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On-Board Federated Learning in Orbital Edge computing 29

On-Board Federated Learning in Orbital Edge Computing

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

作者： Jabbarpour, Mohammad Reza Javadi, Bahman Leong, Philip H.W. Calheiros, Rodrigo N. Boland, David Butler, Chris Western Sydney University Sydney Australia The University of Sydney Sydney Australia AUCloud Melbourne Australia

ISBN: (纸本)9798350330717

Low Earth Orbit (LEO) satellite constellations are used for a wide range of applications including earth observation, communication services, navigation, and positioning. They have emerged as a new source of data but transferring this data to a ground station (GS) for analysis and machine learning requires extensive bandwidth and incurs high latency. Limited battery capacity, communication and computing capabilities are other factors affecting the training process. Federated Learning (FL) is being used to address these challenges, although it heavily relies on the GS for model aggregation. In this paper, we consider Orbital Edge computing (OEC) as an architecture for LEO satellite constellations and propose an on-board Federated Learning to reduce communication with the GS. We present a novel decentralised FL algorithm, called FedOrbit, based on reinforcement learning cluster formation and satellite visiting patterns to utilise intra and inter-satellite communications for model aggregation. Extensive performance evaluation under Walker Delta-based LEO constellation configurations and different datasets including MNIST, CIFAR-10, and EuroSat revealed that FedOrbit can significantly reduce communication rounds, power consumption and training time in comparison to state-of-the-art FL approaches while maintaining a high accuracy. FedOrbit demonstrates a significant decrease in power consumption, specifically by 8.8% and 79.1% for the MNIST dataset, when compared to decentralised and centralised FL approaches, respectively. The proposed technique can also reduce the training time by 5× and 48× compared with the decentralised and centralised FL approaches, respectively. © 2023 IEEE.

关键词： Communication Cost Energy Consumption Federated Learning Low-Earth Orbit Orbital Edge computing

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Accelerating distributed Deep Reinforcement Learning by In-Network Experience Sampling 30

Accelerating Distributed Deep Reinforcement Learning by In-N...

引用

30th Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Furukawa, Masaki Matsutani, Hiroki Keio Univ Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781665469586

A computing cluster that interconnects multiple compute nodes is used to accelerate distributed reinforcement learning based on DQN (Deep Q-Network). In distributed reinforcement learning, Actor nodes acquire experiences by interacting with a given environment and a Learner node optimizes their DQN model. Since data transfer between Actor and Learner nodes increases depending on the number of Actor nodes and their experience size, communication overhead between them is one of major performance bottlenecks. In this paper, their communication performance is optimized by using DPDK (Data Plane Development Kit). Specifically, DPDK-based low-latency experience replay memory server is deployed between Actor and Learner nodes interconnected with a 40GbE (40Gbit Ethernet) network. Evaluation results show that, as a network optimization technique, kernel bypassing by DPDK reduces network access latencies to a shared memory server by 32.7% to 58.9%. As another network optimization technique, an in-network experience replay memory server between Actor and Learner nodes reduces access latencies to the experience replay memory by 11.7% to 28.1% and communication latencies for prioritized experience sampling by 21.9% to 29.1%.

关键词： Computational modeling Reinforcement learning Ethernet Data transfer Servers Low latency communication Kernel

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Simplifying Graph-parallel Computation in Apache Spark with GraphX 8

Simplifying Graph-Parallel Computation in Apache Spark with ...

引用

8th international conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Vyakaranam, Lasya Raman, Ramakrishnan Shah, Harshal Mishra, Nilamadhab Meenakshi, R. Murugan, S. Pune Maharashtra Pune India Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350367904

Streamlining Apache graph-parallel computing the goal of integrating Spark with GraphX is to make processing massive amounts of graph data easier and faster. The goal is to streamline the process of creating and running graph algorithms by using GraphX, a Spark-based distributed graph processing platform. The eventual objective is to create a scalable platform that is easy to use for graph-structured data analysis and manipulation. Improving GraphX's performance, expanding its features, and integrating it with Spark's ecosystem are all part of this process. The expected result is a flexible and strong framework that lets data scientists and developers execute complicated graph analytics tasks with ease. This will pave the way for applications in many fields, including recommendation systems, social network analysis, and biological network analysis. Optimizing performance in graph-parallel computer situations, making the development process easier, and making it more scalable are the main goals. The graph structure visualization results in graph data revealing values in the range of 1.2 to 5.6 for a sample of five distinct vertex/edge types. The degree distribution histogram in the same dataset shows that, for five different frequencies, the numbers range from around 1.5 to 4.5. For the same dataset, the PageRank Convergence Plot shows values ranging from 0.4 to 0.9 for five distinct iterations/scores. © 2024 IEEE.

关键词： Apache Spark Big Data Graph Analytics Graph-parallel Computation GraphX

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Robust Zero-watermarking Algorithm for Medical Images Based on GFTT-KAZE and DCT 26

Robust Zero-watermarking Algorithm for Medical Images Based ...

引用

26th ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing, SNPD-Winter 2023

作者： Dong, Shilong Li, Jingbing Bhatti, Uzair Aslam Ma, Jixin Dong, Fangchun Li, Yanyi School of Information and Communication Engineering Hainan University Haikou China School of Computing & Mathematical Sciences University of Greenwich London United Kingdom

ISBN: (纸本)9798350345865

As medical technology advances, medical images are becoming increasingly important in diagnosis and therapy. However, due to the potential repercussions of leaking or tampering, which could jeopardize patient privacy and treatment, the protection and management of medical images has become a critical concern. As a result, ensuring the encryption and confidentiality of medical images has become critical. Based on GFTT-KAZE-DCT, this research provides a strong digital watermarking system for medical photos. To begin, the GFTT (Good Features to Track) method is used to extract feature points from medical pictures. To describe the feature points, the KAZE method is used, resulting in a feature descriptor matrix. Perceptual hashing is used to further process the initial image. The feature matrix created by the KAZE descriptor is then subjected to DCT (Discrete Cosine Transform). The coefficient matrix generated by DCT is used to acquire the desired eigenvector. To better resist geometric and conventional attacks, the watermark is flexibly integrated with chaos encryption technology, hash function, and the 'third-party concept.' In conclusion, our algorithm provides an effective way for protecting the privacy and integrity of medical images. It incorporates advanced techniques such as GFTT-KAZE-DCT and perceptual hash, and it can withstand a variety of attacks. © 2023 IEEE.

关键词： Discrete cosine transforms

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Automatic load sharing of Transformers using Fuzzy Logic in a distributed generation environment

Automatic load sharing of Transformers using Fuzzy Logic in ...

引用

2023 international conference on Recent advances in Electrical, Electronics and Digital Healthcare Technologies, REEDCON 2023

作者： Ahmad, Abrar Ali, Md Safdar Dr, Ibraheem Sada, Umar Majeed Jamia Millia Islamia Department of Electrical Engineering New Delhi India

ISBN: (纸本)9781665493826

The transformer is very expensive and large equipment of power system. It operates for 24 hours of a day and feeds the load. Sometimes the situation may occur when the load on the transformer is suddenly increased above its rated capacity because of the growing demands of energy. When this situation occurs, the transformer will be overloaded and overheated and may damage the insulation of transformer resulting in interruption of supply. The best solution to avoid the overloading is to operate the number of transformers in parallel. In the suggested approach auxiliary transformer will share the load when the load on the main transformer will rise above its rated capacity. The main aim of the work is also to provide an un-interrupted power supply to the energy consumers (priority based). Fuzzy logic is used for parallel operation of transformers. The problem of interruption of supply due to transformer overloading or overheating can be avoided. © 2023 IEEE.

关键词： Fuzzy logic

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An Efficient distributed Secured Broadcast Stateless Group Key Management Scheme for Mobile Ad Hoc Networks

An Efficient Distributed Secured Broadcast Stateless Group K...

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2022 IEEE international conference on advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Janani, V.S. Devaraju, M. Easwari Enginering College Department of ECE Chennai India

ISBN: (纸本)9781665495295

This paper addresses the issues in managing group key among clusters in Mobile Ad hoc Networks (MANETs). With the dynamic movement of the nodes, providing secure communication and managing secret keys in MANET is difficult to achieve. In this paper, we propose a distributed secure broadcast stateless groupkey management framework (DSBS-GKM) for efficient group key management. This scheme combines the benefits of hash function and Lagrange interpolation polynomial in managing MANET nodes. To provide a strong security mechanism, a revocation system that detects and revokes misbehaviour nodes is presented. The simulation results show that the proposed DSBS-GKM scheme attains betterments in terms of rekeying and revocation performance while comparing with other existing key management schemes. © 2022 IEEE.

关键词： Mobile ad hoc networks

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Fine-grained Task Scheduling Based on Graph Neural Network and Federated Learning in Vehicle Edge computing 29

Fine-grained Task Scheduling Based on Graph Neural Network a...

引用

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

作者： Zhang, Gaowei Song, Junping Hu, Yahui Fan, Pengfei Li, Chong Zhou, Xu Chinese Academy of Sciences Computer Network Information Center Beijing China University of Chinese Academy of Sciences Beijing China China University of Mining and Technology Beijing China

ISBN: (纸本)9798350330717

Vehicle Edge computing (VEC) has emerged as an efficacious paradigm that supports real-time, computation-intensive vehicular applications. However, due to the highly dynamic nature of computing node topology, existing scheduling algorithms need to more effectively apprehend the characteristics of fine-grained task topologies and network topologies. Moreover, they require significant communication overhead and training costs, making them inadequate for fine-grained task scheduling in vehicular networks. In response, our research explores fine-grained task scheduling issues within VEC scenarios, proposing a scheduling algorithm based on Graph Neural Networks and Federated Learning (FL-GNN). This algorithm maintains a global scheduling model that periodically aggregates local scheduling models deployed on Roadside Units (RSUs) and high-performance vehicles. Furthermore, to enhance the model's ability to perceive topology and expedite the convergence rate, we incorporate a graph neural network layer in each local model to preprocess the raw state of the VEC environment. Lastly, we construct a simulation platform and implement multiple competitive solutions, demonstrating the superiority of the FL-GNN algorithm in aspects such as reducing the average task delay, balancing the load, and improving the task scheduling success rate. © 2023 IEEE.

关键词： deep reinforcement learning Federated learning fine-grained task scheduling graph neural networks vehicle edge computing

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RCSR: Robust Client Selection and Replacement in Federated Learning 29

RCSR: Robust Client Selection and Replacement in Federated L...

引用

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

作者： Li, Xuerui Zhao, Yangming Qiao, Chunming University at Buffalo Buffalo United States University of Science and Technology of China Hefei China

ISBN: (纸本)9798350330717

In Federated Learning (FL), to improve the training efficiency, we don't need to let all of the clients join in the training process. Instead, we can select some specific clients to join in the training. In particular, if some of these selected clients become problematic due to various reasons (e.g. shortage of power, poor internet connection, or being vulnerable to attacks) and thus could not successfully complete the training process, then we can discard those clients during training, in order to improve the efficiency. However, discarding those clients could increase the data source's bias, because the data categories that contain those clients' data would be underrepresented during the training process. To solve this problem, in this paper, we propose a robust client selection and replacement approach called RCSR. Using RCSR, we first cluster all clients according to their data distribution, and then use normal clients in the same cluster (with similar data distributions) to replace those problematic clients during training. We apply our methods to a couple of application scenarios in edge computing, and our results show that our methods can save training costs without affecting the accuracy. © 2023 IEEE.

关键词： Client Selection Dataset Bias Edge computing Federated Learning

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The Design and Optimization of Memory Ballooning in SEV Confidential Virtual Machines 15

The Design and Optimization of Memory Ballooning in SEV Conf...

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15th IEEE international conference on Joint Cloud computing (JCC)

作者： Deng, Chang Shen, Zheyun Dingji Li Mi, Zeyu Xia, Yubin China Elect Technol Grp Corp Res Inst 32 Shanghai 201808 Peoples R China Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China

ISBN: (纸本)9798350387339

With the popularity of confidential computing, confidential virtual machines (CVMs) have been widely adopted and they guarantee strong security by hardware. However, there still exist some problems in memory management in CVMs. Since private memory pages of CVMs are encrypted and cannot be accessed by hypervisors, existing CVMs employ static page management to avoid crashes due to the relocation of encrypted memory pages, leading cloud platforms managing CVMs to face more severe memory management pressures than before. Memory ballooning, as an efficient, flexible, and highly compatible memory management mechanism in virtualization, is not available in CVMs based on SEV (Secure Encrypted Virtualization). In this paper, we analyze the design of SEV CVMs and memory ballooning, and enable memory ballooning on SEV CVMs by substituting static page management with dynamic page management and addressing communication issues between the guest frontend and the host backend. Besides, we propose three performance optimization strategies for memory ballooning on SEV CVMs, including asynchronous reclaiming on the host side, an additional shadow vCPU on the guest side, and accelerating cache flushing operations in the host kernel. Experiments show that the time cost of reclaiming memory from SEV CVMs by memory ballooning can be reduced by up to 38 times and up to 55% overhead caused by delaying reclamation in real-world applications like MYSQL can be eliminated.

关键词： Memory Ballooning Secure Encrypted Virtualization Confidential Virtual Machine

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Joint Service Placement and Container Retention for Serverless-Based Vehicular Edge computing 29

Joint Service Placement and Container Retention for Serverle...

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

作者： Hu, Shihong Qu, Zhihao Tang, Bin Ye, Baoliu Hohai University School of Computer and Software China Nanjing University National Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350330717

Lightweight containers in serverless-based vehicular edge computing (SVEC) offer flexible service provisioning for edge service providers (ESPs), enabling quick response to various service requests from mobile vehicles and improving the quality of service delivery. However, the unpredictability of vehicle mobility and the variability of service requests pose significant challenges to service placement for ESPs. Moreover, the cold-start latency experienced by service containers in SVEC can greatly hinder the responsiveness of vehicle services. To mitigate this impact and enhance the overall system performance, container retention is introduced as a solution. In this paper, we study the joint service placement and container retention problem in the dynamic SVEC system. First, we model the joint decision problem with specific constraints and aim to maximize the profit of each edge considering vehicle mobility. Second, an online co-decision scheme called CMU-O is proposed to solve the problem, which adopts an improved upper confidence bound method based on dynamic osmotic pressure (UCB-OP). Finally, the experimental results demonstrate that the service request success rate of the CMU-O is on average 7% higher than the baselines. Furthermore, the profits obtained under the CMU-O are also on average 23% higher than the baselines. © 2023 IEEE.

关键词： container retention serverless computing Service placement vehicular edge computing

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