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DCNN-based load balancing and computation offloading of Intelligent Video Surveillance with Edge computing

DCNN-based load balancing and computation offloading of Inte...

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Evolutionary Algorithms and Soft computing Techniques (EASCT), international conference on

作者： Shweta Koparde G. Sujatha Srinivas Jangirala P. Santhosh Kumar Computer Engineering Dr. DY Patil Institute of Technology Pune India Computer Science and Engineering Kommuri Pratap Reddy Institute of Technology Ghatkeshar India Jindal Global Business School O.P. Jindal Global University Sonipat India Information Technology College of Engineering and Technology SRM Institute of Science and Technology Ramapuram Chennai India

In this study, we offer a distributed Intelligent Video Surveillance (DIVS) system that is set up in an environment of edge computing and is based on Deep Learning (DL). For the DIVS system, we developed a distributed DL training model and a multi-layer edge computing architecture. In order to lower the high network connection overhead and offer precise and low-latency video analysis solutions, the DIVS system may move computing processes from the network core to the network edges. We implement the proposed DIVS system to address the workload balance, model synchronization, and concurrent training problems. We offer task-level parallel and model-level parallel training approaches to further accelerate the video analysis process. Deep Convolutional Neural networks with Load Balancing and Computation Offloading (DCNN-LBCO) is a technique for creating a scalable cloud-based online video platform. In order to minimize security concerns, we provide a particular security layer for MEC systems in this study along with a load balancing and computation offloading technique that work together. A load balancing and computation offloading (LBCO) technique is first recommended in order to distribute MDU among sBSs effectively. A specific advanced encryption standard (AES) cryptographic method is also provided, together with an encryption and decryption key based on electrocardiogram (ECG) signals, to prevent data vulnerability during transmission. A load balancing, compute offloading, and security model that is integrated is provided to lessen the system's time and energy requirements. Extensive testing results show that, as compared to local execution, our technique may save between 68.2% and 72.4% of system utilisation, both with and without extra security layers.

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JointCloud Cross-chain Verification Model of Decentralized Identifiers

JointCloud Cross-chain Verification Model of Decentralized I...

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40th IEEE international Performance, computing, and Communications conference (IPCCC)

作者： Zhong, Tao Shi, Peichang Chang, Junsheng Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab PDL Changsha Peoples R China Natl Univ Def Technol Coll Comp Inst Comp Technol Changsha Peoples R China

ISBN: (纸本)9781665443319

When multiple entities communicate or collaborate in JointCloud, identities are the very prior basis to build trust with each other. Decentralized identifier (DID) can provide a trusted identity with blockchain technology and a complete method of identity verification based on verifiable credentials, which solves problems of conventional centralized identity. However, current DIDs can only conduct verification within a single blockchain, which limits the interoperability of DIDs on different blockchains. Network isolation hinders the verification of DIDs on different blockchains and thus there is a need to break the barrier between blockchains. In this paper, we propose a model to conduct cross-chain verification of DIDs. We build a system of credit evaluation to describe the credibility of DIDs in a unified way and deploy smart contracts to implement cross-chain verification of DIDs. Experimental results verifies the feasibility of the model, which realizes cross-chain verification of DIDs in the networks of blockchain.

关键词： Decentralized identifier cross-chain verification blockchain

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GraVAC: Adaptive Compression for Communication-Efficient distributed DL Training

GraVAC: Adaptive Compression for Communication-Efficient Dis...

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IEEE international conference on Cloud computing, CLOUD

作者： Sahil Tyagi Martin Swany Indiana University Bloomington USA

distributed data-parallel (DDP) training improves overall application throughput as multiple devices train on a subset of data and aggregate updates to produce a globally shared model. The periodic synchronization at each iteration incurs considerable overhead, exacerbated by the increasing size and complexity of state-of-the-art neural networks. Although many gradient compression techniques propose to reduce communication cost, the ideal compression factor that leads to maximum speedup or minimum data exchange remains an open-ended problem since it varies with the quality of compression, model size and structure, hardware, network topology and bandwidth. We propose GraVAC, a framework to dynamically adjust compression factor throughout training by evaluating model progress and assessing gradient information loss associated with compression. GraVAC works in an online, black-box manner without any prior assumptions about a model or its hyperparameters, while achieving the same or better accuracy than dense SGD (i.e., no compression) in the same number of iterations/epochs. As opposed to using a static compression factor, GraVAC reduces end-to-end training time for ResNet101, VGG16 and LSTM by 4.32×, 1.95× and 6.67× respectively. Compared to other adaptive schemes, our framework provides 1.94× to 5.63× overall speedup.

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On Developing Smart IoT-Based distributed System for Remote Detection of Slope Movements

On Developing Smart IoT-Based Distributed System for Remote ...

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international conference on computing, Networking and Communications (ICNC)

作者： Rahul Debnath Ahmed Abdelmoamen Ahmed Md Jobair Bin Alam Department of Computer Science Prairie View A&M University Prairie View TX USA Department of Civil Engineering Prairie View A&M University Prairie View TX USA

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

Landslides or slope failures cause significant damage to the transportation infrastructure worldwide, resulting in billions of dollars of property damage. The initial slope movement of highway slopes, caused by environmental factors and man-made activities, must be detected early to prevent significant losses in infrastructure damages and human lives. However, traditional monitoring approaches for slope movement, such as periodic human inspection and surveying, are labor-intensive and time-consuming. With the advent of the Internet of Things (IoT) and high-speed internet, the potential to use such technologies for monitoring remote earth movement of highway embankments is becoming increasingly important. This paper presents an IoT-based distributed system for the early detection of slope movements to assist transportation authorities in immediately repairing the slope before a major failure occurs. We built a laboratory prototype embankment, equipped with various IoT sensors (e.g., tilt, moisture, and tensiometer sensors), that simulate a failure-prone slope to evaluate the effectiveness of our system. A user-friendly GUI is developed to enable users to utilize the system remotely. The collected sensory data for four months allowed us to analyze the soil hydrologic dynamics and the slope performance by detecting changes in the slope tilting both parallel (pitch) and perpendicular (roll). The experimental results showed that our system could detect negligible movement (1mm) and rotation (0.02°) of slopes.

关键词： Road transportation Working environment noise Transportation Artificial neural networks Sensor systems Vectors Internet of Things Remote monitoring Intelligent sensors Graphical user interfaces

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HPC and GPU solutions for radio interferometry using RICK

HPC and GPU solutions for radio interferometry using RICK

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Emanuele De Rubeis Claudio Gheller Giovanni Lacopo Luca Tornatore Pascal Jahan Elahi Maciej Cytowski Giuliano Taffoni Ugo Varetto Dipartimento di Fisica e Astronomia Università di Bologna Istituto di Radioastronomia INAF via Gobetti 93/2 Bologna Italy Istituto di Radioastronomia INAF Via P. Gobetti 101 Bologna Italy INAF Astronomical Observatory of Trieste Università degli studi di Trieste Trieste Italy INAF Astronomical Observatory of Trieste via GB Tiepolo 11 Trieste Italy Pawsey Supercomputing Centre 1 Bryce Avenue Kensington WA Australia

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Since the last decade, radio astronomy has started a new era: the advent of the Square Kilometer Array (SKA), preceded by its pathfinders, will produce a huge amount of data that will be hard to process with a traditional approach. This means that the current state-of-the-art software for data reduction and imaging will have to be re-modeled to face such data challenge. In order to manage such an increase in data size and computational requirements, scientists need to exploit modern high-performance computing (HPC) architectures. In particular, heterogeneous systems, based on complex combinations of CPUs, accelerators, high-speed networks and composite storage devices need to be used in an efficient and effective way. In this paper, we present an overview on Radio Imaging Code Kernels (RICK; [1]; [2]; [3]), a code able to perform the most computationally demanding steps of w-stacking gridder algorithm exploiting distributed parallelism and GPU acceleration. GPU offloading is possible through CUDA, HIP, and OpenMP, aiming at the largest possible usability among multiple architectures. After detailing the (multi-)GPU approach to the problem and listing all the new code implementations, we analyze its performances considering both the computational and communication workload. We will show how the full, distributed GPU offload of the code, first of its kind and crucial to deal with increasingly large interferometric data, represents not only an extremely fast and optimized approach, but also the greenest one if compared to its parallel CPU counterpart. This code, now publicly available, has been tested with a wide variety of modern interferometers and SKA pathfinders. This represents, to date, the first example of radio imaging software fully enabled to GPUs, becoming a potential state-of-the-art approach for the upcoming SKA. Finally, we will also present the future perspectives about the code, planned to be converted into a library and possibly be used by any of the m

关键词： Performance evaluation Codes Radio astronomy High performance computing Graphics processing units Imaging Radio interferometry Computer architecture Software Usability

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PRACTICAL MASSIVELY parallel MONTE-CARLO TREE SEARCH APPLIED TO MOLECULAR DESIGN 9

PRACTICAL MASSIVELY PARALLEL MONTE-CARLO TREE SEARCH APPLIED...

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9th international conference on Learning Representations, ICLR 2021

作者： Yang, Xiufeng Aasawat, Tanuj Kr Yoshizoe, Kazuki Chugai Pharmaceutical Co. Ltd Japan Parallel Computing Lab - India Intel Labs India RIKEN Center for Advanced Intelligence Project Japan

It is common practice to use large computational resources to train neural networks, known from many examples, such as reinforcement learning applications. However, while massively parallel computing is often used for training models, it is rarely used to search solutions for combinatorial optimization problems. This paper proposes a novel massively parallel Monte-Carlo Tree Search (MP-MCTS) algorithm that works efficiently for a 1,000 worker scale on a distributed memory environment using multiple compute nodes and applies it to molecular design. This paper is the first work that applies distributed MCTS to a real-world and non-game problem. Existing works on large-scale parallel MCTS show efficient scalability in terms of the number of rollouts up to 100 workers. Still, they suffer from the degradation in the quality of the solutions. MP-MCTS maintains the search quality at a larger scale. By running MP-MCTS on 256 CPU cores for only 10 minutes, we obtained candidate molecules with similar scores to non-parallel MCTS running for 42 hours. Moreover, our results based on parallel MCTS (combined with a simple RNN model) significantly outperform existing state-of-the-art work. Our method is generic and is expected to speed up other applications of MCTS. © 2021 ICLR 2021 - 9th international conference on Learning Representations. All rights reserved.

关键词： Reinforcement learning

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HeavyTracker: An Efficient Algorithm for Heavy-Hitter Detection in High-Speed networks

HeavyTracker: An Efficient Algorithm for Heavy-Hitter Detect...

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international conference on parallel and distributed Systems (ICPADS)

作者： Jiakun Yu Yu-E Sun He Huang Yang Du Guoju Gao Hongli Xu Shigang Chen School of Computer Science and Technology Soochow University Suzhou China School of Rail Transportation Soochow University Suzhou China Ministry of Education Key Laboratory of Embedded System and Service Computing (Tongji University) Shanghai China School of Computer Science and Technology University of Science and Technology of China Hefei China

Detecting heavy hitters that constitute the majority of network traffic is a critical task in network measurement. However, the highly skewed network traffic and the size-limited on-chip memory pose great challenges for heavy-hitter detection. The prior arts either use sketches to track all flows or maintain a fix-sized tracking list for recording elephant flows, resulting in limited detection precision or time-consuming tracking list exchanges. This paper presents HeavyTracker, an efficient algorithm that detects heavy hitters based on a count-with-threshold strategy. A two-dimensional tracker unit array is employed in our design to capture the heavy hitters, where each flow is randomly mapped to one unit in all rows. The tracker unit is designed to accept many flows and precisely report the largest two with frequencies that reach a predefined threshold. This design eliminates the frequent and time-consuming exchanges for maintaining the tracking list, allowing us to use a hash table to track the reported elephant flows. Experimental results based on real Internet traces show that HeavyTracker achieves a high $\mathbf{F}_{\beta}$-Score of threshold-t detection (0.97) and over 99% precision of top-k detection under a tight memory size. Besides, it reduces the frequency estimation error by 97.4% compared to the state-of-the-art.

关键词： Art High-speed networks Measurement uncertainty Telecommunication traffic Size measurement Frequency estimation Internet

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GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

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international conference on parallel and distributed Systems (ICPADS)

作者： Ran Qiu Hu Liang Yuchen Liu Shengrong Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures.

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A Medical Pre-diagnosis Scheme Based on Neural Network and Inner Product Function Encryption

A Medical Pre-diagnosis Scheme Based on Neural Network and I...

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Electronic Communication and Artificial Intelligence (IWECAI), international Workshop on

作者： Xu Cui Hao Liu Min Tang Yihong Ma School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China Center for Applied Mathematics of Guangxi (GUET) Guilin China

Artificial neural networks are widely used in various fields, such as intelligent road networks, Internet of Things, and smart medical systems due to their ability to process large amounts of data in parallel, store information in a distributed manner, and self-organize and self-learn. Cloud computing technology has further expanded the development of neural network applications. However, user data often contains sensitive information, and once the data management right is transferred to the cloud, it faces serious security and privacy issues. In the medical field, privacy-preserving implementation of classification algorithms is crucial for ensuring the privacy of electronic medical diagnosis services. Current privacy-preserving medical pre-diagnosis schemes based on homomorphic encryption impose a significant computational and communication burden on users and servers. This paper proposes an efficient privacy-preserving medical pre-diagnosis scheme based on neural networks and inner product function encryption that protects user privacy during pre-diagnosis while having small computational and communication overheads.

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Aerial computing Offloading by distributed Deep Learning in Collaborative Satellite-terrestrial networks 13

Aerial Computing Offloading by Distributed Deep Learning in ...

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13th international conference on Wireless Communications and Signal Processing, WCSP 2021

作者： Li, Haofei Chen, Chen Li, Cong Liu, Lei Gui, Guan Xidian University State Key Laboratory of Integrated Services Networks Xi'an China State Grid JiLin Province Electric Power Company Limited Information Communication Company Changchun China Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing China

ISBN: (纸本)9781665407854

With the rapid development of the Internet of Things (IoT) and its derivative technologies such as Aerial IoT (AIoT), the number of smart devices and their computing requirements have exploded. It makes it difficult for the existing ground base stations to guarantee the user's computing requirements and low latency requirements. The edge computing architecture of orbiting satellites, which provides feasible solutions to the above challenges. In this paper, we design a computing offloading strategy for users to minimize the weighted sum of latency and energy consumption in the cloud-edge-ground three layers hybrid AIoT architecture. Tasks can be calculated locally, on the satellite side, or forwarded to the cloud computing center. Then, the collaborative satellite-terrestrial network distributed offloading algorithm(CNDO) is proposed based on parallel neural networks to give multiple optimal offloading decisions in AIoT. Experimental results show that this scheme has a better performance compared with other comparison schemes. © 2021 IEEE.

关键词： computation offloading

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