infrastructures. Parallel and distributed simulation over cloud will bring us not only the increased productivity and efficiency but also the lower barrier for usage. This paper introduces a private cloud system based on BOINC which supports parallel and distributed simulation. The design idea and implementation method of each component for our system is discussed in detail.

关键词： Computational modeling Monitoring Cloud computing Load modeling Servers Probes

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QoS-aware Deployment of Network of Virtual Appliances Across Multiple Clouds

QoS-aware Deployment of Network of Virtual Appliances Across...

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IEEE International Conference on Cloud computing Technology and science (CloudCom)

作者： Amir Vahid Dastjerdi Saurabh Kumar Garg Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering University of Melbourne Parkville VIC Australia

Cloud computing paradigm allows on-demand access to computing and storages services over the Internet. To solve the complexity of application deployment in Cloud infrastructure, virtual appliances, pre-configured, ready-to-run applications are emerging as a breakthrough technology. However, an automated approach for deploying network of appliances is required to guarantee minimum deployment cost, low latency, and high reliability. In this paper, we propose and compare two different deployment approaches: Forward-checking-based backtracking (FCBB) and genetic-based. They take into account Quality of Service (QoS) criteria such as reliability, data communication cost, and latency between multiple Clouds to choose the most appropriate combination of virtual machines and appliances. We evaluate our approach using a real case study and different request types. Experimental results show both algorithms reach near optimal solution. Further, we investigate effects of factors such as latency requirements, and data communication between appliances on the performance of the algorithms and placement of appliances across multiple Clouds.

关键词： Home appliances Quality of service Servers Monitoring Cloud computing Availability

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Compressive Sensing Approach to Urban Traffic Sensing

Compressive Sensing Approach to Urban Traffic Sensing

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International Conference on Distributed computing systems

作者： Zhi Li Yanmin Zhu Hongzi Zhu Minglu Li Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University China University of Waterloo Canada

Traffic sensing is crucial to a number of tasks such as traffic management and city road network engineering. We build a traffic sensing system with probe vehicles for metropolitan scale traffic sensing. Each probe vehicle senses its instant speed and position periodically and sensory data of probe vehicles can be aggregated for traffic sensing. However, there is a critical issue that the sensory data contain spatiotemporal vacancies with no reports. This is a result of the naturally uneven distribution of probe vehicles in both spatial and temporal dimensions since they move at their own wills. This paper proposes a new approach based on compressive sensing to large-scale traffic sensing in urban areas. We mine the extensive real trace datasets of taxies in an urban environment with principal component analysis and reveal the existence of hidden structures with sensory traffic data that underpins the compressive sensing approach. By exploiting the hidden structures, an efficient algorithm is proposed for finding the best estimate traffic condition matrix by minimizing the rank of the estimate matrix. With extensive trace-driven experiments, we demonstrate that the proposed algorithm outperforms a number of alternative algorithms. Surprisingly, we show that our algorithm can achieve an estimation error of as low as 20% even when more than 80% of sensory data are not present.

关键词： Vehicles Sensors Probes Roads Compressed sensing Matrix decomposition Algorithm design and analysis

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SOLARE: Self-Organizing Latency-Aware Resource Ensemble

SOLARE: Self-Organizing Latency-Aware Resource Ensemble

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

作者： Heungsik Eom David Isaac Wolinsky Renato J. Figueiredo Advanced Computing and Information Systems Laboratory Department of Electrical and Computer Engineering University of Florida Gainesville USA Department of Computer Science Yale University New Haven USA

This paper proposes and evaluates Self-Organizing Latency-Aware Resource Ensemble (SOLARE), a peer-to-peer self-organizing and self-managing cluster system based upon network coordinates and utility functions. In contrast to previous works, SOLARE is a fully decentralized clustering algorithm without any central units such as servers, super peers, cluster heads or landmarks. Furthermore, SOLARE allows for adaptability to dynamic network changes by monitoring the utility of a cluster and migrating nodes to other higher-utility clusters when the utility of an existing cluster is low. Quantitative, simulation-driven evaluations show that SOLARE is able to satisfy user demands expressed by utility functions that integrate system parameters in terms of intra cluster latencies and the number of cluster members. Also, we verify the ability of SOLARE to adapt to dynamic network changes through simulation based experiments that consider the number of nodes which migrate into another cluster and average utility value as nodes join SOLARE.

关键词： Peer to peer computing Clustering algorithms Monitoring computer architecture Servers Performance evaluation Heuristic algorithms

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Enhance Top-down method with Meta-Classification for Very Large-scale Hierarchical Classification 5

Enhance Top-down method with Meta-Classification for Very La...

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5th International Joint Conference on Natural Language Processing, IJCNLP 2011

作者： Wang, Xiao-Lin Zhao, Hai Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China

ISBN: (纸本)9789744665645

Recent large-scale hierarchical classification tasks typically have tens of thousands of classes as well as a large number of samples, for which the dominant solution is the top-down method due to computational complexity. However, the top-down method suffers from accuracy deficiency, that is, its accuracy is generally lower than that of the flat approach of 1-vs-Rest. In this paper, we employ meta-classification technique to enhance the classifying procedure of the top-down method. We analyze the proposed method on the aspect of accuracy, and then test it with two real-world large-scale data sets. Our method both maintains the efficiency of the conventional top-down method and provides competitive classification accuracies. © 2011 AFNLP

关键词： Natural language processing systems

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