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3rd International Conference on Data Management in Grid and Peer-to-Peer Systems, Globe 2010

作者： Chong, Suan Khai Gaber, Mohamed Medhat Krishnaswamy, Shonali Loke, Seng Wai Centre for Distributed Systems and Software Engineering Monash University 900 Dandenong Rd Caulfield EastVIC3145 Australia School of Computing University of Portsmouth Portsmouth HampshirePO1 3HE United Kingdom Department of Computer Science and Computer Engineering La Trobe University VIC3086 Australia

ISBN: (纸本)9783642230738

Extensive data generated by peers of nodes in wireless sensor networks (WSNs) needs to be analysed and processed in order to extract information that is meaningful to the user. Data processing techniques that achieve this goal on sensor nodes are required to operate while meeting resource constraints such as memory and power to prolong a sensor network’s lifetime. This survey serves to provide a comprehensive examination of such techniques, enabling developers of WSN applications to select and implement data processing techniques that perform efficiently for their intended WSN application. It presents a general analysis of the issue of energy conservation in sensor networks and an up-to-date classification and evaluation of data processing techniques that have factored in energy constraints of sensors. © Springer-Verlag Berlin Heidelberg 2011.

关键词： Sensor nodes

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Sample subset optimization for classifying imbalanced biological data

Sample subset optimization for classifying imbalanced biolog...

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作者： Yang, Pengyi Zhang, Zili Zhou, Bing B. Zomaya, Albert Y. School of Information Technologies University of Sydney NSW 2006 Australia NICTA Australian Technology Park Eveleigh NSW 2015 Australia Centre for Distributed and High Performance Computing University of Sydney NSW 2006 Australia Faculty of Computer and Information Science Southwest University CQ 400715 China School of Information Technology Deakin University VIC 3217 Australia

ISBN: (纸本)9783642208461

Data in many biological problems are often compounded by imbalanced class distribution. That is, the positive examples may largely outnumbered by the negative examples. Many classification algorithms such as support vector machine (SVM) are sensitive to data with imbalanced class distribution, and result in a suboptimal classification. It is desirable to compensate the imbalance effect in model training for more accurate classification. In this study, we propose a sample subset optimization technique for classifying biological data with moderate and extremely high imbalanced class distributions. By using this optimization technique with an ensemble of SVMs, we build multiple roughly balanced SVM base classifiers, each trained on an optimized sample subset. The experimental results demonstrate that the ensemble of SVMs created by our sample subset optimization technique can achieve higher area under the ROC curve (AUC) value than popular sampling approaches such as random over-/under-sampling;SMOTE sampling, and those in widely used ensemble approaches such as bagging and boosting. © 2011 Springer-Verlag.

关键词： Support vector machines

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An Energy-Efficient Scheduling Algorithm for Sporadic Real-Time Tasks in Multiprocessor Systems

An Energy-Efficient Scheduling Algorithm for Sporadic Real-T...

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

作者： Dong-Song Zhang Fang-Yuan Chen Hong-Hua Li Shi-Yao Jin De-Ke Guo National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China School of Computing Science Simon Fraser University Burnaby BC Canada National Laboratory for Information System Engineering School of Information Systems and Management National University of Defense Technology Changsha China

As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, the real-time energy-efficient scheduling in multiprocessor systems becomes an urgent problem to reduce the system energy consumption while meeting real-time constraints. For a multiprocessor with independent DVFS and DPM at each processor, this paper proposes an energy-efficient real-time scheduling algorithm named LRE-DVFS-EACH, based on LRE-TL which is an optimal real-time scheduling algorithm for sporadic tasks. LRE-DVFS-EACH utilizes the concept of TL plane and the idea of fluid scheduling to dynamically scale the voltage and frequency of processors at the initial time of each TL plane as well as the release time of a sporadic task in each TL plane. Consequently, LRE-DVFS-EACH can obtain a reasonable tradeoff between the real-time constraints and the energy saving. LRE-DVFS-EACH is also adaptive to the change of workload caused by the dynamic release of sporadic tasks, which can obtain more energy savings. The experimental results show that compared with existing algorithms, LRE-DVFS-EACH can not only guarantee the optimal feasibility of sporadic tasks, but also achieve more energy savings in all cases, especially in the case of high workloads.

关键词： Real time systems Scheduling algorithm Time frequency analysis Scheduling Heuristic algorithms Power demand

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Scientific Workflow Makespan Reduction through Cloud Augmented Desktop Grids

Scientific Workflow Makespan Reduction through Cloud Augment...

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

作者： Christopher J. Reynolds Stephen Winter Gabor Z. Terstyanszky Tamas Kiss Pamela Greenwell Sandor Acs Peter Kacsuk Centre for Parallel Computing School of Electronics and Computer Science University of Westminster London UK Department of Biomedical Sciences University of Westminster London UK MTA SZTAKI Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

Scientific workflows are common in biomedical research, particularly for molecular docking simulations such as those used in drug discovery. Such workflows typically involve data distribution between computationally demanding stages which are usually mapped onto large scale compute resources. Volunteer or Desktop Grid (DG) computing can provide such infrastructure but has limitations resulting from the heterogeneous nature of the compute nodes. These constraints mean that reducing the make span of a given workflow stage submitted to a DG becomes problematic. Late jobs can significantly affect the make span, often completing long after the bulk of the computation has finished. In this paper we present a system capable of significantly reducing the make span of a scientific workflow. Our system comprises a DG which is dynamically augmented with an infrastructure as a service (IaaS) Cloud. Using this solution, the Cloud resources are used to process replicated late jobs. Our system comprises a core component termed the scheduler, which implements an algorithm to perform late job detection, Cloud resource management (instantiation and reuse), and job monitoring. We offer a formal definition of this algorithm, whilst we also provide an evaluation of our prototype using a production scientific workflow.

关键词： Bridges Portals Servers Monitoring Cloud computing computer architecture

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An Energy-Efficient Kernel Framework for Large-Scale Data Modeling and Classification

An Energy-Efficient Kernel Framework for Large-Scale Data Mo...

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IEEE International Symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Paul D. Yoo Jason W.P. Ng Albert Y. Zomaya Department Computer Engineering Etisalat British Telecom (BT) Innovation Centre Khalifa University of Science Technology and Research Abu Dhabi UAE Centre of Distributed and High Performance Computing School of Information Technologies (J12) University of Sydney NSW Australia

Energy-efficient computing has now become a key challenge not only for data-center operations, but also for many other energy-driven systems, with the focus on reducing of all energy-related costs, and operational expenses, as well as its corresponding and environmental impacts. Intelligent machine-learning systems are typically performance driven. For instance, most non-parametric model-free approaches are often known to require high computational cost in order to find the global optima. Designing more accurate machine-learning systems to satisfy the market needs will hence lead to a higher likelihood of energy waste due to the increased computational cost. This paper thus introduces an energy-efficient framework for large-scale data modeling and classification. It can achieve a test error comparable to or better than the state-of-the-art machine-learning models, while at the same time, maintaining a low computational cost when dealing with large-scale data. The effectiveness of the proposed approaches has been demonstrated by our experiments with two large-scale KDD datasets: Mtv-1 and Mtv-2.

关键词： Electromyography Machine learning Energy efficiency distributed processing

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Energy-aware data processing techniques for wireless sensor networks: A review

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2011年 6790 LNCS卷 117-137页

作者： Chong, Suan Khai Gaber, Mohamed Medhat Krishnaswamy, Shonali Loke, Seng Wai Centre for Distributed Systems and Software Engineering Monash University 900 Dandenong Rd Caulfield East VIC 3145 Australia School of Computing University of Portsmouth Portsmouth Hampshire PO1 3HE United Kingdom Department of Computer Science and Computer Engineering La Trobe University Victoria 3086 Australia

ISBN: (纸本)9783642230738

Extensive data generated by peers of nodes in wireless sensor networks (WSNs) needs to be analysed and processed in order to extract information that is meaningful to the user. Data processing techniques that achieve this goal on sensor nodes are required to operate while meeting resource constraints such as memory and power to prolong a sensor network's lifetime. This survey serves to provide a comprehensive examination of such techniques, enabling developers of WSN applications to select and implement data processing techniques that perform efficiently for their intended WSN application. It presents a general analysis of the issue of energy conservation in sensor networks and an up-to-date classification and evaluation of data processing techniques that have factored in energy constraints of sensors. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Sensor nodes

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A framework of Intelligent Environment with Smart-Active Objects (IESAO) for flexible and efficient crowd simulation 10

A framework of Intelligent Environment with Smart-Active Obj...

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Proceedings of the 2010 Spring Simulation Multiconference

作者： Liang, Anson Yuanxi Low, Malcolm Yoke Hean Lees, Michael Harold Cai, Wentong Zhou, Suiping Parallel and Distributed Computing Centre School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore

ISBN: (纸本)9781450300698

Agent-based crowd simulation has been widely applied in the analysis of evacuation safety under disastrous and terrorist circumstances. In crowd simulation, the virtual environment plays an important role in influencing human behavior and defining the scenario. The concepts of informed environment and smart objects have been adopted to improve the realism of the simulation by embedding semantic information in the environment. However, there is no formal approach of using these concepts in crowd simulation. In this paper, we propose a flexible and efficient framework of Intelligent Environment with Smart and Active Objects (IESAO). We properly define different types of entities and interactions, and describe the approach of modeling environment-related behavior in the intelligent environment. This framework gives a new perspective of modeling human behavior in the design phase. An implementation and a case study are discussed to show the flexibility and efficiency of this framework. © 2010 SCS.

关键词： Semantics

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Visualization and simulation tool for automated stowage plan generation system

Visualization and simulation tool for automated stowage plan...

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作者： Aye, Win Cho Low, Malcolm Yoke Hean Ying, Huang Shell Jing, Hsu Wen Fan, Liu Min, Zeng Parallel and Distributed Computing Centre School of Computer Engineering Nanyang Technological University Nanyang Avenue Singapore 639798 Singapore

ISBN: (纸本)9789881701282

This paper presents a tool for the visualization and simulation of automated stowage plan generation for large containership. The stowage plan is generated automatically based on a heuristic algorithm. The allocation algorithm generates different stowage plan in three main stages which includes basic allocation, special container allocation and various stages of stability adjustments. The purpose of this study is to describe how visualization of stowage plan and simulation of allocation sequences can help in the formulation of new and better stowage allocation algorithm.

关键词： Visualization

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Self-adaptive mechanism for multi-objective evolutionary algorithms

Self-adaptive mechanism for multi-objective evolutionary alg...

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作者： Zeng, Fanchao Low, Malcolm Yoke Hean Decraene, James Zhou, Suiping Cai, Wentong Parallel and Distributed Computing Centre School of Computer Engineering Nanyang Technological University Nanyang Avenue Singapore 639798 Singapore

ISBN: (纸本)9789881701282

Evolutionary algorithms can efficiently solve multi-objective optimization problems (MOPs) by obtaining diverse and near-optimal solution sets. However, the performance of multi-objective evolutionary algorithms (MOEAs) is often limited by the suitability of their corresponding parameter settings with respect to different optimization problems. The tuning of the parameters is a crucial task which concerns resolving the conflicting goals of convergence and diversity. Moreover, parameter tuning is a time-consuming trial-and-error optimization process which restricts the applicability of MOEAs to provide real-time decision support. To address this issue, we propose a self-adaptive mechanism (SAM) to exploit and optimize the balance between exploration and exploitation during the evolutionary search. This "explore first and exploit later" approach is addressed through the automated and dynamic adjustment of the distribution index of the simulated binary crossover (SBX) operator. Our experimental results suggest that SAM can produce satisfactory results for different problem sets without having to predefine/pre-optimize the MOEA's parameters. SAM can effectively alleviate the tedious process of parameter tuning thus making on-line decision support using MOEA more feasible.

关键词： Evolutionary algorithms

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Improving ship stability in automated stowage planning for large containerships

Improving ship stability in automated stowage planning for l...

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作者： Min, Zeng Low, Malcolm Yoke Hean Jing, Hsu Wen Ying, Huang Shell Fan, Liu Aye, Win Cho Parallel and Distributed Computing Centre School of Computer Engineering Nanyang Technological University Nanyang Avenue Singapore 639798 Singapore

ISBN: (纸本)9789881701282

Stowage planning for container vessels concerns the core competence of the shipping lines. As such, automated stowage planning has attracted much research in the past two decades, but with few documented successes. In an ongoing project, we are developing a prototype stowage planning system aiming for large containerships. The system consists of three modules: the stowage plan generator, the stability adjustment module, and the optimization engine. This paper mainly focuses on the stability adjustment module. The objective of the stability adjustment module is to check the global ship stability of the stowage plan produced by the stowage plan generator and resolve the stability issues by applying a heuristic algorithm to search for alternative feasible locations for containers that violate some of the stability criteria. We demonstrate that the procedure proposed is capable of solving the stability problems for a large containership with more than 5000 TEUs.

关键词： Automation

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