The Experts below are selected from a list of 5403 Experts worldwide ranked by ideXlab platform
Gabriel-miro Muntean - One of the best experts on this subject based on the ideXlab platform.
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Device characteristics-based differentiated Energy-efficient Adaptive Solution for video delivery over heterogeneous wireless networks
2013 IEEE Wireless Communications and Networking Conference (WCNC), 2013Co-Authors: Ruiqi Ding, Gabriel-miro MunteanAbstract:The limited battery capacity of current mobile devices and increasing amount of rich media content delivered over wireless networks have driven the latest research on energy efficient content delivery over wireless networks. Many energy-aware research Solutions have been proposed involving traffic shaping, content adaptation, content sharing, etc. The existing Solutions focus on the delivery application without considering application running environment and device features that pose different energy constraints on the whole content delivery process. This paper presents a Device characteristics-based differentiated Energy-efficient Adaptive Solution (DEAS) for video delivery over heterogeneous wireless networks. DEAS constructs an energy-oriented system profile including power signatures of various device components for each running application. Based on this profile, an energy efficient content delivery adaptation is performed for the current application. The proposed Solution is evaluated by simulation-based testing and compared with other state of the art approaches in terms of performance and energy efficiency. The results show how DEAS outperforms the other well-known Solutions.
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WCNC - Device characteristics-based differentiated Energy-efficient Adaptive Solution for video delivery over heterogeneous wireless networks
2013 IEEE Wireless Communications and Networking Conference (WCNC), 2013Co-Authors: Ruiqi Ding, Gabriel-miro MunteanAbstract:The limited battery capacity of current mobile devices and increasing amount of rich media content delivered over wireless networks have driven the latest research on energy efficient content delivery over wireless networks. Many energy-aware research Solutions have been proposed involving traffic shaping, content adaptation, content sharing, etc. The existing Solutions focus on the delivery application without considering application running environment and device features that pose different energy constraints on the whole content delivery process. This paper presents a Device characteristics-based differentiated Energy-efficient Adaptive Solution (DEAS) for video delivery over heterogeneous wireless networks. DEAS constructs an energy-oriented system profile including power signatures of various device components for each running application. Based on this profile, an energy efficient content delivery adaptation is performed for the current application. The proposed Solution is evaluated by simulation-based testing and compared with other state of the art approaches in terms of performance and energy efficiency. The results show how DEAS outperforms the other well-known Solutions.
Ruiqi Ding - One of the best experts on this subject based on the ideXlab platform.
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Device characteristics-based differentiated energy-efficient Adaptive Solution for multimedia delivery over heterogeneous wireless networks
2014Co-Authors: Ruiqi DingAbstract:Energy efficiency is a key issue of highest importance to mobile wireless device users, as those devices are powered by batteries with limited power capacity. It is of very high interest to provide device differentiated user centric energy efficient multimedia content delivery based on current application type, energy-oriented device features and user preferences. This thesis presents the following research contributions in the area of energy efficient multimedia delivery over heterogeneous wireless networks: 1. ASP: Energy-oriented Application-based System profiling for mobile devices: This profiling provides services to other contributions in this thesis. By monitoring the running applications and the corresponding power demand on the smart mobile device, a device energy model is obtained. The model is used in conjunction with applications’ power signature to provide device energy constraints posed by running applications. 2. AWERA 3. DEAS: A Device characteristics-based differentiated Energy-efficient Adaptive Solution for video delivery over heterogeneous wireless networks. Based on the energy constraint, DEAS performs energy efficient content delivery adaptation for the current application. Unlike the existing Solutions, DEAS takes all the applications running on the system into account and better balances QoS and energy efficiency. 4. EDCAM 5. A comprehensive survey on state-of-the-art energy-efficient network protocols and energy-saving network technologies.
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Device characteristics-based differentiated Energy-efficient Adaptive Solution for video delivery over heterogeneous wireless networks
2013 IEEE Wireless Communications and Networking Conference (WCNC), 2013Co-Authors: Ruiqi Ding, Gabriel-miro MunteanAbstract:The limited battery capacity of current mobile devices and increasing amount of rich media content delivered over wireless networks have driven the latest research on energy efficient content delivery over wireless networks. Many energy-aware research Solutions have been proposed involving traffic shaping, content adaptation, content sharing, etc. The existing Solutions focus on the delivery application without considering application running environment and device features that pose different energy constraints on the whole content delivery process. This paper presents a Device characteristics-based differentiated Energy-efficient Adaptive Solution (DEAS) for video delivery over heterogeneous wireless networks. DEAS constructs an energy-oriented system profile including power signatures of various device components for each running application. Based on this profile, an energy efficient content delivery adaptation is performed for the current application. The proposed Solution is evaluated by simulation-based testing and compared with other state of the art approaches in terms of performance and energy efficiency. The results show how DEAS outperforms the other well-known Solutions.
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WCNC - Device characteristics-based differentiated Energy-efficient Adaptive Solution for video delivery over heterogeneous wireless networks
2013 IEEE Wireless Communications and Networking Conference (WCNC), 2013Co-Authors: Ruiqi Ding, Gabriel-miro MunteanAbstract:The limited battery capacity of current mobile devices and increasing amount of rich media content delivered over wireless networks have driven the latest research on energy efficient content delivery over wireless networks. Many energy-aware research Solutions have been proposed involving traffic shaping, content adaptation, content sharing, etc. The existing Solutions focus on the delivery application without considering application running environment and device features that pose different energy constraints on the whole content delivery process. This paper presents a Device characteristics-based differentiated Energy-efficient Adaptive Solution (DEAS) for video delivery over heterogeneous wireless networks. DEAS constructs an energy-oriented system profile including power signatures of various device components for each running application. Based on this profile, an energy efficient content delivery adaptation is performed for the current application. The proposed Solution is evaluated by simulation-based testing and compared with other state of the art approaches in terms of performance and energy efficiency. The results show how DEAS outperforms the other well-known Solutions.
Michela Becchi - One of the best experts on this subject based on the ideXlab platform.
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Deploying Graph Algorithms on GPUs: An Adaptive Solution
2013 IEEE 27th International Symposium on Parallel and Distributed Processing, 2013Co-Authors: Da Li, Michela BecchiAbstract:Thanks to their massive computational power and their SIMT computational model, Graphics Processing Units (GPUs) have been successfully used to accelerate a wide variety of regular applications (linear algebra, stencil computations, image processing and bioinformatics algorithms, among others). However, many established and emerging problems are based on irregular data structures, such as graphs. Examples can be drawn from different application domains: networking, social networking, machine learning, electrical circuit modeling, discrete event simulation, compilers, and computational sciences. It has been shown that irregular applications based on large graphs do exhibit runtime parallelism; moreover, the amount of available parallelism tends to increase with the size of the datasets. In this work, we explore an implementation space for deploying a variety of graph algorithms on GPUs. We show that the dynamic nature of the parallelism that can be extracted from graph algorithms makes it impossible to find an optimal Solution. We propose a runtime system able to dynamically transition between different implementations with minimal overhead, and investigate heuristic decisions applicable across algorithms and datasets. Our evaluation is performed on two graph algorithms: breadth-first search and single-source shortest paths. We believe that our proposed mechanisms can be extended and applied to other graph algorithms that exhibit similar computational patterns.
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IPDPS - Deploying Graph Algorithms on GPUs: An Adaptive Solution
2013 IEEE 27th International Symposium on Parallel and Distributed Processing, 2013Co-Authors: Da Li, Michela BecchiAbstract:Thanks to their massive computational power and their SIMT computational model, Graphics Processing Units (GPUs) have been successfully used to accelerate a wide variety of regular applications (linear algebra, stencil computations, image processing and bioinformatics algorithms, among others). However, many established and emerging problems are based on irregular data structures, such as graphs. Examples can be drawn from different application domains: networking, social networking, machine learning, electrical circuit modeling, discrete event simulation, compilers, and computational sciences. It has been shown that irregular applications based on large graphs do exhibit runtime parallelism; moreover, the amount of available parallelism tends to increase with the size of the datasets. In this work, we explore an implementation space for deploying a variety of graph algorithms on GPUs. We show that the dynamic nature of the parallelism that can be extracted from graph algorithms makes it impossible to find an optimal Solution. We propose a runtime system able to dynamically transition between different implementations with minimal overhead, and investigate heuristic decisions applicable across algorithms and datasets. Our evaluation is performed on two graph algorithms: breadth-first search and single-source shortest paths. We believe that our proposed mechanisms can be extended and applied to other graph algorithms that exhibit similar computational patterns.
Xiang Zhou - One of the best experts on this subject based on the ideXlab platform.
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An Adaptive Solution for Large-Scale, Cross-Video and Real-Time Visual Analytics
2015 IEEE International Conference on Multimedia Big Data, 2015Co-Authors: Xiao Hu, Zhihong Yu, Huan Zhou, Hongbo Lv, Zhipeng Jiang, Xiang ZhouAbstract:This paper aims at a new challenge caused by a specific type of real-life problems that require to not only process tremendous videos, dig out cross-video information, but also guarantee a real-time responsiveness to the user. Moreover, users want to adapt the resources to the actual amount of visual objects, rather than to the number of videos, in order to better match resource consumption to the true business needs. All the state-of-the-art Solutions cannot meet these requirements altogether, while this paper developed a series of techniques to address each specific problem, and then proposed a new Adaptive Solution for large-scale, cross-video, and real-time visual analytics.
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BigMM - An Adaptive Solution for Large-Scale, Cross-Video and Real-Time Visual Analytics
2015 IEEE International Conference on Multimedia Big Data, 2015Co-Authors: Xiao Hu, Zhihong Yu, Huan Zhou, Hongbo Lv, Zhipeng Jiang, Xiang ZhouAbstract:This paper aims at a new challenge caused by a specific type of real-life problems that require to not only process tremendous videos, dig out cross-video information, but also guarantee a real-time responsiveness to the user. Moreover, users want to adapt the resources to the actual amount of visual objects, rather than to the number of videos, in order to better match resource consumption to the true business needs. All the state-of-the-art Solutions cannot meet these requirements altogether, while this paper developed a series of techniques to address each specific problem, and then proposed a new Adaptive Solution for large-scale, cross-video, and real-time visual analytics.
Da Li - One of the best experts on this subject based on the ideXlab platform.
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Deploying Graph Algorithms on GPUs: An Adaptive Solution
2013 IEEE 27th International Symposium on Parallel and Distributed Processing, 2013Co-Authors: Da Li, Michela BecchiAbstract:Thanks to their massive computational power and their SIMT computational model, Graphics Processing Units (GPUs) have been successfully used to accelerate a wide variety of regular applications (linear algebra, stencil computations, image processing and bioinformatics algorithms, among others). However, many established and emerging problems are based on irregular data structures, such as graphs. Examples can be drawn from different application domains: networking, social networking, machine learning, electrical circuit modeling, discrete event simulation, compilers, and computational sciences. It has been shown that irregular applications based on large graphs do exhibit runtime parallelism; moreover, the amount of available parallelism tends to increase with the size of the datasets. In this work, we explore an implementation space for deploying a variety of graph algorithms on GPUs. We show that the dynamic nature of the parallelism that can be extracted from graph algorithms makes it impossible to find an optimal Solution. We propose a runtime system able to dynamically transition between different implementations with minimal overhead, and investigate heuristic decisions applicable across algorithms and datasets. Our evaluation is performed on two graph algorithms: breadth-first search and single-source shortest paths. We believe that our proposed mechanisms can be extended and applied to other graph algorithms that exhibit similar computational patterns.
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IPDPS - Deploying Graph Algorithms on GPUs: An Adaptive Solution
2013 IEEE 27th International Symposium on Parallel and Distributed Processing, 2013Co-Authors: Da Li, Michela BecchiAbstract:Thanks to their massive computational power and their SIMT computational model, Graphics Processing Units (GPUs) have been successfully used to accelerate a wide variety of regular applications (linear algebra, stencil computations, image processing and bioinformatics algorithms, among others). However, many established and emerging problems are based on irregular data structures, such as graphs. Examples can be drawn from different application domains: networking, social networking, machine learning, electrical circuit modeling, discrete event simulation, compilers, and computational sciences. It has been shown that irregular applications based on large graphs do exhibit runtime parallelism; moreover, the amount of available parallelism tends to increase with the size of the datasets. In this work, we explore an implementation space for deploying a variety of graph algorithms on GPUs. We show that the dynamic nature of the parallelism that can be extracted from graph algorithms makes it impossible to find an optimal Solution. We propose a runtime system able to dynamically transition between different implementations with minimal overhead, and investigate heuristic decisions applicable across algorithms and datasets. Our evaluation is performed on two graph algorithms: breadth-first search and single-source shortest paths. We believe that our proposed mechanisms can be extended and applied to other graph algorithms that exhibit similar computational patterns.
