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A Complementary Modularized Ramp Metering Approach Based on Iterative Learning Control and ALINEA

IEEE Transactions on Intelligent Transportation systems 2011年第4期12卷 1305-1318页

作者： Zhongsheng Hou Xin Xu Jingwen Yan Jian-Xin Xu Gang Xiong Advanced Control Systems Laboratory School of Electronic and Information Engineering Beijing Jiaotong University BJTU Beijing China Institute of Automation College of Mechatronics and Automation National University of Defense Technology Changsha China Department of Electrical and Computer Engineering National University of Singapore Singapore Automation Institute Chinese Academy of Sciences (CAS) Beijing China

Ramp metering is an effective tool for traffic management on freeway networks. In this paper, we apply iterative learning control (ILC) to address ramp metering in a macroscopic-level freeway environment. By formulating the original ramp metering problem as an output regulating and disturbance rejection problem, ILC has been applied to control the traffic response. The learning mechanism is further combined with Asservissement Linéaire d'Entrée Autoroutière (ALINEA) in a complementary manner to achieve the desired control performance. The ILC-based ramp metering strategy and the modified modularized ramp metering approach based on ILC and ALINEA in the presence of input constraints are also analyzed to highlight the advantages and the robustness of the proposed methods. Extensive simulations are given to verify the effectiveness of the proposed approaches.

关键词： Traffic control Mathematical model Convergence Learning systems Feedforward neural networks

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Multi-objective virtual machine placement in virtualized data center environments

Multi-objective virtual machine placement in virtualized dat...

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2010 IEEE/ACM International Conference on Green computing and Communications, GreenCom 2010, 2010 IEEE/ACM International Conference on Cyber, Physical and Social computing, CPSCom 2010

作者： Xu, Jing Fortes, José A. B. Advanced Computing and Information Systems Laboratory Department of Electrical and Computer Engineering University of Florida PO Box 116200 Gainesville FL 32611-6200 United States

ISBN: (纸本)9780769543314

Server consolidation using virtualization technology has become increasingly important for improving data center efficiency. It enables one physical server to host multiple independent virtual machines (VMs), and the transparent movement of workloads from one server to another. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for large-scale data centers. In this paper, a two-level control system is proposed to manage the mappings of workloads to VMs and VMs to physical resources. The focus is on the VM placement problem which is posed as a multi-objective optimization problem of simultaneously minimizing total resource wastage, power consumption and thermal dissipation costs. An improved genetic algorithm with fuzzy multi-objective evaluation is proposed for efficiently searching the large solution space and conveniently combining possibly conflicting objectives. A simulation-based evaluation using power-consumption and thermal-dissipation models (learned from profiling of a BladeCenter), demonstrates the good performance, scalability and robustness of our proposed approach when compared with four well-known bin-packing algorithms and two single-objective approaches. Our approach can seek and find solutions that exhibit good balance among the conflicting objectives while others cannot. © 2010 IEEE.

关键词： Virtual machine

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Looking around with your brain in a virtual world

Looking around with your brain in a virtual world

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IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)

作者： Danny Plass-Oude Bos Matthieu Duvinage Oytun Oktay Jaime Delgado Saa Huseyin Guruler Ayhan Istanbullu Marijn van Vliet Bram van de Laar Mannes Poel Linsey Roijendijk Luca Tonin Ali Bahramisharif Boris Reuderink HMI group Faculty of EEMCS University of Twente Netherlands TCTS Laboratory Electrical Engineering University of Mons Belgium Electronics and Communications Corlu Engineering Namik Kemal Üniversitesi Turkey IEE Group of Robotics and Intelligent Systems Universidad del Norte Colombia Faculty of Engineering and Natural Sciences VPA Laboratory Sabanci University Turkey Department of Electronics and Computer Education Faculty of Technical Education Mugla University Turkey Faculty of Computer Engineering Balikesir University Turkey Donders Institute for Brain Cognition and Behaviour Radboud University Nijmegen Nijmegen Netherlands CNBI École Polytechnique Fédérale de Lausanne Switzerland Institute for Computing and Information Sciences Radboud University Nijmegen Nijmegen Netherlands

Offline analysis pipelines have been developed and evaluated for the detection of covert attention from electroen-cephalography recordings, and the detection of overt attention in terms of eye movement based on electrooculographic measurements. Some additional analysis were done in order to prepare the pipelines for use in a real-time system. This real-time system and a game application in which these pipelines are to be used were implemented. The game is set in a virtual environment where player is a wildlife photographer on an uninhabited island. Overt attention is used to adjust the angle of the first person camera, when the player is tracking animals. When making a photograph, the animal will flee when it notices it is looked at directly, so covert attention is required to get a good shot. Future work will entail user tests with this system to evaluate usability, user experience, and characteristics of the signals related to overt and covert attention when used in such an immersive environment.

关键词： Pipelines Accuracy Electrooculography Electrodes Training Electroencephalography Games

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On iterative learning control design for tracking iteration-varying trajectories with high-order internal model

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控制理论与应用（英文版） 2010年第3期8卷 309-316页

作者： Chenkun YIN Jianxin XU Zhongsheng HOU Advanced Control Systems Laboratory School of Electronics and Information Engineering Beijing Jiaotong University Beijing 100044 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing 100044 China

In this paper, iterative learning control （ILC） design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models （HOLM）. An HOlM formulated as a polynomial operator between consecutive iterations describes the changes of desired trajectories in the iteration domain and makes the iterative learning problem become iteration varying. The classical ILC for tracking iteration-invariant reference trajectories, on the other hand, is a special case of HOlM where the polynomial renders to a unity coefficient or a special first-order internal model. By inserting the HOlM into P-type ILC, the tracking performance along the iteration axis is investigated for a class of continuous-time nonlinear systems. Time-weighted norm method is utilized to guarantee validity of proposed algorithm in a sense of data-driven control.

关键词： ILC High-order intemal model Iteration-varying Nonlinear systems Continuous-time

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On the Use of Machine Learning to Predict the Time and Resources Consumed by Applications 10

On the Use of Machine Learning to Predict the Time and Resou...

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IEEE/ACM International Symposium on Cluster computing and the Grid (CCGRID)

作者： Andréa Matsunaga José A.B. Fortes Advanced Computing and Information Systems Laboratory Department of Electrical and Computer Engineering University of Florida Gainesville FL USA

ISBN: (纸本)9780769540399

Most data centers, clouds and grids consist of multiple generations of computing systems, each with different performance profiles, posing a challenge to job schedulers in achieving the best usage of the infrastructure. A useful piece of information for scheduling jobs, typically not available, is the extent to which applications will use available resources once they are executed. This paper comparatively assesses the suitability of several machine learning techniques for predicting spatio temporal utilization of resources by applications. Modern machine learning techniques able to handle large number of attributes are used, taking into account application- and system-specific attributes (e.g., CPU micro architecture, size and speed of memory and storage, input data characteristics and input parameters). The work also extends an existing classification tree algorithm, called Predicting Query Runtime (PQR), to the regression problem by allowing the leaves of the tree to select the best regression method for each collection of data on leaves. The new method (PQR2) yields the best average percentage error, predicting execution time, memory and disk consumption for two bioinformatics applications, BLAST and RAxML, deployed on scenarios that differ in system and usage. In specific scenarios where usage is a non-linear function of system and application attributes, certain configurations of two other machine learning algorithms, Support Vector Machine and k-nearest neighbors, also yield competitive results. In addition, experiments show that the inclusion of system performance and application-specific attributes also improves the performance of machine learning algorithms investigated.

关键词： Machine learning Machine learning algorithms Processor scheduling Regression tree analysis Mesh generation Cloud computing Grid computing Classification tree analysis Classification algorithms Prediction algorithms

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Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments

Multi-Objective Virtual Machine Placement in Virtualized Dat...

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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： Jing Xu Jose A. B. Fortes Advanced Computing and Information Systems Laboratory Department of Electrical and Computer Engineering University of Florida Gainesville FL USA

Server consolidation using virtualization technology has become increasingly important for improving data center efficiency. It enables one physical server to host multiple independent virtual machines (VMs), and the transparent movement of workloads from one server to another. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for large-scale data centers. In this paper, a two-level control system is proposed to manage the mappings of workloads to VMs and VMs to physical resources. The focus is on the VM placement problem which is posed as a multi-objective optimization problem of simultaneously minimizing total resource wastage, power consumption and thermal dissipation costs. An improved genetic algorithm with fuzzy multi-objective evaluation is proposed for efficiently searching the large solution space and conveniently combining possibly conflicting objectives. The simulation-based evaluation using power-consumption and thermal-dissipation models based on profiling of a Blade Center, demonstrates the good performance, scalability and robustness of our proposed approach. Compared with four well-known bin-packing algorithms and two single-objective approaches, the solutions obtained from our approach seek good balance among the conflicting objectives while others cannot.

关键词： Servers Optimization Power demand Virtual machining Resource management Memory management Thermal management

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Compact method for modeling and simulation of memristor devices: Ion conductor chalcogenide-based memristor devices

Compact method for modeling and simulation of memristor devi...

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2010 IEEE/ACM International Symposium on Nanoscale Architectures, NANOARCH 2010

作者： Pino, Robinson E. Bohl, James W. McDonald, Nathan Wysocki, Bryant Rozwood, Peter Campbell, Kristy A. Oblea, Antonio Timilsina, Achyut Air Force Research Laboratory Information Directorate Advanced Computing Architectures Rome NY 13441 United States Boise State University Department of Electrical and Computer Engineering Boise ID 83725 United States

ISBN: (纸本)9781424480180

A compact model and simulation methodology for chalcogenide based memristor devices is proposed. From a microprocessor design view point, it is important to be able to simulate large numbers of devices within the integrated circuit architecture in order to speed up reliably the development process. Ideally, device models would accurately describe the characteristic device behavior and would be represented by single-valued equations without requiring the need for recursive or numerically intensive solutions. With this in mind, we have developed an empirical chalcogenide compact memristor model that accurately describes all regions of operations of memristor devices employing single-valued equations. ©2010 IEEE.

关键词： Memristors

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Integration of global and local information in videos for key frame extraction 11

Integration of global and local information in videos for ke...

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11th IEEE International Conference on information Reuse and Integration, IRI 2010

作者： Liu, Dianting Shyu, Mei-Ling Chen, Chao Chen, Shu-Ching Department of Electrical and Computer Engineering University of Miami Coral Gables FL 33124 United States Distributed Multimedia Information Systems Laboratory School of Computing and Information Sciences Florida International University Miami FL 33199 United States

ISBN: (纸本)9781424480975

Key frame extraction methods aim to obtain a set of frames that can efficiently represent and summarize video contents and be reused in many video retrieval-related applications. An effective set of key frames, viewed as a high-quality summary of the video, should include the major objects and events of the video, and contain little redundancy and overlapped content. In this paper, a new key frame extraction method is presented, which not only is based on the traditional idea of clustering in the feature extraction phase but also effectively reduces redundant frames using the integration of local and global information in videos. Experimental results on the TRECVid 2007 test video dataset have demonstrated the effectiveness of our proposed key frame extraction method in terms of the compression rate and retrieval precision. © 2010 IEEE.

关键词： Integration

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User-level virtual networking mechanisms to support virtual machine migration over multiple clouds

User-level virtual networking mechanisms to support virtual ...

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IEEE Globecom Workshops

作者： Maurício Tsugawa Pierre Riteau Andréa Matsunaga José Fortes Advanced Computing and Information Systems Laboratory Department of Electrical and Computer Engineering University of Florida Gainesville FL USA IRISA Laboratory Université de Rennes 1 Rennes France

Dynamic allocation of multiple cloud resources adapting to application needs over time can be achieved by taking advantage of wide-area VM live migration technologies. However, migration of VMs across different subnets, potentially in multiple clouds, requires networking support to keep the network state of moving VMs unchanged. Two problems make traditional solutions to machine mobility inefficient in this scenario: (1) administrative overheads due to coordination requirements between moving machines and the network infrastructure; and (2) degraded network performance of machines moved away from their “home” networks. New solutions are needed to efficiently support the migration of virtual machines over multiple cloud providers. The user-level virtual network architecture presented in this paper implements mechanisms to allow VM migration over clouds without requiring support from the physical network infrastructure, and automatically reconfiguring virtual networks to maximize the network performance of migrated virtual machines.

关键词： IP networks Virtual machining Routing Cloud computing Virtual machine monitors Software

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Studying the impact of synchronization frequency on scheduling tasks with dependencies in heterogeneous systems

Studying the impact of synchronization frequency on scheduli...

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作者： Andronikos, Theodore Ciorba, Florina M. Riakiotakis, Ioannis Papakonstantinou, George Chronopoulos, Anthony T. Department of Informatics Ionian University Corfu Greece Computing Systems Laboratory Department of Electrical and Computer Engineering National Technical University of Athens Greece Center for Advanced Vehicular Systems Mississippi State University MS United States Department of Computer Science University of Texas at San Antonio 6900 N. Loop 1604 West San Antonio TX 78249 United States

In this work, we develop and evaluate a theoretical model, which we then use to study the impact of the synchronization frequency on the performance of dynamic self-scheduling algorithms. These algorithms are used to parallelize loops with data dependencies on heterogeneous systems. The proposed model uses a formula to estimate the parallel time as a function of the synchronization frequency. Inter-node communication has been proven to be the dominant factor for the performance degradation of applications containing loops with data dependencies. The synchronization mechanism therefore requires careful fine-tuning in order to give the best possible performance. The proposed model determines the optimal synchronization frequency that results in the minimum parallel time. We use this model to study the impact of the synchronization frequency on the parallel execution of a computational kernel from image processing. For this kernel, the synchronization frequency giving the minimum parallel time predicted by our theoretical model was very close to the synchronization frequency giving the least parallel time in practice. We validate our model by extensive comparisons of the theoretically predicted parallel time and synchronization frequency against those obtained from practical experiments. The comparisons show that the proposed model is highly accurate, its predictions for the optimal synchronization frequency being within 0.0250% of the experimentally optimal synchronization frequency in the best case, and within 0.1750% of the experimentally optimal synchronization frequency in the worst case. Finally, the comparisons show that the proposed model improves on a previously existing model in heterogeneous systems, whereas it gives similar results in homogeneous systems. © 2010 Elsevier B.V. All rights reserved.

关键词： Synchronization

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