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MANTRA: an Edge-computing Framework based on Multi-Armed Bandit for Latency- and Energy-aware Job Offloading in Vehicular Networks 9

MANTRA: an Edge-Computing Framework based on Multi-Armed Ban...

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9th IEEE international conference on Network Softwarization (IEEE NetSoft) - Boosting Future Networks through Advanced Softwarization

作者： Busacca, Fabio Palazzo, Sergio Raftopoulos, Raoul Schembra, Giovanni Univ Catania CNIT Res Unit Catania Italy

ISBN: (纸本)9798350399806

the optimization of job offloading procedures in modern vehicular networks is a problem of utmost importance. In this regard, this paper proposes MANTRA, a distributed framework based on multi-player multi-armed bandit (MPMAB) algorithms for latency- and energy-aware job offloading in vehicular networks. the main goal of MANTRA is to support procedures of job offloading in green vehicular networks to achieve a target tradeoff between energy consumption and job processing latency. In particular, MANTRA is intended to run on so-called MEC-in-a-box (M-Box) devices, portable batterypowered Road Side Units (RSUs) specifically designed to work without mobile connectivity and of a fixed power grid. To demonstrate MANTRA effectiveness, we model the vehicular network using the queueing theory for M/M/m/K systems. We run an extensive evaluation campaign and compare MANTRA with several baselines, including a centralized, oracle-based approach. In such a way, we demonstrate how MANTRA outperforms the baselines and quickly converges to the performance of the centralized approach in a fully-distributed way in terms of job processing latency and network outage probability.

关键词： Edge computing Vehicular Networks Multiarmed Bandit Job Offloading

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Incident-Aware distributed Signal systems in Self-Organised Traffic Control systems 9

Incident-Aware Distributed Signal Systems in Self-Organised ...

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9th international conference on Vehicle Technology and Intelligent Transport systems (VEHITS)

作者： Tomforde, Sven Ohl, Yanneck thomsen, Ingo Univ Kiel Intelligent Syst D-24118 Kiel Germany

ISBN: (纸本)9789897586521

Traffic congestion is a major contributor to carbon dioxide emissions and causes air pollution which poses various health risks. In response to such challenges, traffic management systems are becoming increasingly intelligent and adaptive. Particularly self-organised approaches such as the Organic Traffic Control (OTC) system offer additional advantages such as efficiency, scalability, and robustness. In addition to the local and traffic-dependent switching of traffic signals, a central task of such a system is the coordinated adaptation of traffic lights by means of Progressive Signal systems. In this paper, we present a novel approach for establishing decentralised PSSs that takes into account recognised incidents and thus proactively ensures optimised traffic flows. We develop three different strategies and evaluate them using realistic simulations.

关键词： Traffic Management Organic Traffic Control Progressive Signal systems Green Waves Incident Detection Self-Organisation

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distributed Collaborative Control of Reconfigurable Battery Energy Storage Based on Consensus Algorithms 9

Distributed Collaborative Control of Reconfigurable Battery ...

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9th international conference on Power and Renewable Energy

作者： Wang, Manshang Xu, Yiran Guo, Jinkun Jiang, Libo Zheng, Tianwen Zhenjiang Power Supply Co State Grid Jiangsu Elect Power Co Ltd Zhenjiang Jiangsu Peoples R China Tsinghua Univ Sichuan Energy Internet Res Inst Chengdu Sichuan Peoples R China

ISBN: (纸本)9798350377477;9798350377460

To enhance the high-reliability operation capability of reconfigurable battery energy storage systems, a distributed cooperative control method of reconfigurable battery energy storage based on consensus algorithms is proposed in this paper, which can improve the frequency and power characteristics of the system. Firstly, the mathematical model of virtual synchronous generator(VSG) control, the structure of reconfigurable battery energy storage system, and the parallel VSG distributed architecture are introduced;Secondly, combined with the mathematical model of VSG control, the overall scheme of frequency recovery and power output reasonable distribution of VSG parallel system is designed;then, according to the structural characteristics of reconfigurable battery energy storage, a battery pack reconfiguration strategy is proposed, which can meet the power output demand through the free combination of battery packs;Finally, a simulation model is built in Matlab/Simulink environment, and the simulation results verify the correctness and effectiveness of the proposed method.

关键词： Reconfigurable battery energy storage Consensus algorithms Collaborative control Frequency recovery Power distribution

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Innovations in mathematical modeling, AI, and optimization techniques

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JOURNAL OF SUPERcomputing 2025年第1期81卷 1-4页

作者： Ohue, Masahito Yasuo, Nobuaki Takata, Masami Inst Sci Tokyo Sch Comp Dept Comp Sci Yokohama Kanagawa 2268501 Japan Inst Sci Tokyo Acad Convergence Mat & Informat TAC MI Tokyo 1528550 Japan Nara Womens Univ Res Grp Informat & Commun Technol Life Nara 6308506 Japan

this special issue is dedicated to examining the rapidly evolving fields of artificial intelligence, mathematical modeling, and optimization, with particular emphasis on their growing importance in computational science. It features the most notable papers from the "Mathematical Modeling and Problem Solving" workshop at PDPTA'24, the 30th international conference on Parallel and distributed Processing Techniques and Applications. the issue showcases pioneering research in areas such as natural language processing, system optimization, and high-performance computing. the nine selected studies include novel AI-driven methods for chemical compound generation, historical text recognition, and music recommendation, along with advancements in hardware optimization through reconfigurable accelerators and vector register sharing. Additionally, evolutionary and hyper-heuristic algorithms are explored for sophisticated problem-solving in engineering design, and innovative techniques are introduced for high-speed numerical methods in large-scale systems. Collectively, these contributions demonstrate the significance of AI, supercomputing, and advanced algorithms in driving the next generation of scientific discovery.

关键词： Mathematical modeling Artificial intelligence Parallel and distributed computing Reconfigurable computing Drug discovery

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Methods for the reliability optimization of district-distributed heating systems with prosumers

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ENERGY REPORTS 2023年 9卷 584-593页

作者： Postnikov, Ivan Russian Acad Sci ESI SB RAS Melentiev Energy Syst Inst Siberian Branch Lermontov St 130 Irkutsk 664033 Russia

District-distributed heating systems are considered as the result of the development of existing district heating systems, in which various energy technologies are integrated to achieve maximum efficiency of the heating to consumers. the distributed sector in these systems is associated, first of all, with the implementation of prosumers. the problem statement is determining such values of reliability parameters of components (in particular, failure rates) of studied system that provide the required level of reliability of heating to consumers at the minimum cost for ensuring this level of reliability taking into account the redundancy of prosumers. A methodology is based on the nodal probability of failure-free operation index, the Markov random process, the laws of heat transfer processes in heat supply, and some other methods. A case example is considered, an analysis of the results obtained is carried out, and directions for further research are formulated. (c) 2022 the Author(s). Published by Elsevier Ltd. this is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： District heating systems Prosumer Reliability optimization Failure-free operation probability System-wide reliability parameter Markov random process thermal inertia

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Haywire - A System for Visualizing and Analyzing Streaming IoT Data 9

Haywire - A System for Visualizing and Analyzing Streaming I...

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9th IEEE international conference on Fog and Mobile Edge computing (FMEC)

作者： Taivalsaari, Antero Nokia Technol Espoo Finland Tampere Univ Tampere Finland

ISBN: (纸本)9798350366495;9798350366488

In recent years there has been a notable trend towards streaming data systems. the Internet of things has transformed the Internet into a massive collection of live data streams. these streams range from slowly trickling sensor data from, e.g., residential water or gas meters to very bandwidth-hungry high-definition 360 degree live video streams. In a complex IoT system with thousands of data streams, one of the challenges for system developers and maintainers is the ability to ensure that data in all streams is flowing normally. In this paper we present a system for visualizing streaming data in a quick "at-a-glance" fashion. the Haywire system constructs and dynamically maintains a live 3D graph representation of all streams, and presents the streams in a graphical Web UI with force-directed layout. the user interface includes detailed message statistics and the ability to quickly dive deeper into individual streams if any anomalies are detected in their behavior.

关键词： Internet of things IoT Programmable World streaming data stream processing message brokers fog computing edge computing data visualization

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Edge computing-Enabled Lightweight Deep Neural Network for Real-Time Video Surveillance in Maritime Cyber-Physical systems 9

Edge Computing-Enabled Lightweight Deep Neural Network for R...

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9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Luo, Caofei Wu, Fumin Xiong, Renhao Xu, Guanhua Liu, Wen Maritime Electronics Research Institute Co. Ltd. Ningbo315040 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310058 China School of Navigation Wuhan University of Technology Wuhan430063 China

ISBN: (纸本)9798350376548

With the rapid developments of low-end IoT devices and artificial intelligence (AI), the heterogeneous and dynamic data has been increasing drastically, especially in the era of AI-enabled maritime cyber-physical systems (CPS). It is intractable to provide the efficient and flexible computer vision applications through traditional cloud-centric data processing strategies, which commonly suffer from the limitations, e.g., high cost, high- latency communication, and high occupied storage, etc. To meet these challenges, edge computing has meaningfully evolved into a flexible and powerful distributed architecture. It contributes to improvements in bandwidth savings and response time in practical applications. To guarantee efficient video surveillance in AI-powered maritime CPS, the edge computing-based lightweight deep network (termed ShipNet-YOLOv3), incorporated with Kalman filtering and Hungarian algorithm, is proposed to implement real-time detection and tracking of moving ships. In particular, ShipNet-YOLOv3 adopts Darknet-53 as the backbone network (i.e., feature extractor) with three prediction boxes. the channel pruning and fine-tuning strategy are further introduced to balance the trade-off between computational cost, network size, and detection (or tracking) accuracy. Experimental results on both synthetic and realistic scenarios have illustrated that the proposed method could provide superior ship detection and tracking results under different adverse imaging conditions. It thus has the capacity of promoting real-time video surveillance in AI -enabled maritime CPS. © 2024 IEEE.

关键词： Kalman filters

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Research on Unknown distributed Denial-of-Service Attacks Intrusion Detection Based on Deep Learning 9

Research on Unknown Distributed Denial-of-Service Attacks In...

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9th international conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Zhang, Liumei An, Zhaoyang Hu, Yuanjiao Xi'an Shiyou University School of Computer Science Xi'an China

ISBN: (纸本)9798350376548

the increasing prevalence of distributed Denial-of- Service (DDoS) attacks poses a serious threat to network security, emphasizing the crucial need for effective intrusion detection methods. the continuous evolution of attack strategies has exposed the limitations of traditional feature engineering and statistical analysis in detection mechanisms. To address this challenge, this study proposes an innovative DDoS attack detection framework based on deep learning. At the core of this framework lies a Temporal Convolutional Network (TCN) structure, cleverly integrated with an attention mechanism to enhance the model's ability to identify and extract crucial information. this integration not only aids in capturing long- term dependencies and global patterns in time-series data but also significantly improves detection accuracy and system robustness. To validate the effectiveness of the proposed method, comprehensive experimental evaluations were conducted using the CICDDoS2019 dataset. the results demonstrate a notable achievement, with our model achieving a high classification accuracy of 98.14%. In comparison to traditional machine learning methods, the model exhibits significantly reduced false positive and false negative rates. © 2024 IEEE.

关键词： Network intrusion

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A distributed State Management Approach for FaaS Platforms 9

A Distributed State Management Approach for FaaS Platforms

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9th international conference on Computer and Communication systems (ICCCS)

作者： Xu, Yang Yu, Jiazhao Guo, Hairui Li, Jialun Wu, Weigang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9798350350227;9798350350210

Function-as-a-Service (FaaS) has become increasingly popular due to its ease-of-use operations, scalability, and low management overhead. However, the stateless nature makes it inappropriate for deploying applications that require maintaining state data across series execution. this motivates state management for FaaS platforms, which is currently satisfied by integrating additional storage services or a central coordinator. In this paper, we propose a new approach for FaaS platforms to manage function states in a distributed way. Specifically, we utilize function-level checkpoint to maintain function state and enable state to scale with the number of function instances. To synchronize state among multiple instances of the same function, we employ a synchronization mechanism supported by consensus protocols. Compared to existing works, our new approach distributes the burden of state management across all nodes and does not rely on additional services. In evaluation, we compare our approach to baseline system with a micro-benchmark and three representative applications. the results show that our approach can keep strong consistency of state between functions and reduce function execution time significantly.

关键词： Function as a Service Stateful Application State Management Synchronization

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Analysis of the Surface Water Quality in the State of Karnataka using distributed Platform 9

Analysis of the Surface Water Quality in the State of Karnat...

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9th international conference on Advanced computing and Communication systems, ICACCS 2023

作者： Dasu, Shravya Shobha, G. Shetty, Jyothi RV College of Engineering Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350397376

Water, one of the essential substances for life on earth is crucial for humans, plants and animals. Regular testing and monitoring of water quality is essential to ensure its safety for consumption. this process includes measuring physical and chemical parameters such as PH, total dissolved solids (TDS) etc. Herein, surface water quality in Karnataka, India was analyzed over 40 years. In order to analyze the large amount of data collected, high performance computing cluster (HPCC) systems was used. Biochemical oxygen demand (BOD) predictive model was built using linear regression. Additionally, a potability classification model was built using the random forest classifier algorithm. these algorithms are present in the LinearRegression and LearningTrees bundle provided by HPCC systems. Finally, a graphical visualization of water quality in four basins over time was performed with the Visualizer bundle. this was done to understand the improvement or degradation of water quality. the analysis was performed on a single node and an 8-node cluster respectively. In comparison with the former, the latter had a shorter execution time. © 2023 IEEE.

关键词： Water quality

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