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A Novel Robust Reinforcement Learning-based Dependent Task Offloading Algorithm for Mobile Edge Intelligence 29

A Novel Robust Reinforcement Learning-based Dependent Task O...

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

作者： Deng, Xu Sun, Peng Yang, Kun Fang, Gaoyun Boukerche, Azzedine Song, Liang Fudan University Academy for Engineering and Technology China Innovation Platform for Academicians of Hainan Province Haikou570228 China Duke Kunshan University China University of Ottawa Canada

ISBN: (纸本)9798350330717

With the rise of advanced applications based on Artificial Intelligence (AI) and Internet-of-Things (IoT), mobile devices have become more intelligent, introducing a novel concept, Mobile Edge Intelligence. But the limited on-board resources often hinder the capabilities of mobile devices. Mobile Edge Computing (MEC), regarded as an effective method to expand device capability, effectively overcomes this barrier. However, the dynamic networks driven by mobility and the dependency on applications pose significant challenges for offloading, which can degrade MEC's overall performance. Therefore, how to effectively combine the above points to achieve a stable and effective sharing of computing resources between devices and servers is a critical issue. In this paper, we consider a multi-slot MEC system with device mobility and multiple applications of unknown arrival. To improve application completion rate while reducing task delay, we introduce a novel, robust distributed offloading algorithm, which calls the Multi-Attention Pointer network-based Reinforcement Learning algorithm (MAPRL), for the dynamic and unstable resource offloading scenario. Numerous experiments have been carried out to demonstrate that, compared with the existing methods, MAPRL exhibits robustness when facing the changing scenario, it can adapt to the unknown workload and dynamic network connections to enhance the offloading performance. © 2023 IEEE.

关键词： deep reinforcement learning dependent task offloading Mobile edge computing

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Cross-regional Teaching Resource Sharing Solution based on HADOOP Architecture 24

Cross-regional Teaching Resource Sharing Solution Based on H...

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2024 International Symposium on Artificial Intelligence for Education, ISAIE 2024

作者： Xu, Yingzi Yu, Li Qingyuan Polytechnic Guangdong Qingyuan China

ISBN: (纸本)9798400707100

This study explores a cross-regional teaching resource sharing solution based on Hadoop architecture, aiming to solve the current problem of uneven distribution of educational resources. First, the challenges of existing resource sharing solutions in terms of network infrastructure differences, data consistency management, and system scalability are analyzed. To address these issues, Hadoop provides an effective solution path with its distributed storage and parallel computing *** (Hadoop distributed File System) ensures high availability and fault tolerance by distributing data storage on multiple nodes, thus realizing efficient cross-regional resource *** model accelerates the analysis and processing of educational data, which improves the response speed and resource utilization of the *** Resource Scheduler further optimizes the system performance and reduces network latency by dynamically allocating computing resources and load balancing mechanism. The study shows that the application of Hadoop architecture in education not only improves the management efficiency of educational resources, but also significantly enhances the fairness and reliability of resource sharing, providing solid technical support for maximizing the utilization of educational resources. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： MapReduce

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FPGAs-based Edge Computing with distributed Leader Election on Internet of Things

FPGAs-based Edge Computing with Distributed Leader Election ...

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2023 South American conference on Visible Light Communications, SACVLC 2023

作者： Vera, Daniela Toledo, Karel Bolufe, Sandy Fernandez-Campusano, Christian Department of Electrical Engineering Santiago9170124 Chile

ISBN: (纸本)9798350358605

This paper presents a flexible architecture for the Internet of Things (IoT) with edge computing based on Field Programmable Gate Arrays (FPGAs) and distributed leader election (DLE). An enhanced DLE algorithm is introduced which implicitly monitors the communication nodes on the wireless sensor networks (WSNs) and chooses the appropriate leader to improve the reliability of the system. At the edge computing layer, devices rely on hardware description language to leverage the versatility and parallel processing capabilities of FPGAs. We also perform simulations and hardware implementation to evaluate the performance of specific tasks. Numerical results evidence the rise of the leader and how the data from network nodes are utilized to distinguish signals from noise successfully. © 2023 IEEE.

关键词： Internet of things

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What Appears Suboptimal May Surprise You: A Fixed-Rate Scheduling Policy for Geo-distributed CoFlows 29

What Appears Suboptimal May Surprise You: A Fixed-Rate Sched...

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

作者： Liu, Haoyu Ding, Feiyan Wang, Yao Xiang, Qiao Shu, Jiwu Du, Haizhou Kong, Linghe Liu, Xue Xiamen University School of Informatics Xiamen China University of Electric Power of Shanghai School of Computer Science and Technology Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China McGill University School of Computer Science Montreal Canada

ISBN: (纸本)9798350330717

All existing coflow scheduling algorithms compute dynamic-rate schedules that change the rates of flows during transmission. In this paper, we make a crucial finding: although dynamically adjusting the rates of flows could lead to a better coflow completion time (CCT) in theory, it would introduce additional pressures on the congestion control mechanism in the underlying network, which result in poor CCT in practice. This difference between theoretical CCT and practical CCT is further exacerbated in wide-area networks, where the topology does not provide any bisection guarantee as data center networks do. To this end, we designed a fixed-rate coflow scheduling policy called FSCO. Although in theory, the best fixed-rate schedule is usually suboptimal, it keeps the in-flight traffic relatively steady, reducing the risk of triggering congestion control. The core of FSCO is an efficient scheduling algorithm based on the classic network utilization maximization (NUM) framework. We implement a prototype of FSCO and evaluate its performance extensively using real-world topologies and coflow traces. Experimental results show that the total CCT reduces up to 30% compared to baselines while yielding up to 12× speedups compared to the solver. © 2023 IEEE.

关键词： Coflow Scheduling Dual decomposition network Utility Maximization

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N-MlpE: Optimizing Multilayer Perceptron network-based Knowledge Graph Embedding Model with Neighborhood Information 29

N-MlpE: Optimizing Multilayer Perceptron Network-based Knowl...

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

作者： Xu, Qing Ren, Xiaoli Ren, Kaijun Lin, Jiarun Li, Xiaoyong National University of Defense Technology College of Meteorology and Oceanography Changsha China

ISBN: (纸本)9798350330717

As an effective knowledge organizing and modeling technique, knowledge graph has become a key topic in graph research, but the practical application of KG is limited by its incompleteness. In recent years, many knowledge graph embedding(KGE) methods for knowledge graph completion(KGC) based on graph neural networks(GNN) have been proposed. However, most GNN-based KGC models are still suffer from the encoder-decoder structure of low efficiency in aggregating neighborhood information and the difficulty of model training. This paper present an optimized model that incorporates Neighborhood information into knowledge inference, to improve the performance of KGC models based on multilayer perceptron network(MLP), which is named N-MlpE. We generate an input sequence that includes the query triplet and its neighbor entities and relationships, and then feed it to an adaptive filter module to remove useless neighbors for the inference to improve the accuracy of the inference, and reduce the computational complexity of training the model. The filtered sequence is then fed into a weight calculation module and a feature extraction module simultaneously, the former is designed based on selfattention to model the relevant rule inference, which enhances the interpretability of KGE models, and the latter is based on MLP and used to capture the long-distance interactions between triplets, which can significantly improve the accuracy of inference. Extensive experiments are conducted on two standard KG datasets WN18RR and FB15k237 to verify the effectiveness of N-MlpE, the results show that the accuracy of N-MlpE model outperforms most GNN-based models. © 2023 IEEE.

关键词： distributed representation inference knowledge graph completion knowledge graph embedding link prediction

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Switching at flit level: A Congestion Efficient Flow Control Strategy for network-on-Chip 28

Switching at flit level: A Congestion Efficient Flow Control...

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28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Bose, Avik Ghosal, Prasun Indian Inst Engn Sci & Technol POB Garden Howrah 711103 WB India

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165820

The proposed work demonstrates a novel flow control mechanism that brings the flexibility to route every flit of a packet independently, alleviating the network congestion more efficiently in Virtual Channel (VC) based NoC routing. A 100% buffer utilization is achieved by putting an end to the overhead of VC arbitration. The modified router design comes with a 20% area and 4.73% energy saving on the generic one. Packet Latency improvement of 35 78%, 27 68%, and 45 62% is observed over the mesh, torus, and flattened butterfly topologies respectively under uniform random traffic. The average packet drop rate is reduced by a minimum of 28% across the above topologies.

关键词： Flit Switching Low Latency High throughput

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VSNT: Adaptive network Traffic Forecasting via VMD and Deep Learning with SCI-Block and Attention Mechanism 21

VSNT: Adaptive Network Traffic Forecasting via VMD and Deep ...

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21st IEEE International Symposium on parallel and distributed processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Dong, Pingping Zuo, Yuning Chen, Liying Li, Shangyu Zhang, Lianming Hunan Normal University College of Information Science and Engineering Changsha China

ISBN: (纸本)9798350329223

As the Internet evolves towards a dense network paradigm, advancements in mobile services have resulted in a consistent have consistently amplified the volume of mobile data. Consequently, precise network traffic forecasting has become critical for effective network planning and resource allocation. The Informer model, based on the Transformer architecture, has been optimized for time-series forecasting recently. However, the Informer tends to accentuate noise within individual time series, which leads to overfitting or decreased robustness, ultimately affecting forecasting performance. To address these challenges, an adaptive network traffic forecasting model named VSNT is introduced. VSNT employs Variational Mode Decomposition (VMD) to adaptively decompose original network traffic into Intrinsic Mode Function (IMF) components. For the preservation of the interrelation among IMF components, comprehensive representation learning is undertaken prior to their input into the neural network. This network utilizes an encoder-decoder structure and integrates tools such as the SCI-Block and attention mechanisms. The SCI-Block is instrumental in extracting multiscale network attributes, while the attention mechanism emphasizes pronounced features. Compared to the intricate Informer model, VSNT reduces the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) by approximately 51% and 35% respectively. This positions VSNT as a robust solution for accurate network traffic forecasting, providing a fruitful path for research in network traffic forecasting and time-series analysis. © 2023 IEEE.

关键词： Variational mode decomposition

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GNIDP: Gaussian-Noise-based Information Diffusion Prediction Model 29

GNIDP: Gaussian-Noise-based Information Diffusion Prediction...

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

作者： Wang, Yiran Zhang, Yin Wang, Ranran Zhang, Zhaokang Wan, Wenchao University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798350330717

Due to the significant Influence of social networks, information diffusion prediction, which aims to study the spread of messages among users, has become a crucial objective in various scenarios. Existing works have mostly attempted to integrate multiple source features such as social relationships and user preferences for prediction. Although it can improve the accuracy of predictions to some extent, there are still issues with complex models, low computational efficiency, and limited prediction performance. To address these challenges, this paper proposes a Gaussian-Noise-based Information Diffusion Prediction Model (GNIDP). As we know, we are the first to model the information diffusion in the social network as Gaussian noise diffusion, specifically, GNIDP utilizes Gaussian noise to simulate information diffusion patterns during information propagation, while capturing the evolving diffusion patterns over time across different time slots. Consequently, our model does not rely on social topology to achieve prediction, while improving the efficiency and accuracy of predictions. Experiments on two real social network datasets demonstrates that GNIDP achieves 13.54% relative gains over the best baseline on average. Therefore, the proposed model can be applied in scenarios where social topology is missing or difficult to obtain particularly in large-scale datasets requiring efficient diffusion prediction. © 2023 IEEE.

关键词： diffusion gaussian noise information diffusion prediction social network

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RSMCC: Enabling Ring-based Software Managed Cache-Coherent Embedded SoCs 28

RSMCC: Enabling Ring-based Software Managed Cache-Coherent E...

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28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Kornaros, Georgios Hellen Mediterranean Univ Elect & Comp Engn Dept Iraklion 71004 Crete Greece

ISBN: (纸本)9781728165820

Modern embedded heterogeneous Systems-on-Chip (SoCs) integrate differentiated memory hierarchy which require the programmer to undertake mostly simple, software-driven coherence protocols to optimize data-parallel applications. This paper presents a hybrid ring-based cache coherent multi-core architecture and a framework to develop memory hierarchy protocols customized to system constraints and workload needs. The ring protocol is developed in software over a typical mesh network-on-Chip (NoC) which is used to convey cache coherent messages. Management and system monitoring operations utilize the NoC hardware routing protocol to achieve speed and minimize software interference and overheads. The software cache coherent management protocol lays on top of a typical shared memory programming model and facilitates easy user-level control and integration with user application, while removing concerns for data allocation for scratchpad-based systems.

关键词： network-on-chip

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Decision Tree-based Rule Derivation for Intrusion Detection in Safety-Critical Automotive Systems

Decision Tree-Based Rule Derivation for Intrusion Detection ...

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euromicro conference on parallel, distributed and network-based processing

作者： Lucas Buschlinger Sanat Sarda Christoph Krauß Roland Rieke Fraunhofer SIT Germany Fraunhofer Singapore Darmstadt University of Applied Sciences Germany

Intrusion Detection Systems (IDSs) are being introduced into safety-critical systems such as connected vehicles. Since the behavior and effectiveness of measures are validated before approval, the decisions made by an IDS are required to be traceable and the IDS also needs to work efficiently on resource-constrained embedded systems. These requirements complicate the direct use of Machine Learning (ML) approaches in IDS design. In this paper, we propose an approach to using ML to generate rules for an efficient rule-based IDS like Snort. Our approach eases the time-consuming and difficult process of creating a rule set. We use decision trees to generate rules that can be used by experts as a basis for creating a rule set for a specific safety-critical use case. In addition, we use long short-term memory methods to circumvent the problem of limited training data availability, a common limitation in safety-critical systems. Our implementation and evaluation shows the feasibility of our approach to derive specific IDS rules for such systems.

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