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Journal of Software 2011年第12 SPEC. ISSUE期6卷 2341-2349页

作者： Guan, Haibing Zhu, Erzhou Chen, Kai Ma, Ruhui He, Yunchao Deng, Haipeng Yang, Hongbo Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University Shanghai China

Dynamic binary translation (DBT) has attracted much attention as a powerful technique for the runtime adaptation of software among different ISAs. It offers unprecedented flexibility in the control and modification of a program during the runtime. However, its inherent high overhead has perplexed researchers for many years. In order to reduce the overhead of DBT, this paper presents a dynamic-static combined approach to reorganize the layout of software cache. Under this approach, we first employ an emulating execution to collect the profile information and the translated target code. Especially, the path of execution flow will be tracked. In the static phase, based on the profile information collected in the previous stage, we first use the method of code replicating to build the traces, and then reorganize the layout of the target code by putting the hottest traces at the top of the software cache. Because of exact prediction and improved locality, the execution stream will concentrate on a small area with less control transfer. This approach can greatly reduce the overhead of DBT on the condition that the program runs repeatedly. Experimental results on executing the SPEC 2000 benchmarks show that our approach can reduce more than 30% run time on average. © 2011 Academy Publisher.

关键词： Codes (symbols)

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Combining Static and Dynamic Analysis to Discover Software Vulnerabilities

Combining Static and Dynamic Analysis to Discover Software V...

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International Conference on Innovative Mobile and Internet Services in Ubiquitous computing (IMIS)

作者： Ruoyu Zhang Shiqiu Huang Zhengwei Qi Haibin Guan School of Software Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University China School of Electronic Information and Electrical Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University China

Dynamic taint analysis has been proved to be very effective in solving security problems recently, especially in software vulnerability detection and malicious behavior prevention. Unfortunately, most of current researches in this field focus on the runtime protection, and are incapable to discover the potential threat in the software. This paper describes a novel approach to overcome the limitation of traditional dynamic taint analysis by integrating static analysis into the system and presents framework SDCF. The framework translates the binary into assembly code and tracks the data flow. Then with static method, the system can get the important information which can't be gained at runtime, such as unexecuted part of the code. When this information is acquired, they will be provided to the client tools. The practicability of the framework is validated by implementing and evaluating a tool built on SDCF. The result of the experiments shows that our system is able to detect latent software vulnerabilities efficiently.

关键词： Software Registers Runtime Performance analysis Monitoring Testing Optimization

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Frontier-based multi-robot map exploration using Particle Swarm Optimization

Frontier-based multi-robot map exploration using Particle Sw...

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IEEE Swarm Intelligence Symposium, SIS

作者： Yiheng Wang Alei Liang Haibing Guan School of Software Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University Shanghai China Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University Shanghai China

Exploring an unknown environment using team of autonomous mobile robots is an important task in many real-world applications. Many existing map exploration algorithms are based on frontier, which is the boundary between unexplored space and known open space. In the context of multiple robots, the main problem of frontier-based algorithm is to choose appropriate target points for the individual robots so that they can efficiently explore the different part of the common area. This paper proposed a novel distributed frontier-based map exploration algorithm using Particle Swarm Optimization model for robot coordination. In this algorithm, the robot keeps moving to the nearby frontier to reduce the size of the unknown region, and is navigated towards frontier far away based on the PSO model after exploring the local area. The exploration is completed when there are no frontier cells on the map. Our algorithm has been implemented and tested both in simulation runs and real world experiment. The result shows that our method has a good scalability and efficiency.

关键词： Legged locomotion Robot kinematics Collision avoidance Algorithm design and analysis Robot sensing systems Redundancy

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Social relations model for collaborative filtering 25

Social relations model for collaborative filtering

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25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11

作者： Li, Wu-Jun Yeung, Dit-Yan Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781577355083

We propose a novel probabilistic model for collaborative filtering (CF), called SRMCoFi, which seamlessly integrates both linear and bilinear random effects into a principled framework. The formulation of SRMCoFi is supported by both social psychological experiments and statistical theories. Not only can many existing CF methods be seen as special cases of SRMCoFi, but it also integrates their advantages while simultaneously overcoming their disadvantages. The solid theoretical foundation of SRMCoFi is further supported by promising empirical results obtained in extensive experiments using real CF data sets on movie ratings. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Collaborative filtering

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An algorithm for self-organized aggregation of swarm robotics using timer

An algorithm for self-organized aggregation of swarm robotic...

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IEEE Swarm Intelligence Symposium, SIS

作者： Xinan Yan Alei Liang Haibing Guan School of Software and Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University Shanghai China

Aggregation is a basic collective behavior in biology, and is a prerequisite for many applications of swarm robotics. This paper proposes a new distributed algorithm for aggregation of swarm robotics under the constraints of no central control, no information about positions, and only local interaction among robots. Our control strategy contains two states, Search and Wait, for individual robot, as in the model of probabilistic finite state automata (PFSA). The main difference from PFSA is the way of how individual robot decides to leave from an aggregate. In our approach, each robot in an aggregate has a timer as its lifetime in the aggregate instead of having a leaving probability in PFSA. Further, to make all robots aggregate together, the key idea of our algorithm is that robots in small aggregate have shorter lifetime than those in large aggregate. The lifetime of individual robot in an aggregate is approximated as the lifetime of the aggregate, by making all the timers of the robots in the same aggregate have the same time settings, where the time is proportional to aggregate size and is updated only with the increase of aggregate size. By choosing linear function as the time setting of the timer, experiments based on simulator have been done. The results show that our algorithm is successful and scalable for large scale of robots, and indicate that the performance of the aggregation of all robots is improved by decreasing the lifetime of uncompleted aggregate in high robot density or increasing it in low robot density.

关键词： Aggregates Robot sensing systems Robot kinematics Wireless communication Search problems

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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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A Power and Performance Management Framework for Virtualized Server Clusters

A Power and Performance Management Framework for Virtualized...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Yongqiang Gao Zhengwei Qi Yubin Wu Rui Wang Liang Liu Jitao Xu Haibing Guan Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai China IBM Research-China Beijing China

Nowadays, one of the most important goals of data center management is to maximize their profit by minimizing power consumption and service-level agreement (SLA) violations of hosted applications. System dynamics make it difficult to implement optimization in both aspects on shared infrastructures. A majority of existing works either focused on one aspect or applied models that are trained offline for application-specific workload. In addition, virtualization is being widely used in large-scale data centers to attain basic benefits like fault and performance isolation, and to improve system manageability. A key challenge that comes with virtualization is to dynamically provisioning resources for virtual machines and optimize their capacity for meeting service level objectives at the lowest possible cost. In this paper, we present a hierarchical management framework to assure application-level performance while minimizing power consumption for virtualized data centers. A novelty of the management framework is to combine control theory with linear programming technique. Empirical results show that the proposed framework brings substantial energy saving, while ensuring application performance. Especially, the integration of the performance controller and the energy optimizer results in an energy saving of 43% on our hardware testbed.

关键词： Servers Resource management Power demand Virtual machining Time factors Energy consumption Central Processing Unit

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Generalized latent factor models for social network analysis

Generalized latent factor models for social network analysis

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22nd International Joint Conference on Artificial Intelligence, IJCAI 2011

作者： Li, Wu-Jun Yeung, Dit-Yan Zhang, Zhihua Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong College of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9781577355120

Homophily and stochastic equivalence are two primary features of interest in social networks. Recently, the multiplicative latent factor model (MLFM) is proposed to model social networks with directed links. Although MLFM can capture stochastic equivalence, it cannot model well homophily in networks. However, many real-world networks exhibit homophily or both homophily and stochastic equivalence, and hence the network structure of these networks cannot be modeled well by MLFM. In this paper, we propose a novel model, called generalized latent factor model (GLFM), for social network analysis by enhancing homophily modeling in MLFM. We devise a minorization-maximization (MM) algorithm with linear-time complexity and convergence guarantee to learn the model parameters. Extensive experiments on some real-world networks show that GLFM can effectively model homophily to dramatically outperform state-of-the-art methods.

关键词： Stochastic systems

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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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GPU-accelerated DNA distance matrix computation

Proceedings - 2011 6th Annual ChinaGrid Conference, ChinaGri...

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Proceedings - 2011 6th Annual ChinaGrid Conference, ChinaGrid 2011 2011年 42-47页

作者： Ying, Zhi Lin, Xinhua See, Simon Chong-Wee Li, Minglu Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of High Performance Computing AStar Singapore Singapore NVIDIA Corporation United States Shanghai Key Laboratory of Scalable Computing and Systems China

ISBN: (纸本)9780769544724

Distance matrix calculation used in phylogeny analysis is computational intensive. The growing sequences data sets necessitate fast computation method. This paper accelerate Felsenstein's DNADIST program by using OpenCL to exploit the great computation capability of graphic card. The GPU-accelerated DNADIST program achieves more than 12-fold speedup over the serial CPU program on a personal workstation with a 2.66GHz quad-core Intel CPU and an AMD HD5850 graphics card. And dual HD5850 cards on the same platform perform linear improvement of 24-fold speedup. The program also shows good performance portability by achieving 16-fold speedup with a NVIDIA Tesla C2050 card. © 2011 IEEE.

关键词： Graphics processing unit

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