21,338 research outputs found
Scalable algorithms for QoS-aware virtual network mapping for cloud services
Both business and consumer applications increasingly depend on cloud solutions. Yet, many are still reluctant to move to cloud-based solutions, mainly due to concerns of service quality and reliability. Since cloud platforms depend both on IT resources (located in data centers, DCs) and network infrastructure connecting to it, both QoS and resilience should be offered with end-to-end guarantees up to and including the server resources. The latter currently is largely impeded by the fact that the network and cloud DC domains are typically operated by disjoint entities. Network virtualization, together with combined control of network and IT resources can solve that problem. Here, we formally state the combined network and IT provisioning problem for a set of virtual networks, incorporating resilience as well as QoS in physical and virtual layers. We provide a scalable column generation model, to address real world network sizes. We analyze the latter in extensive case studies, to answer the question at which layer to provision QoS and resilience in virtual networks for cloud services
Creative Gardens: Towards Digital Commons
date-added: 2015-03-04 03:12:21 +0000 date-modified: 2015-04-01 06:49:53 +0000date-added: 2015-03-04 03:12:21 +0000 date-modified: 2015-04-01 06:49:53 +0000This work was supported by the Arts and Humanities Research Council, CreativeWorks London Hub, grant AH/J005142/1, and the European Regional Development Fund, London Creative and Digital Fusion
Resource provisioning in Science Clouds: Requirements and challenges
Cloud computing has permeated into the information technology industry in the
last few years, and it is emerging nowadays in scientific environments. Science
user communities are demanding a broad range of computing power to satisfy the
needs of high-performance applications, such as local clusters,
high-performance computing systems, and computing grids. Different workloads
are needed from different computational models, and the cloud is already
considered as a promising paradigm. The scheduling and allocation of resources
is always a challenging matter in any form of computation and clouds are not an
exception. Science applications have unique features that differentiate their
workloads, hence, their requirements have to be taken into consideration to be
fulfilled when building a Science Cloud. This paper will discuss what are the
main scheduling and resource allocation challenges for any Infrastructure as a
Service provider supporting scientific applications
Shallow Water Bathymetry Mapping from UAV Imagery based on Machine Learning
The determination of accurate bathymetric information is a key element for
near offshore activities, hydrological studies such as coastal engineering
applications, sedimentary processes, hydrographic surveying as well as
archaeological mapping and biological research. UAV imagery processed with
Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide
a low-cost alternative to established shallow seabed mapping techniques
offering as well the important visual information. Nevertheless, water
refraction poses significant challenges on depth determination. Till now, this
problem has been addressed through customized image-based refraction correction
algorithms or by modifying the collinearity equation. In this paper, in order
to overcome the water refraction errors, we employ machine learning tools that
are able to learn the systematic underestimation of the estimated depths. In
the proposed approach, based on known depth observations from bathymetric LiDAR
surveys, an SVR model was developed able to estimate more accurately the real
depths of point clouds derived from SfM-MVS procedures. Experimental results
over two test sites along with the performed quantitative validation indicated
the high potential of the developed approach.Comment: 8 pages, 9 figure
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