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Cooperative Computation Offloading for Video Analysis in Ultra-Dense LEO Satellite-Terrestrial Networks

Cooperative Computation Offloading for Video Analysis in Ult...

引用

IEEE international conference on Information Communication and Signal Processing (ICICSP)

作者： Qi Zhao Tianjiao Chen Jiang Liu Fangqi Liu Yuke Zhou Beijing Institute of Spacecraft Environment Engineering Beijing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecom Beijing China State Key Laboratory of Networking and Switching Technology Purple Mountain Laboratories Beijing University of Posts and Telecom Beijing Nanjing China

Video analysis using artificial intelligence (AI) is widely adopted in various services. However, ground users with limited resources may not process such tasks locally. Fortunately, the ultra-dense low earth orbit (LEO) satellite networks allow multiple satellites to cooperatively handle these tasks to provide low-latency computing services. Therefore, this paper considers a cooperative computation offloading scheme for video analysis in ultra-dense LEO satellite-terrestrial networks, allowing for flexible task scheduling and video quality selection. Considering the privacy of satellites and the dynamic network environment, the cooperative computation offloading problem is established as a distributed Markov decision process (MDP) to reduce the task delay while increasing the accuracy of video analysis. Then, a multi-agent deep reinforcement learning (DRL) approach is proposed to obtain efficient offloading strategies. Finally, simulations are conducted to verify the feasibility and performance of the proposed scheme.

关键词： Privacy Satellites Processor scheduling Low earth orbit satellites Signal processing algorithms Dynamic scheduling Quality assessment

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ADDAI: Anomaly Detection using distributed AI

ADDAI: Anomaly Detection using Distributed AI

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IEEE international conference on networking, Sensing and Control

作者： Maede Zolanvari Ali Ghubaish Raj Jain Computer Science and Engineering Washington University St. Louis MO USA

ISBN: (纸本)9781665440493

When dealing with the Internet of Things (IoT), especially industrial IoT (IIoT), two manifest challenges leap to mind. First is the massive amount of data streaming to and from IoT devices, and second is the fast pace at which these systems must operate. distributed computing in the form of edge/cloud structure is a popular technique to overcome these two challenges. In this paper, we propose ADDAI (Anomaly Detection using distributed AI) that can easily span out geographically to cover a large number of IoT sources. Due to its distributed nature, it guarantees critical IIoT requirements such as high speed, robustness against a single point of failure, low communication overhead, privacy, and scalability. Through empirical proof, we show the communication cost is minimized, and the performance improves significantly while maintaining the privacy of raw data at the local layer. ADDAI provides predictions for new random samples with an average success rate of 98.4% while reducing the communication overhead by half compared with the traditional technique of offloading all the raw sensor data to the cloud.

关键词： Cloud computing Data privacy Privacy Costs Scalability Robustness Sensors

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distributed computing framework of intelligent sensor network for electric power internet of things 9

Distributed computing framework of intelligent sensor networ...

引用

9th IEEE Joint international Information Technology and Artificial Intelligence conference, ITAIC 2020

作者： Li, Dawei Yu, Ruimo Song, Chunhe Li, Shu Jia, Gengtao Zhou, Xiaoli Start Grid Shanghai Information Telecommunication Company Shanghai200122 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

The power IoT terminal environment is complex, and current smart sensor sensors have problems such as poor networking capabilities and large processing delays, and few specific edge-side automatic networking capabilities. This paper analyzes and elaborates the distributed intelligent sensor computing framework from the perspective of edge intelligent networking, designs a computing model for intelligent sensors of power Internet of Things terminals, and gives the edge-based deployment of convolutional neural network architecture, data aggregation, model training, and intelligent reasoning. Finally, the validity of the proposed framework model is verified by public sample data. © 2020 IEEE.

关键词： Internet of things

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WholeGraph: a fast graph neural network training framework with multi-GPU distributed shared memory architecture 22

WholeGraph: a fast graph neural network training framework w...

引用

Proceedings of the international conference on High Performance computing, networking, Storage and Analysis

作者： Dongxu Yang Junhong Liu Jiaxing Qi Junjie Lai NVIDIA Beijing China

Graph neural networks (GNNs) are prevalent to deal with graph-structured datasets, encoding graph data into low dimensional vectors. In this paper, we present a fast training graph neural network framework, i.e., WholeGraph, based on a multi-GPU distributed shared memory architecture. Whole-Graph consists of partitioning the graph and corresponding node or edge features to multi-GPUs, eliminating the bottleneck of communication between CPU and GPUs during the training process. And the communication between different GPUs is implemented by GPUDirect Peer-to-Peer (P2P) memory access technology. Furthermore, WholeGraph provides several optimized computing operators. Our evaluations show that on large-scale graphs WholeGraph outperforms state-of-the-art GNN frameworks, such as Deep Graph Library (DGL) and Pytorch Geometric (PyG). The speedups of WholeGraph are up to 57.32x and 242.98x compared with DGL and PyG on a single machine multi-GPU node, respectively. The ratio of GPU utilization can sustain above 95% during GNN training process.

关键词： graph neural network GPU shared memory architecture

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Scalable distributed high-order stencil computations 22

Scalable distributed high-order stencil computations

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Proceedings of the international conference on High Performance computing, networking, Storage and Analysis

作者： Mathias Jacquelin Mauricio Araya-Polo Jie Meng Cerebras Systems TotalEnergies EP Research & Technology US LLC

Stencil computations lie at the heart of many scientific and industrial applications. Stencil algorithms pose several challenges on machines with cache based memory hierarchy, due to low re-use of memory accesses if special care is not taken to optimize them. This work shows that for stencil computation a novel algorithm that leverages a localized communication strategy effectively exploits the second generation Cerebras Wafer-Scale Engine (WSE-2), which has no cache hierarchy. This study focuses on a 25-point stencil finite-difference method for the 3D wave equation, a kernel frequently used in earth modeling as numerical simulation. In essence, the algorithm trades memory accesses for data communication and takes advantage of the fast communication fabric provided by the architecture. The algorithm ---historically memory-bound--- becomes compute-bound. This allows the implementation to achieve near perfect weak scaling, reaching up to 503 TFLOPs on WSE-2, a figure that only full clusters can eventually yield.

关键词： multi-processor architecture and micro-architecture stencil computation distributed memory wafer-scale energy high performance computing

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Evolutionary Search for Energy-Efficient distributed Cooperative Spectrum Sensing

Evolutionary Search for Energy-Efficient Distributed Coopera...

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2020 international conference on computing, networking and Communications, ICNC 2020

作者： Jiang, He Li, Lusi He, Haibo Liu, Lingjia University of Rhode Department of Electrical Computer and Biomedical Engineering Island KingstonRI02881 United States Virginia Tech Bradley Department of Electrical and Computer Engineering BlacksburVA24060 United States

ISBN: (纸本)9781728149059

This paper concerns the energy efficiencyoptimization for distributed cooperative spectrum sensing. In the considered distributed spectrum sensing system, each sensor measures the local test statistic for the target spectrum bands and these measurements will be combined together through a weighted consensus protocol. In this way, all the sensors are able to make contributions to the improvement of the spectrum sensing performance. However, the significance of a sensor's contribution depends on its local signal to noise ratio. From energy efficiency perspective, it is not reasonable to invest energy on the sensors that bring little benefits. In this paper, we formulate an energy efficiency optimization framework for distributed spectrum sensing. Our objective is to achieve the target spectrum sensing performance with as few sensors as possible. Accordingly, a genetic algorithm based approach and a particle swarm optimization based approach are proposed for this problem. © 2020 IEEE.

关键词： Energy efficiency

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Topology Self-optimization for Anti-tracking Network via Nodes distributed computing 17th

Topology Self-optimization for Anti-tracking Network via Nod...

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17th EAI international conference on Collaborative computing: networking, Applications, and Worksharing, CollaborateCom 2021

作者： Tian, Changbo Zhang, Yongzheng Yin, Tao Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783030926342

Anti-tracking network aims to protect the privacy of network users’ identities and communication relationship. The research of P2P-based anti-tracking network has attracted more and more attentions because of its decentralization, scalability, and widespread distribution. But, P2P-based anti-tracking network still faces the attacks on network structure which can destroy the usability of anti-tracking network effectively. So, a secure and resilient network structure is an important prerequisite to maintain the stability and security of anti-tracking network. In this paper, we propose a topology self-optimization method for anti-tracking network via nodes distributed computing. Based on convex-polytope topology (CPT), our proposal achieves topology self-optimization by each node optimizing its local topology in optimum structure. Through the collaboration of all nodes in network, the whole network topology will evolve into the optimum structure. Our experimental results show that the topology self-optimization method improves the network robustness and resilience of anti-tracking network when confronting to the dynamic network environment. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Structural optimization

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Reducing Communication in Graph Neural Network Training

Reducing Communication in Graph Neural Network Training

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international conference on High Performance computing, networking, Storage and Analysis (SC)

作者： Tripathy, Alok Yelick, Katherine Buluc, Aydin Univ Calif Berkeley Elect Engn & Comp Sci Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA USA

ISBN: (纸本)9781728199986

Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse connectivity information of the data. GNNs represent this connectivity as sparse matrices, which have lower arithmetic intensity and thus higher communication costs compared to dense matrices, making GNNs harder to scale to high concurrencies than convolutional or fully -connected neural networks. We introduce a family of parallel algorithms for training GNNs and show that they can asymptotically reduce communication compared to previous parallel GNN training methods. We implement these algorithms, which are based on 1D, 1.5D, 2D, and 31) sparse -dense matrix multiplication, using *** on GPU-equipped clusters. Our algorithms optimize communication across the full GNN training pipeline. We train GNNs on over a hundred GPUs on multiple datasets, including a protein network with over a billion edges.

关键词： Graph neural networks distributed training communication-avoiding algorithms

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distributed Reinforcement Learning with States Feature Encoding and States Stacking in Continuous Action Space 16th

Distributed Reinforcement Learning with States Feature Encod...

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16th EAI international conference on Collaborative computing: networking, Applications, and Worksharing, CollaborateCom 2020

作者： Xu, Tianqi Shi, Dianxi Wang, Zhiyuan Chen, Xucan Zhang, Yaowen Beijing100071 China Tianjin300457 China

ISBN: (纸本)9783030675363

The practical application of reinforcement learning agents is often bottlenecked by the duration of training time. To accelerate training, practitioners often turn to distributed reinforcement learning architectures to parallelize and accelerate the training process. This work, we utilize the distributed reinforcement learning architecture to deal with continuous control tasks. The Importance Weighted Actor-Learner Architectures (IMPALA) decouples the acting and learning process to reduce queuing time. IMPALA attains higher scores on the new DMLab-30 set and the Atari-57 set because of its high performance, good scalability, and high efficiency. We extend IMPALA on the continuous control tasks with three changes. We encoder states into low dimensional data to establish an action distribution function that the agents have the ability to exploit and explore. A queue buffer is used to store a mini-batch data and discard them after training. In order to make the agent take appropriate action in the continuous control environment, we stack the past three steps states that attempt to make the robot moves smoothly. Finally, experiments are carried out on Mujoco tasks. The results show that our work is better than other distributed reinforcement learning algorithms. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Reinforcement learning

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Decomposition of power system inspection services for 5G cloud-edge-end collaboration

Decomposition of power system inspection services for 5G clo...

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international Wireless Communications and Mobile computing conference, IWCMC

作者： Hui Xiang Yucheng Wang Yuxiang Lv Yawen Dong Hongyan Wang Yang Yang Liangkang Wei Fanqin Zhou Lei Feng Anhui Jiyuan Software Co. Ltd State Grid Information &#x0026 Telecommunication Group Hefei China State Key Laboratory of Networking and Switching Technology BUPT Beijing China

With the development of the smart power grid, the requirements for intelligent, vivid, and real-time power system inspection services are getting higher. 5G, artificial intelligence, edge computing, big data, virtual reality, and other information technologies bring breakthroughs for power system inspection services evolution, but in the meanwhile, making the services more complex and draining large amounts of computing resources. In the 5G networks with collaborative computing enabled in cloud-edge-end nodes, how to reasonably decompose a complex power system inspection service into multiple task clusters becomes a prerequisite for the efficient distributed deployment of the service and its rapid execution. This paper proposes an approach for the decomposition of intelligent power system inspection services. It considers the computing resource requirements of the functional components in a power system inspection service as well as the interactions between them, perceives the computing capabilities of heterogeneous nodes, such as cloud, edge, and collaborative end terminal in 5G networks, and decomposes the service into multiple task clusters which retain the original logical structure. Simulation results show that the approach can produce suitable results, which can reduce the expected service execution time and improve the utilization of network resources.

关键词： Wireless communication Cloud computing Solid modeling 5G mobile communication Computational modeling Heuristic algorithms Collaboration

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