The Experts below are selected from a list of 19752 Experts worldwide ranked by ideXlab platform
Mihaela Van Der Schaar - One of the best experts on this subject based on the ideXlab platform.
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efficient resource provisioning and rate selection for stream mining in a Community Cloud
IEEE Transactions on Multimedia, 2013Co-Authors: Mihaela Van Der SchaarAbstract:Real-time stream mining such as surveillance and personal health monitoring, which involves sophisticated mathematical operations, is computation-intensive and prohibitive for mobile devices due to the hardware/computation constraints. To satisfy the growing demand for stream mining in mobile networks, we propose to employ a Cloud-based stream mining system in which the mobile devices send via wireless links unclassified media streams to the Cloud for classification. We aim at minimizing the classification-energy cost, defined as an affine combination of classification cost and energy consumption at the Cloud, subject to an average stream mining delay constraint (which is important in real-time applications). To address the challenge of time-varying wireless channel conditions without a priori information about the channel statistics, we develop an online algorithm in which the Cloud operator can dynamically adjust its resource provisioning on the fly and the mobile devices can adapt their transmission rates to the instantaneous channel conditions. It is proved that, at the expense of increasing the average stream mining delay, the online algorithm achieves a classification-energy cost that can be pushed arbitrarily close to the minimum cost achieved by the optimal offline algorithm. Extensive simulations are conducted to validate the analysis.
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Energy-efficient Community Cloud for real-time stream mining
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Mihaela Van Der SchaarAbstract:Real-time stream mining such as surveillance and personal health monitoring is computation-intensive and prohibitive for mobile devices due to the hardware/computation constraints. To satisfy the growing demand for stream mining in mobile networks, we propose to employ a Cloud-based stream mining system in which the mobile devices send via wireless links unclassified media streams to the Cloud for classification. We focus on minimizing the classification-energy cost, defined as an affine combination of classification cost and energy consumption at the Cloud, subject to an average stream mining delay constraint (which is important in real-time applications). To address the challenge of time-varying wireless channel conditions without a priori information about the channel statistics, we develop an online algorithm in which the Cloud operator can adjust its resource provisioning on the fly and the mobile devices can adapt their transmission rates to the instantaneous channel conditions. It is proved that, at the expense of increasing the average stream mining delay, the online algorithm achieves a classification-energy cost that can be pushed arbitrarily close to the minimum cost achieved by the optimal offline algorithm. Extensive simulations are conducted to validate the analysis.
Matthew Christensen - One of the best experts on this subject based on the ideXlab platform.
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The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach
Atmospheric Measurement Techniques, 2017Co-Authors: Gregory R. Mcgarragh, Adam C. Povey, Oliver Sus, Stefan Stapelberg, Cornelia Schlundt, Simon Proud, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Martin StengelAbstract:Abstract. The Community Cloud retrieval for Climate (CC4CL) is a Cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of Cloud optical thickness, effective radius and Cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model, which includes the clear-sky model, the liquid water and ice Cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and the "fast" radiative transfer solution (which includes a multiple scattering treatment). All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for Cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for Cloud optical thicknesses greater than 10. Results for Clouds of optical thicknesses less than 10 have errors up to 20 %.
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The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors
2017Co-Authors: Oliver Sus, Adam C. Povey, Stefan Stapelberg, Cornelia Schlundt, Martin Stengel, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Gregory Mcgarragh, Simon ProudAbstract:Abstract. We present the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time-series provided at various resolutions, from 0.5° to 0.02°. By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework, and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high-latitudes and over the Gulf of Guinea/West Africa. The results show that CC4CL provides very realistic estimates of Cloud top height and cover for optically thick Clouds but, where optically thin Clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument Cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one data set, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.
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The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors
Atmospheric Measurement Techniques, 2017Co-Authors: Oliver Sus, Gregory R. Mcgarragh, Adam C. Povey, Stefan Stapelberg, Cornelia Schlundt, Martin Stengel, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Simon ProudAbstract:Abstract. We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02 ∘ . By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea–West Africa. The results show that CC4CL provides very realistic estimates of Cloud top height and cover for optically thick Clouds but, where optically thin Clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument Cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.
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the Community Cloud retrieval for climate cc4cl part 1 a framework applied to multiple satellite imaging sensors
Atmospheric Measurement Techniques, 2017Co-Authors: Oliver Sus, Gregory R. Mcgarragh, Adam C. Povey, Stefan Stapelberg, Cornelia Schlundt, Martin Stengel, Gareth Thomas, Matthew Christensen, C PoulsenAbstract:Abstract. We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02 ∘ . By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea–West Africa. The results show that CC4CL provides very realistic estimates of Cloud top height and cover for optically thick Clouds but, where optically thin Clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument Cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.
Felix Freitag - One of the best experts on this subject based on the ideXlab platform.
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Cloudy in guifi.net: Establishing and sustaining a Community Cloud as open commons
Future Generation Computer Systems, 2018Co-Authors: Roger Baig, Felix Freitag, Leandro NavarroAbstract:Abstract Commons are natural or human-made resources that are managed cooperatively. The guifi.net Community network is a successful example of a digital infrastructure, a computer network, managed as an open commons. Inspired by the guifi.net case and its commons governance model, we claim that a computing Cloud, another digital infrastructure, can also be managed as an open commons if the appropriate tools are put in place. In this paper, we explore the feasibility and sustainability of Community Clouds as open commons: open user-driven Clouds formed by Community-managed computing resources. We propose organising the infrastructure as a service (IaaS) and platform as a service (PaaS) Cloud service layers as common-pool resources (CPR) for enabling a sustainable Cloud service provision. On this basis, we have outlined a governance framework for Community Clouds, and we have developed Cloudy, a Cloud software stack that comprises a set of tools and components to build and operate Community Cloud services. Cloudy is tailored to the needs of the guifi.net Community network, but it can be adopted by other communities. We have validated the feasibility of Community Clouds in a deployment in guifi.net of some 60 devices running Cloudy for over two years. To gain insight into the capacity of end-user services to generate enough value and utility to sustain the whole Cloud ecosystem, we have developed a file storage application and tested it with a group of 10 guifi.net users. The experimental results and the experience from the action research confirm the feasibility and potential sustainability of the Community Cloud as an open commons.
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MECC@Middleware - Towards decentralised resilient Community Clouds
Proceedings of the 2nd Workshop on Middleware for Edge Clouds & Cloudlets - MECC '17, 2017Co-Authors: Arjuna Sathiaseelan, Leandro Navarro, Felix Freitag, Mennan Selimi, Carlos Molina, Adisorn Lertsinsrubtavee, Fernando M. V. Ramos, Roger BaigAbstract:Recent years have seen a trend towards decentralisation - from initiatives on decentralized web to decentralized network infrastructures. In this position paper, we present an architectural vision for decentralising Cloud service infrastructures. Our vision is on Community Cloud infrastructures on top of decentralised access infrastructures i.e. Community networks, using resources pooled from the Community. Our architectural vision considers some fundamental challenges of integrating the current state of the art virtualisation technologies such as Software Defined Networking (SDN) into Community infrastructures which are highly unreliable. Our proposed design goal is to include lightweight network and processing virtualization with fault tolerance mechanisms to ensure sufficient level of reliability to support local services.
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Towards Decentralised Resilient Community Cloud Infrastructures
arXiv: Distributed Parallel and Cluster Computing, 2017Co-Authors: Arjuna Sathiaseelan, Leandro Navarro, Felix Freitag, Mennan Selimi, Carlos Molina, Adisorn Lertsinsrubtavee, Fernando M. V. Ramos, Roger BaigAbstract:Recent years have seen a trend towards decentralisation - from initiatives on decentralized web to decentralized network infrastructures (e.g Community networks). In this position paper, we present an architectural vision for decentralising Cloud service infrastructures. Our vision is on the notion of Community Cloud infrastructures on top of decentralised access infrastructures i.e. Community networks, using resources pooled from the Community. Our architectural vision takes into consideration some of the fundamental challenges of integrating the current state of the art virtualisation technologies such as Software Defined Networking (SDN) into Community infrastructures which are highly unreliable. Our proposed design goal is to include lightweight virtualization and fault tolerance mechanisms into the architecture to ensure sufficient level of reliability to support critical applications.
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GECON - On the Sustainability of Community Clouds in guifi.net
Economics of Grids Clouds Systems and Services, 2016Co-Authors: Roger Baig, Felix Freitag, Leandro NavarroAbstract:The Internet and Cloud services are key enablers for participation in society. The need for Internet access in areas underserved by commercial telecom operators has often been a motivation to develop Community networks. Many examples around the world show successful cooperative developments of open, participatory local networking infrastructures. Such collaborative models have not yet been applied to local Cloud computing resources and services. In this paper, we elaborate on the sustainability model of the http://guifi.net Community network as a basis for Cloud-based infrastructures and services in communities. We first look at the elements of http://guifi.net, which support the sustainability and growth of the networking infrastructure. We then discuss their application to Cloud-based services within the network and come up with a framework of tools and components for Community Cloud resources and services. Finally, we assess the current status of the experimental Community Cloud in http://guifi.net, where some of the proposed tools are already operational.
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ICNC - Cloud-based Community services in Community networks
2016 International Conference on Computing Networking and Communications (ICNC), 2016Co-Authors: Roger Baig, Roger Pueyo, Leandro Navarro, Felix Freitag, Agustí Moll, Vladimir VlassovAbstract:Wireless networks have shown to be a cost effective solution for an IP-based communication infrastructure in under-served areas. Services and application, if deployed within these wireless networks, add value for the users. This paper shows how Cloud infrastructures have been made operational in a Community wireless network, as a particular case of a Community Cloud, developed according to the specific requirements and conditions of the Community. We describe the conditions and requirements of such a Community Cloud and explain our technical choices and experience in its deployment in the Community network. The user take-up has started, and our case supports the tendency of Cloud computing moving towards the network edge.
Mihaela Van Der Schaar - One of the best experts on this subject based on the ideXlab platform.
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CDC - Energy-efficient Community Cloud for real-time stream mining
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Shaolei Ren, Mihaela Van Der SchaarAbstract:Real-time stream mining such as surveillance and personal health monitoring is computation-intensive and prohibitive for mobile devices due to the hardware/computation constraints. To satisfy the growing demand for stream mining in mobile networks, we propose to employ a Cloud-based stream mining system in which the mobile devices send via wireless links unclassified media streams to the Cloud for classification. We focus on minimizing the classification-energy cost, defined as an affine combination of classification cost and energy consumption at the Cloud, subject to an average stream mining delay constraint (which is important in real-time applications). To address the challenge of time-varying wireless channel conditions without a priori information about the channel statistics, we develop an online algorithm in which the Cloud operator can adjust its resource provisioning on the fly and the mobile devices can adapt their transmission rates to the instantaneous channel conditions. It is proved that, at the expense of increasing the average stream mining delay, the online algorithm achieves a classification-energy cost that can be pushed arbitrarily close to the minimum cost achieved by the optimal offline algorithm. Extensive simulations are conducted to validate the analysis.
Oliver Sus - One of the best experts on this subject based on the ideXlab platform.
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The Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approach
2017Co-Authors: Gregory R. Mcgarragh, Caroline A. Poulsen, Gareth E. Thomas, Adam C. Povey, Oliver Sus, Stefan Stapelberg, Cornelia Schlundt, Simon Proud, Matthew W. Christensen, Martin StengelAbstract:Abstract. The Community Cloud retrieval for Climate (CC4CL) is a Cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of Cloud optical thickness, effective radius and Cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model which, includes the clear-sky model, the liquid water and ice Cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), the "fast" radiative transfer solution (which includes a multiple scattering treatment) All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for Cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modelling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the non-linear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for Cloud optical thicknesses greater than 10. Results for Clouds of optical thicknesses less than 10 have errors ranging up to 20 %.
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The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach
Atmospheric Measurement Techniques, 2017Co-Authors: Gregory R. Mcgarragh, Adam C. Povey, Oliver Sus, Stefan Stapelberg, Cornelia Schlundt, Simon Proud, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Martin StengelAbstract:Abstract. The Community Cloud retrieval for Climate (CC4CL) is a Cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of Cloud optical thickness, effective radius and Cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model, which includes the clear-sky model, the liquid water and ice Cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and the "fast" radiative transfer solution (which includes a multiple scattering treatment). All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for Cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for Cloud optical thicknesses greater than 10. Results for Clouds of optical thicknesses less than 10 have errors up to 20 %.
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The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors
2017Co-Authors: Oliver Sus, Adam C. Povey, Stefan Stapelberg, Cornelia Schlundt, Martin Stengel, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Gregory Mcgarragh, Simon ProudAbstract:Abstract. We present the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time-series provided at various resolutions, from 0.5° to 0.02°. By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework, and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high-latitudes and over the Gulf of Guinea/West Africa. The results show that CC4CL provides very realistic estimates of Cloud top height and cover for optically thick Clouds but, where optically thin Clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument Cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one data set, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.
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The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors
Atmospheric Measurement Techniques, 2017Co-Authors: Oliver Sus, Gregory R. Mcgarragh, Adam C. Povey, Stefan Stapelberg, Cornelia Schlundt, Martin Stengel, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Simon ProudAbstract:Abstract. We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02 ∘ . By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea–West Africa. The results show that CC4CL provides very realistic estimates of Cloud top height and cover for optically thick Clouds but, where optically thin Clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument Cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.
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the Community Cloud retrieval for climate cc4cl part 1 a framework applied to multiple satellite imaging sensors
Atmospheric Measurement Techniques, 2017Co-Authors: Oliver Sus, Gregory R. Mcgarragh, Adam C. Povey, Stefan Stapelberg, Cornelia Schlundt, Martin Stengel, Gareth Thomas, Matthew Christensen, C PoulsenAbstract:Abstract. We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02 ∘ . By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea–West Africa. The results show that CC4CL provides very realistic estimates of Cloud top height and cover for optically thick Clouds but, where optically thin Clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument Cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.