2,315 research outputs found

    The countryside in urbanized Flanders: towards a flexible definition for a dynamic policy

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    The countryside, the rural area, the open space, … many definitions are used for rural Flanders. Everyone makes its own interpretation of the countryside, considering it as a place for living, working or recreating. The countryside is more than just a geographical area: it is an aggregate of physical, social, economic and cultural functions, strongly interrelated with each other. According to international and European definitions of rural areas there would be almost no rural area in Flanders. These international definitions are all developed to be used for analysis and policy within their specific context. They are not really applicable to Flanders because of the historical specificity of its spatial structure. Flanders is characterized by a giant urbanization pressure on its countryside while internationally rural depopulation is a point of interest. To date, for every single rural policy initiative – like the implementation of the European Rural Development Policy – Flanders used a specifically adapted definition, based on existing data or previously made delineations. To overcome this oversupply of definitions and delineations, the Flemish government funded a research project to obtain a clear and flexible definition of the Flemish countryside and a dynamic method to support Flemish rural policy aims. First, an analysis of the currently used definitions of the countryside in Flanders was made. It is clear that, depending on the perspective or the policy context, another definition of the countryside comes into view. The comparative study showed that, according to the used criteria, the area percentage of Flanders that is rural, varies between 9 and 93 per cent. Second, dynamic sets of criteria were developed, facilitating a flexible definition of the countryside, according to the policy aims concerned. This research part was focused on 6 policy themes, like ‘construction, maintenance and management of local (transport) infrastructures’ and ‘provision of (minimum) services (education, culture, health care, …)’. For each theme a dynamic set of criteria or indicators was constructed. These indicators make it possible to show where a policy theme manifests itself and/or where policy interventions are possible or needed. In this way every set of criteria makes up a new definition of rural Flanders. This method is dynamic; new data or insights can easily be incorporated and new criteria sets can be developed if other policy aims come into view. The developed method can contribute to a more region-oriented and theme-specific rural policy and funding mechanism

    Generating Reality with Geosimulation Models: An Agent-Based Social-Spatial Network Modelling Perspective

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    Models in general and geosimulation in particular are epistemologically characterized by two principles: first, they produce reality through their existence and communication rather than simply representing it. Second, they reduce complexity in the process of mapping complexity. Since almost any current phenomenon is understood as complex without specifying how complex it is and in which sense, geosimulation models are important tools in solving this problem of specifying and representing complexity. This capability rests, among other things, upon its multilevel approach (bottom-up and top-down) and its ability to translate terms into numbers and thus into distinct singularities. A demonstration of such an understanding of models will be given by presenting a socio-spatial simulation approach in the domain of network analysis and social capital operationalization. Two Austrian regions serve as case studies using empirical and simulated data. The demonstration includes the endeavor to intertwine a place-based geography with a network-based geography

    Enhancement of Metaheuristic Algorithm for Scheduling Workflows in Multi-fog Environments

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    Whether in computer science, engineering, or economics, optimization lies at the heart of any challenge involving decision-making. Choosing between several options is part of the decision- making process. Our desire to make the "better" decision drives our decision. An objective function or performance index describes the assessment of the alternative's goodness. The theory and methods of optimization are concerned with picking the best option. There are two types of optimization methods: deterministic and stochastic. The first is a traditional approach, which works well for small and linear problems. However, they struggle to address most of the real-world problems, which have a highly dimensional, nonlinear, and complex nature. As an alternative, stochastic optimization algorithms are specifically designed to tackle these types of challenges and are more common nowadays. This study proposed two stochastic, robust swarm-based metaheuristic optimization methods. They are both hybrid algorithms, which are formulated by combining Particle Swarm Optimization and Salp Swarm Optimization algorithms. Further, these algorithms are then applied to an important and thought-provoking problem. The problem is scientific workflow scheduling in multiple fog environments. Many computer environments, such as fog computing, are plagued by security attacks that must be handled. DDoS attacks are effectively harmful to fog computing environments as they occupy the fog's resources and make them busy. Thus, the fog environments would generally have fewer resources available during these types of attacks, and then the scheduling of submitted Internet of Things (IoT) workflows would be affected. Nevertheless, the current systems disregard the impact of DDoS attacks occurring in their scheduling process, causing the amount of workflows that miss deadlines as well as increasing the amount of tasks that are offloaded to the cloud. Hence, this study proposed a hybrid optimization algorithm as a solution for dealing with the workflow scheduling issue in various fog computing locations. The proposed algorithm comprises Salp Swarm Algorithm (SSA) and Particle Swarm Optimization (PSO). In dealing with the effects of DDoS attacks on fog computing locations, two Markov-chain schemes of discrete time types were used, whereby one calculates the average network bandwidth existing in each fog while the other determines the number of virtual machines existing in every fog on average. DDoS attacks are addressed at various levels. The approach predicts the DDoS attack’s influences on fog environments. Based on the simulation results, the proposed method can significantly lessen the amount of offloaded tasks that are transferred to the cloud data centers. It could also decrease the amount of workflows with missed deadlines. Moreover, the significance of green fog computing is growing in fog computing environments, in which the consumption of energy plays an essential role in determining maintenance expenses and carbon dioxide emissions. The implementation of efficient scheduling methods has the potential to mitigate the usage of energy by allocating tasks to the most appropriate resources, considering the energy efficiency of each individual resource. In order to mitigate these challenges, the proposed algorithm integrates the Dynamic Voltage and Frequency Scaling (DVFS) technique, which is commonly employed to enhance the energy efficiency of processors. The experimental findings demonstrate that the utilization of the proposed method, combined with the Dynamic Voltage and Frequency Scaling (DVFS) technique, yields improved outcomes. These benefits encompass a minimization in energy consumption. Consequently, this approach emerges as a more environmentally friendly and sustainable solution for fog computing environments

    Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

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    This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

    Dynamic Scaling for Service Oriented Applications: Implications of Virtual Machine Placement on IaaS Clouds

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    Abstraction of physical hardware using infrastructure-as-a-service (IaaS) clouds leads to the simplistic view that resources are homogeneous and that infinite scaling is possible with linear increases in performance. Support for autonomic scaling of multi-tier service oriented applications requires determination of when, what, and where to scale. \u27When\u27 is addressed by hotspot detection schemes using techniques including performance modeling and time series analysis. \u27What\u27 relates to determining the quantity and size of new resources to provision. \u27Where\u27 involves identification of the best location(s) to provision new resources. In this paper we investigate primarily \u27where\u27 new infrastructure should be provisioned, and secondly \u27what\u27 the infrastructure should be. Dynamic scaling of infrastructure for service oriented applications requires rapid response to changes in demand to meet application quality-of-service requirements. We investigate the performance and resource cost implications of VM placement when dynamically scaling server infrastructure of service oriented applications . We evaluate dynamic scaling in the context of providing modeling-as-a-service for two environmental science models

    SPARC 2016 Salford postgraduate annual research conference book of abstracts

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    Autonomous management of cost, performance, and resource uncertainty for migration of applications to infrastructure-as-a-service (IaaS) clouds

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    2014 Fall.Includes bibliographical references.Infrastructure-as-a-Service (IaaS) clouds abstract physical hardware to provide computing resources on demand as a software service. This abstraction leads to the simplistic view that computing resources are homogeneous and infinite scaling potential exists to easily resolve all performance challenges. Adoption of cloud computing, in practice however, presents many resource management challenges forcing practitioners to balance cost and performance tradeoffs to successfully migrate applications. These challenges can be broken down into three primary concerns that involve determining what, where, and when infrastructure should be provisioned. In this dissertation we address these challenges including: (1) performance variance from resource heterogeneity, virtualization overhead, and the plethora of vaguely defined resource types; (2) virtual machine (VM) placement, component composition, service isolation, provisioning variation, and resource contention for multitenancy; and (3) dynamic scaling and resource elasticity to alleviate performance bottlenecks. These resource management challenges are addressed through the development and evaluation of autonomous algorithms and methodologies that result in demonstrably better performance and lower monetary costs for application deployments to both public and private IaaS clouds. This dissertation makes three primary contributions to advance cloud infrastructure management for application hosting. First, it includes design of resource utilization models based on step-wise multiple linear regression and artificial neural networks that support prediction of better performing component compositions. The total number of possible compositions is governed by Bell's Number that results in a combinatorially explosive search space. Second, it includes algorithms to improve VM placements to mitigate resource heterogeneity and contention using a load-aware VM placement scheduler, and autonomous detection of under-performing VMs to spur replacement. Third, it describes a workload cost prediction methodology that harnesses regression models and heuristics to support determination of infrastructure alternatives that reduce hosting costs. Our methodology achieves infrastructure predictions with an average mean absolute error of only 0.3125 VMs for multiple workloads

    Air Force Institute of Technology Research Report 2006

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    This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics
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