The Experts below are selected from a list of 90 Experts worldwide ranked by ideXlab platform
Qun Li - One of the best experts on this subject based on the ideXlab platform.
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LAVEA: Latency-Aware Video Analytics on Edge Computing Platform
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017Co-Authors: Shanhe Yi, Qingyang Zhang, Quan Zhang, Qun LiAbstract:We present LAVEA, a system built for Edge computing, which offloads computation tasks between clients and Edge nodes, collaborates nearby Edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an Edge-first design to minimize the response time, and compared various task placement schemes tailed for inter-Edge collaboration. Our results reveal that the client-Edge Configuration has task speedup against local or client-cloud Configurations.
Shanhe Yi - One of the best experts on this subject based on the ideXlab platform.
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LAVEA: Latency-Aware Video Analytics on Edge Computing Platform
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017Co-Authors: Shanhe Yi, Qingyang Zhang, Quan Zhang, Qun LiAbstract:We present LAVEA, a system built for Edge computing, which offloads computation tasks between clients and Edge nodes, collaborates nearby Edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an Edge-first design to minimize the response time, and compared various task placement schemes tailed for inter-Edge collaboration. Our results reveal that the client-Edge Configuration has task speedup against local or client-cloud Configurations.
Quan Zhang - One of the best experts on this subject based on the ideXlab platform.
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lavea latency aware video analytics on Edge computing platform
Information Security, 2017Co-Authors: Zijiang Hao, Qingyang Zhang, Quan Zhang, Weisong ShiAbstract:Along the trend pushing computation from the network core to the Edge where the most of data are generated, Edge computing has shown its potential in reducing response time, lowering bandwidth usage, improving energy efficiency and so on. At the same time, low-latency video analytics is becoming more and more important for applications in public safety, counter-terrorism, self-driving cars, VR/AR, etc. As those tasks are either computation intensive or bandwidth hungry, Edge computing fits in well here with its ability to flexibly utilize computation and bandwidth from and between each layer. In this paper, we present LAVEA, a system built on top of an Edge computing platform, which offloads computation between clients and Edge nodes, collaborates nearby Edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an Edge-first design and formulated an optimization problem for offloading task selection and prioritized offloading requests received at the Edge node to minimize the response time. In case of a saturating workload on the front Edge node, we have designed and compared various task placement schemes that are tailed for inter-Edge collaboration. We have implemented and evaluated our system. Our results reveal that the client-Edge Configuration has a speedup ranging from 1.3x to 4x (1.2x to 1.7x) against running in local (client-cloud Configuration). The proposed shortest scheduling latency first scheme outputs the best overall task placement performance for inter-Edge collaboration.
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LAVEA: Latency-Aware Video Analytics on Edge Computing Platform
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017Co-Authors: Shanhe Yi, Qingyang Zhang, Quan Zhang, Qun LiAbstract:We present LAVEA, a system built for Edge computing, which offloads computation tasks between clients and Edge nodes, collaborates nearby Edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an Edge-first design to minimize the response time, and compared various task placement schemes tailed for inter-Edge collaboration. Our results reveal that the client-Edge Configuration has task speedup against local or client-cloud Configurations.
Qingyang Zhang - One of the best experts on this subject based on the ideXlab platform.
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lavea latency aware video analytics on Edge computing platform
Information Security, 2017Co-Authors: Zijiang Hao, Qingyang Zhang, Quan Zhang, Weisong ShiAbstract:Along the trend pushing computation from the network core to the Edge where the most of data are generated, Edge computing has shown its potential in reducing response time, lowering bandwidth usage, improving energy efficiency and so on. At the same time, low-latency video analytics is becoming more and more important for applications in public safety, counter-terrorism, self-driving cars, VR/AR, etc. As those tasks are either computation intensive or bandwidth hungry, Edge computing fits in well here with its ability to flexibly utilize computation and bandwidth from and between each layer. In this paper, we present LAVEA, a system built on top of an Edge computing platform, which offloads computation between clients and Edge nodes, collaborates nearby Edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an Edge-first design and formulated an optimization problem for offloading task selection and prioritized offloading requests received at the Edge node to minimize the response time. In case of a saturating workload on the front Edge node, we have designed and compared various task placement schemes that are tailed for inter-Edge collaboration. We have implemented and evaluated our system. Our results reveal that the client-Edge Configuration has a speedup ranging from 1.3x to 4x (1.2x to 1.7x) against running in local (client-cloud Configuration). The proposed shortest scheduling latency first scheme outputs the best overall task placement performance for inter-Edge collaboration.
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LAVEA: Latency-Aware Video Analytics on Edge Computing Platform
2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017Co-Authors: Shanhe Yi, Qingyang Zhang, Quan Zhang, Qun LiAbstract:We present LAVEA, a system built for Edge computing, which offloads computation tasks between clients and Edge nodes, collaborates nearby Edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an Edge-first design to minimize the response time, and compared various task placement schemes tailed for inter-Edge collaboration. Our results reveal that the client-Edge Configuration has task speedup against local or client-cloud Configurations.
Hideaki Tsuchiya - One of the best experts on this subject based on the ideXlab platform.
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computational study of Edge Configuration and quantum confinement effects on graphene nanoribbon transport
IEEE Electron Device Letters, 2011Co-Authors: Ryutaro Sako, Hiroshi Hosokawa, Hideaki TsuchiyaAbstract:We investigated Edge Configuration and quantum confinement effects on electron transport in armchair-Edged graphene nanoribbons (A-GNRs) by using a computational approach. We found that the Edge bond relaxation has a significant influence not only on the bandgap energy but also on the electron effective mass. We also found that A-GNRs with N = 3m family (N is the number of atoms in its transverse direction, and m is a positive integer) exhibits smaller effective mass by comparing it at the same bandgap energy. As a result, A-GNRs with N = 3m family are found to be favorable for use in channels of field-effect transistors.
