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International Conference on Advanced Information Networking and Applications (AINA)

作者： Mohsen Amini Salehi Bahman Javadi Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia

Many applications in federated Grids have quality-of-service (QoS) constraints such as deadline. Admission control mechanisms assure QoS constraints of the applications by limiting the number of user requests accepted by a resource provider. However, in order to maximize their profit, resource owners are interested in accepting as many requests as possible. In these circumstances, the question that arises is: what is the effective number of requests that can be accepted by a resource provider in a way that the number of accepted external requests is maximized and, at the same time, QoS violations are minimized. In this paper, we answer this question in the context of a virtualized federated Grid environment, where each Grid serves requests from external users along with its local users and requests of local users have preemptive priority over external requests. We apply analytical queuing model to address this question. Additionally, we derive a preemption-aware admission control policy based on the proposed model. Simulation results under realistic working conditions indicate that the proposed policy improves the number of completed external requests (up to 25%). In terms of QoS violations, the 95% confidence interval of the average difference with other policies is between (14.79%, 18.56%).

关键词： Admission control Mathematical model Equations Analytical models Time factors Queueing analysis

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An Evolutionary Approach to Improve Connectivity Prediction in Mobile Wireless Sensor Networks

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Procedia computer science 2012年 10卷 1100-1105页

作者： Gustavo Medeiros de Araújo A.R. Pinto Jörg Kaiser Leandro Buss Becker Department of Automation and Control Systems Federal University of Santa Catarina Florianópolis Brazil Department of Distributed Systems Otto-Von-Guericke-Univesitat Magdeburg Germany Department of Computer Science and Statistics Paulista State University (UNESP) São Paulo Brazil

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

关键词： Wireless sensor networks Mobility Connectivity prediction Genetic Algorithm Classifier systems

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Simulation Optimization for Healthcare Emergency departments

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Procedia computer science 2012年 9卷 1464-1473页

作者： Eduardo Cabrera Manel Taboada Ma Luisa Iglesias Francisco Epelde Emilio Luque Computer Architecture and Operating Systems Department (CAOS) University Autonoma of Barcelona (UAB) Spain Tomàs Cerd‘a Computer Science School (UAB) Spain Hospital of Sabadell Consorci Sanitari Parc Taulí Barcelona Spain

This article presents an Agent-Based modeling (ABM) simulation to design a decision support system (DSS) for Healthcare Emergency department (ED). This DSS aims to aid EDs heads in setting up management guidelines to improve the operation of EDs. This ongoing research is being performed by the Research Group in Individual Oriented Modeling (IoM) at the University Autonoma of Barcelona (UAB) with close collaboration of Hospital ED Staff Team. The objective of the proposed ABM procedure is to optimize the performance of such complex and dynamic Healthcare EDs, because worldwide most of them are overcrowded, and unable to provide ad hoc care, quality and service. Exhaustive search (ES) optimization is used to find out the optimal ED staff configuration, which includes doctors, triage nurses, and admission personnel, i.e., a multidimensional problem. An index is proposed to minimize patient length of stay in the ED. The results obtained by using an alternative pipeline scheme to ES are promising and a better understanding of the problem is achieved. The impact of the pipeline scheme to reduce the computational cost of exhaustive search is outlined.

关键词： Optimization healthcare operational management emergency department agent-based simulation decision support systems computational cost

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Evolving radial basis function networks via GP for estimating fitness values using surrogate models

Evolving radial basis function networks via GP for estimatin...

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Congress on Evolutionary Computation

作者： Ahmed Kattan Edgar Galvan Evolutionary Design and Optimisation Group Computer Science Department Um Al-Qura University Saudi Arabia Distributed Systems Group School of Computer Science and Statistics Trinity College Dublin Ireland

In real-world problems with candidate solutions that are very expensive to evaluate, Surrogate Models (SMs) mimic the behaviour of the simulation model as closely as possible while being computationally cheaper to evaluate. Due to their nature, SMs can be seen as heuristics that can help to estimate the fitness of a candidate solution without having to evaluate it. In this paper, we propose a new SM based on Genetic Programming (GP) and Radial Basis Function Networks (RBFN), called GP-RBFN Surrogate. More specifically, we use GP to evolve both: the structure of a RBF and its parameters. The SM evolved by our algorithm is tested in one of the most studied NP-complete problem (MAX-SAT) and its performance is compared against RBFN Surrogate, GAs, Random Search and (1+1) ES. The results obtained by performing extensive empirical experiments indicate that our proposed approach outperforms the other four methods in terms of finding better solutions without the need of evaluating a large portion of candidate solutions.

关键词： Training Mathematical model Computational modeling Standards Approximation methods Search problems Optimization

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Verifying temporal properties of use-cases in natural language

Verifying temporal properties of use-cases in natural langua...

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8th International Symposium on Formal Aspects of Component Software, FACS 2011

作者： Simko, Viliam Hauzar, David Bures, Tomas Hnetynka, Petr Plasil, Frantisek Charles University Faculty of Mathematics and Physics Department of Distributed and Dependable Systems Malostranské náměstí 25 Prague 1 118 00 Czech Republic Institute of Computer Science Academy of Sciences of the Czech Republic Pod Vodárenskou věží 2 Prague 8 182 07 Czech Republic

ISBN: (纸本)9783642357428

This paper presents a semi-automated method that helps iteratively write use-cases in natural language and verify consistency of behavior encoded within them. In particular, this is beneficial when the use-cases are created simultaneously by multiple developers. The proposed method allows verifying the consistency of textual use-case specification by employing annotations in use-case steps that are transformed into temporal logic formulae and verified within a formal behavior model. A supporting tool for plain English use-case analysis is currently being enhanced by integrating the verification algorithm proposed in the paper. © 2012 Springer-Verlag.

关键词： Model checking

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Fail*: Towards a versatile fault-injection experiment framework

ARCS Workshops, ARCS 2012

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ARCS Workshops, ARCS 2012 2012年

作者： Schirmeier, Horst Hoffmann, Martin Kapitza, Rüdiger Lohmann, Daniel Spinczyk, Olaf Department of Computer Science 12 Technische Universität Dortmund Germany Department of Computer Science 4 Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Institute of Operating Systems and Computer Networks TU Braunschweig Germany

ISBN: (纸本)9781467319133

Many years of research on dependable, fault-tolerant software systems yielded many tool implementations for vulnerability analysis and experimental validation of resilience measures. We identify two disjoint classes of fault-injection (FI) experiment tools in the field, and argue that both are plagued by inherent deficiencies, such as insufficient target state access, little or no means to switch to another target system, and non-reusable experiment code. In this article, we present a novel design approach for a FI infrastructure that aims at combining the strengths of both classes. Our Fail* experiment framework provides carefully-chosen abstractions simplifying both the implementation of different simulator/hardware target backends and the reuse of experiment code, while retaining the ability for deep targetstate access for specialized FI experiments. An exemplary report on first experiences with a prototype implementation based on existing x86 and ARM simulators demonstrates the tool's versatility. © 2012 Gesellschaft fuer Informatk.

关键词： Software testing

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Kernel methods based approaches to image classification and retrieval

Kernel methods based approaches to image classification and ...

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International Conference on Advanced Computing, ICAC

作者： C. Chandra Sekhar Sasi Kumar Madhan Subhas Raj Kumar Buyya Department of Computer Science and Engineering Indian Institute of Technology Madras CDAC Pune Chronus Corporation Computer Science and Software Engineering and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne Australia

Support vector machine (SVM) is a pattern classification model suitable for classification and annotation of images using non-vectorial type representations of images. Varying length patterns extracted from image data correspond to sets of local feature vectors. Kernels designed for varying length patterns are called as dynamic kernels. The talk presents the issues in designing the dynamic kernel based SVMS for classification and annotation of images. Different methods for designing the dynamic kernels are presented. An intermediate matching kernel (IMK) for a pair of varying length patterns is constructed by matching the pairs of local feature vectors selected using a set of virtual feature vectors. For patterns corresponding to sets of feature vectors, a Gaussian mixture model (GMM) is used as the set of virtual feature vectors. The GMM-based IMK is considered for image processing tasks such as image classification, matching and annotation in content-based image retrieval. The talk presents results of experimental studies on image classification, annotation and retrieval of images using the kernel methods.

关键词： Cloud computing Kernel Technological innovation Support vector machine classification Learning systems Hidden Markov models Computational modeling

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Interconnectedness of Complex systems of Internet of Things through Social Network Analysis for Disaster Management

Interconnectedness of Complex Systems of Internet of Things ...

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International Conference on Intelligent Networking and Collaborative systems (INCOS)

作者： Asta Zelenkauskaite Nik Bessis Stelios Sotiriadis Eleana Asimakopoulou Department of Telecommunications Indiana University Bloomington USA Distributed and Intelligent Systems Research Group University of Derby Derby UK Department of Computer Science and Technology University of Bedfordshire Luton UK

This visionary paper presents the Internet of Things paradigm in terms of interdependent dynamic dimensions of objects and their properties. Given that in its current state Internet of Things (IoT) has been viewed as a paradigm based on hierarchical distribution of objects, evaluation of the dynamic nature of the hierarchical structures faces challenges in its evaluation and analysis. Within this in mind, our focus is on the area of complex social networks and the dynamic social network construction within the context of IoT. This is by highlighting and addressing the tagging issues of the objects to the real-world domain such as in disaster management, these are in relation to their hierarchies and interrelation within the context of social network analysis. Specifically, we suggest to investigate and deepen the understanding of the IoT paradigm through the application of social network analysis as a method for interlinking objects -- and thus, propose ways in which IoT could be subsequently interlinked and analyzed through social network analysis approach - which provides possibilities for linking of the objects, while extends it into real-world domain. With this in mind, we present few applications and key characteristics of disaster management and the social networking analysis approach, as well as, foreseen benefits of its application in the IoT domain.

关键词： Social network services Context Sensors Internet Disaster management Wireless sensor networks

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Disaster Management and Profile Modelling of IoT Objects: Conceptual Parameters for Interlinked Objects in Relation to Social Network Analysis

Disaster Management and Profile Modelling of IoT Objects: Co...

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International Conference on Intelligent Networking and Collaborative systems (INCOS)

In recent years, the emergence of ubiquitous and pervasive computing suggests the radical transformation of the Internet to incorporate physical objects. The transformation suggests the enabling of a new form of communication and interaction style that incorporates people, their smart devices and their physical objects through the utilization of distributed sensors spread in the environment. In this study, we propose extending Internet of Things (IoT) by modelling an IoT enabled smart environment as a whole, representing the dynamic communication and interaction among all objects (users, devices, physical objects and sensors). This is by recognizing and categorizing objects' properties in the form of a generic profile. We also reflect this in a disaster management context. That is by identifying which of the parameters of these smart objects are fixed, constant and persistent over time and which parameters are actually change over time, i.e. those characterized by their transient and dynamic nature. Thus, through the process of communication and interaction of the objects, we analyze parameters by demonstrating their static and/or dynamic properties as well as those supporting context-aware variables which are evident in disaster scenarios. To achieve these goals, we designed the persistent or temporal relationships to encompass internal information of smart-objects, along with their characteristics that actually depict their capacity to offer services to users by properties' matchmaking. The interlinked relationships represent a 'social network' providing a terrain of flexible scenarios that would lead to tailored parameters to fit user preferences. To enable communication among them in a dynamic dimension we utilized a distributed topology in which communication could occur indirectly between objects. Finally, we detailed a generic - but equally applicable for disaster management - case scenario in which we used graph theory to demonstrate how embedded intelligenc

关键词： Social network services Context Organizations Disaster management Sensors Object recognition Internet

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Handling missing values in data mining - A case study of heart failure dataset

Handling missing values in data mining - A case study of hea...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： N. Poolsawad L. Moore C. Kambhampati J. G. F. Cleland Distributed Reliable Intelligent Systems Research Group (DRIS) Department of Computer Science University of Hull Hull UK Hull York Medical School Department of Cardiology University of Hull Hull UK

In this paper, we investigate the characteristics of a clinical dataset using feature selection and classification techniques to deal with missing values and develop a method to quantify numerous complexities. The research aims to find features that have high effect on mortality time frame, and to design methodologies which will cope with the following challenges: missing values, high dimensionality, and the prediction problem. The experimental results will be extended to develop prediction model for HF This paper also provides a comprehensive evaluation of a set of diverse machine learning schemes for clinical datasets.

关键词： Data mining Classification algorithms Predictive models Heart Artificial neural networks Accuracy Prediction algorithms

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