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DTC: A Dynamic Trusted Collaboration Architecture for Mobile Edge computing 52

DTC: A Dynamic Trusted Collaboration Architecture for Mobile...

引用

52nd Annual IEEE/ifip international conference on Dependable Systems and networks (DSN)

作者： Du, Ruizhong Gao, Yan Hebei Univ Sch Cyber Secur & Comp Hebei Key Lab Highly Trusted Informat Syst Baoding Peoples R China

ISBN: (数字)9781665402620

ISBN: (纸本)9781665402620

Under mobile edge computing (MEC), collaborative computing is a new paradigm to optimize edge network load and server resource allocation. However, there are still issues such as resource imbalance and unreliable identity when collaborative nodes provide computing and caching services for computing devices. Therefore, an efficient and reliable MEC network should be established to manage these challenges. In this paper, a dynamic trusted collaboration scheme is proposed. Specifically, the transaction supervision of nodes in the MEC scenario is first realized through blockchain-based mobile edge computing (BMEC) technology. In BMEC, a management scheme is established laying the foundation for building a trusted MEC environment. Second, a collaborative model is designed. On this basis, it is formulated as an optimization problem based on computing, communication, caching, and energy consumption. The formulation problem is verified to be NP complete. Thus, an improved heuristic algorithm is adopted in this study to solve this problem. Ultimately, the experimental results demonstrate that DTC not only has good performance but also can effectively enhance the reliability of the MEC network compared with other cooperative schemes.

关键词： mobile edge computing collaborative computing blockchain consensus algorithm improved heuristic algorithm

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Improving Real-time Data Streams Performance on Autonomous Surface Vehicles using DataX 32

Improving Real-time Data Streams Performance on Autonomous S...

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32nd Euromicro international conference on parallel, Distributed and network-Based Processing (PDP)

作者： Mellone, Gennaro De Vita, Ciro Giuseppe Coviello, Giuseppe Aucelli, Pietro Patrizio Ciro Ciaramella, Angelo Montella, Raffaele Univ Napoli Parthenope Dept Sci & Technol Naples Italy Univ Naples Parthenope Dep Sci & Technol Naples FL USA NEC Labs Amer Inc Integrated Syst Princeton NJ 08540 USA

ISBN: (纸本)9798350363074;9798350363081

In the evolving Artificial Intelligence (AI) era, the need for real-time algorithm processing in marine edge environments has become a crucial challenge. Data acquisition, analysis, and processing in complex marine situations require sophisticated and highly efficient platforms. This study optimizes real-time operations on a containerized distributed processing platform designed for Autonomous Surface Vehicles (ASV) to help safeguard the marine environment. The primary objective is to improve the efficiency and speed of data processing by adopting a microservice management system called DataX. DataX leverages containerization to break down operations into modular units, and resource coordination is based on Kubernetes. This combination of technologies enables more efficient resource management and real-time operations optimization, contributing significantly to the success of marine missions. The platform was developed to address the unique challenges of managing data and running advanced algorithms in a marine context, which often involves limited connectivity, high latencies, and energy restrictions. Finally, as a proof of concept to justify this platform's evolution, experiments were carried out using a cluster of single-board computers equipped with GPUs, running an AI-based marine litter detection application and demonstrating the tangible benefits of this solution and its suitability for the needs of maritime missions.

关键词： Containerization edge computing cluster ASVs object detection

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Execution Management of Distributed Quantum computing Jobs 5

Execution Management of Distributed Quantum Computing Jobs

引用

2024 international conference on Quantum computing and Engineering

作者： Ferrari, Davide Bandini, Michele Amoretti, Michele Univ Parma Dept Engn & Architecture Quantum Software Lab I-43124 Parma Italy

ISBN: (纸本)9798331541378

In a distributed quantum computation, a large quantum circuit gets sliced into sub -circuits that must be executed at the same time on a quantum computing cluster. The interactions between the sub -circuits are usually defined in terms of non -local gates that require shared entangled pairs and classical communication between different nodes. Assuming that multiple end users submit distributed quantum computing (DQC) jobs to the cluster, an execution management problem arises. This is actually a parallel job scheduling problem, in which a set of jobs of varying processing times need to be scheduled on multiple machines while trying to minimize the length of the schedule. In a previous work, we started investigating the problem considering random circuits and approximating the length of each DQC job with the number of layers of the circuit. In this work, we put forward the study by considering a more realistic model for estimating DQC job lengths and by performing evaluations with circuits of practical interest.

关键词： distributed quantum computing execution management job scheduling quantum network

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A Global Task Scheduling Method Based on network Measurement and Prediction in computing Power networks 29

A Global Task Scheduling Method Based on Network Measurement...

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29th IEEE international conference on parallel and Distributed Systems, ICPADS 2023

作者： Zhang, Chuanfu Li, Wen Chen, Jing Ge, Jing Wang, Di Li, Na Sun, Hao Geng, Yudong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

To improve the task scheduling efficiency in computing power networks, this paper proposes a global task scheduling method based on network measurement and prediction in computing power networks (GTS-MP), which selects a more suitable computing power cluster and storage platform with a higher network availability score between them to implement tasks. The experiments are conducted on a test bench of urban area networks in 12 cities. The results show that this method greatly reduces the execution time of artificial intelligence model training tasks, with a maximum reduction of 40% compared to other plans. © 2023 IEEE.

关键词： computing power network global scheduling network availability score task offloading

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parallel Computation of Random Walks on Distributed Graphs 8

Parallel Computation of Random Walks on Distributed Graphs

引用

8th IEEE/ACIS international conference on Big Data, Cloud computing, and Data Science, BCD 2023

作者： Hu, Yao Keio University Research Institute for Digital Media and Content Hiyoshi Campus Yokohama223-8523 Japan

ISBN: (纸本)9798350373615

A random walk is a process in which a random walker takes consecutive steps in space at equal intervals of time, with the length and direction of each step determined independently. Models related to random walks have been applied in diverse areas such as prediction, recommendation algorithms, and more recently, supervised learning and network analysis. Notably, there is a shortage of parallel computing models for random walks on distributed graphs across various scenarios. Therefore, the objective of this work is to offer a versatile model for random walks on distributed graphs, covering network principles and algorithmic models, followed by their parallel implementations. Evaluation results demonstrate the remarkable efficiency of our network model and highlight the future prospects of parallel random walks. © 2023 IEEE.

关键词： Message passing

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Machine Learning-empowered network Measurement: A Critical Path to Traffic Anomaly Detection in IoT-enabled Smart Grid 30

Machine Learning-empowered Network Measurement: A Critical P...

引用

30th IEEE international conference on parallel and Distributed Systems, ICPADS 2024

作者： Zang, Hongrui Liu, Hongbo Sun, Wei Zhang, Guangyuan Kong, Xiangyu State Grid Jilin Electric Power Company Limited Communication Company Branch Changchun China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9798331515966

Large-scale deployment of Internet of Things (IoT) devices provides efficient data collection and control capabilities in the smart grid, while edge computing plays a key role in increasing the speed of data processing and reducing latency. The growth of edge computing in the smart grid is inevitably increasing the stability requirement in network transmission and the necessity of adequate network measurement to detect traffic anomalies. There are two challenges for measurement in IoT-enabled networks. The first challenge is the high processing speed and limited memory space. The second challenge is the varying network traffic. This paper studies how to use sketch techniques to fulfill the required capabilities of network measurement. Sketches have been considered as the most promising solution for network measurement in recent years, because they greatly optimize the speed and memory usage at the cost of small error. However, most sketches do not work well for varying network traffic. These sketches require to adjust their internal memory usage while the optimal point is sensitive to specified flow size distribution and memory size. To address this problem, we propose a machine learning empowered sketch framework. The proposed sketch framework trains a neural network model that online adjusts the memory usage based on a small sample of flow. We conduct data-driven simulations to evaluate the proposed framework. By comparing measurement results on public dataset, we can see that the proposed framework significantly improves the accuracy of measurement and anomaly detection without sacrificing the line-rate processing capability. © 2024 IEEE.

关键词： Edge computing

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Towards Programmable networking With CoopNet: A Horizontal parallel Multipath Approach

Towards Programmable Networking With CoopNet: A Horizontal P...

引用

international conference on computing, networking and Communications (ICNC)

作者： Voicu, Razvan Cristian Chang, Yusun Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Kennesaw State Univ Sch Elect Engn Atlanta GA USA

ISBN: (纸本)9781665457194

The advent of the Internet of Things (IoT) and machine-to-machine (M2M) communication provide a system for collecting and manipulating big data and a platform for sensing, actuating, and automating the environment. IoT, M2M communication, social networking, and mass multimedia severely strain the communication infrastructure. Thus, the archaic communication frameworks require necessary improvements. One such improvement is the simultaneous usage of parallel communication links of differing radio access networks. This paper presents a Machine Learning (ML) optimization for link selection and use in CoopNet, a horizontal programmable communication architecture. Programmable networking paves the way for advancing communication to improve performance, reliability, security, and policy-based applications, including network decoupling. The ML implementation in CoopNet improves throughput by over 17%, delay by 10%, and reduces individual link utilization.

关键词： Heterogeneous networks Device to Device network Architecture Communication Spectrum Efficiency Multipath Machine Learning RNN FFNN Infrastructure as a Service

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Video Captioning Based on Sign Language Using YOLOV8 Model 6th

Video Captioning Based on Sign Language Using YOLOV8 Model

引用

6th ifip international conference on Internet of Things, ifip IoT 2023

作者： Vidhyasagar, B.S. Lakshmanan, An Sakthi Abishek, M.K. Kalimuthu, Sivakumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India University Putra Malaysia Seri Kembangan Malaysia

ISBN: (纸本)9783031458774

One of the fastest-growing research areas is the recognition of sign language. In this field, many novel techniques have lately been created. People who are deaf-dumb primarily communicate using sign language. Real-time sign language is essential for people who cannot hear or speak (the dumb and the deaf). Hand gestures are one of the non-verbal communication methods used in sign language. People must be aware of these people's language because it is their only means of communication. In this work, we suggest creating and implementing a model to offer transcripts of the sign language that disabled individuals use during a live meeting or video conference. The dataset utilized in this study is downloaded from the Roboflow website and used for training and testing the data. Transfer Learning is a key idea in this situation since a trained model is utilized to identify the hand signals. The YOLOv8 model, created by Ultralytics, is employed for this purpose and instantly translates the letters of the alphabet (A-Z) into their corresponding texts. In our method, the 26 ASL signs are recognized by first extracting the essential components of each sign from the real-time input video, which is then fed into the Yolo-v8 deep learning model to identify the sign. The output will be matched to the signs contained in the neural network and classified into the appropriate signs based on a comparison between the features retrieved and the original signs present in the database. © 2024, ifip international Federation for Information Processing.

关键词： Statistical tests

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Fault Tolerant Data and Model parallel Deep Learning in Edge computing networks 21

Fault Tolerant Data and Model Parallel Deep Learning in Edge...

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21st IEEE international conference on Mobile Ad-Hoc and Smart Systems (MASS)

作者： Sen, Tanmoy Shen, Haiying Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA

ISBN: (纸本)9798350363999;9798350364002

Deep learning (DL) training or retraining on an edge computing network is promising due to its local computation advantage over the cloud. Data and model parallel DL training is a solution to handle the challenges of the large and increasing scale of deep neural network (DNN) models and resource constraints of edge devices. However, its training accuracy by a certain time will be greatly affected or the training process will slow down if one model partition experiences faults due to causes such as edge node mobility, resource unavailability and hardware errors. Though task replication strategies have been used in wireless networks or the TensorFlow machine learning framework, they cannot handle DL job unique features (i.e., large amounts of intermediate results, high accuracy requirement and DL job dataflow) in such DL training. To address this problem, we propose a proactive Fault Tolerant data and model parallel DL system at the Edge (FTLE) to improve DL training time and accuracy by a certain time. FTLE proactively calculates and predicts the fault probability of each model partition using a DL method. It then decides the number of replicas of each partition based on its fault probability, its significance on the accuracy and its importance in the DL job dataflow. Then, FTLE assigns the replicas to edge devices, and schedules a replica to run before each predicted fault occurs based on the DL job dataflow in order to reduce training time and replication time overhead. Our trace-driven experiment on real devices shows that for one epoch training, FTLE shows around 59% reduction in training time, 45% increase in accuracy, and 65% reduction in replication overhead compared to other methods.

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The Performance Analysis of Model parallelism in Deep Residual network 3

The Performance Analysis of Model Parallelism in Deep Residu...

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3rd international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2022

作者： Feng, Yuqin Major in Computer Science Department of Engineering Virginia Tech BlacksburgVA24060 United States

ISBN: (纸本)9781510661301

With the rapid information and communications technology growth and continuous invention, the concept of parallel computing has become the core of computer science, and its capabilities are continually documented in providing high processing solutions. The graphic processing unit (GPU) plays a crucial role in deep learning and parallel computing. The primary purpose of this paper is to discuss two concepts of deep computing: data parallelism and model parallelism. Additionally, the discussion will delve into their importance and associated challenges in parallel processing. Furthermore, the paper summarises the Residual network (ResNet) system with the basic principles and formulas. Establishing efficient parallel algorithms for GPU processing is necessary. This paper evaluates the performance and speed between single-core and multi-core working environments for two kinds of convolutional neural network structures with different types of GPUs. The first one is LeNet, and the other one is VGG16. There are two final results of this experiment. By analyzing the data of performance, VGG16 has better performance than LeNet. By analyzing the data of speed, the speed of LeNet is faster than that of VGG16. © 2023 SPIE.

关键词： Graphics processing unit

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