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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

共 149974 条记录，以下是761-770 订阅

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Principles of Scaling Cloud computing of Digital Twins Using Accelerator Computers with Mass Multi-Core Parallelism 17

Principles of Scaling Cloud Computing of Digital Twins Using...

引用

17th international conference on Management of Large-Scale System Development, MLSD 2024

作者： Zatuliveter, Yury Fishchenko, Elena V.A. Trapeznikov Institute of Control Sciences of Ras Laboratory of Distributed Data-Analysis and Control Systems Moscow Russia

ISBN: (纸本)9798350375718

In relation to large industrially significant systems (using the example of nuclear power plants (NPP)), the ways of developing the architecture and functionality of cloud systems are considered. The development ways are aimed at providing full-featured cloud services based on industry standards for a system-holistic solution to the entire range of problems of using digital twin technologies with high computational complexity. The possibilities of increasing computing performance through the hybrid amplification of cloud servers by interfacing them with accelerator computers with mass, dynamically scalable multi-core parallelism (hundreds, thousands and more computing cores) are shown. The method of dynamic scaling of mass multi-core parallelism is proposed, intended to expand the space of virtualization of cloud resources. © 2024 ieee.

关键词： Nuclear power plants

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Decentralized Task Scheduling in Satellite Edge computing 9

Decentralized Task Scheduling in Satellite Edge Computing

引用

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

作者： Casalicchio, Emiliano Magliarisi, Danilo Sapienza Univ Rome Dept Comp Sci Rome Italy Blekinge Inst Technol Karlskrona Sweden

ISBN: (纸本)9798350366495;9798350366488

Satellite Edge computing has been recently introduced to deploy innovative computational services in space using Low Earth Orbit (LEO) satellite constellations as a distributed computational platform. Running a distributed computing platform in space introduces new challenges to traditional problems like computation offloading, task scheduling, mobility management, fault detection, and recovery. This research focuses on the problem of task scheduling, proposing a system model that accounts for the dynamics of the Satellite Edge computing environment and a formulation of the scheduling problem as an optimization problem that minimizes the average task response time under constraints on available resources and task completion deadlines. Then, we propose a decentralized algorithm that estimates the task response time and computes a scheduling solution in a fixed time, which depends only on the number of Inter Satellite Links a satellite has (typically four). Finally, we estimate and compare the overhead of the decentralized versus the decentralized solutions, showing the advantages of the proposed approach. Simulation experiments allow us to compare the performance of the decentralized approach with the performance of baseline decentralized and centralized solutions. Results show that, in all scenarios considered, the proposed decentralized algorithm performs better than the baseline centralized and decentralized solutions and is more scalable and highly available.

关键词： Satellite Cloud computing LEO Edge computing Decentralized Scheduling Performance evaluation Simulation

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SimuScale: Optimizing Parameters for Autoscaling of Serverless Edge Functions through Co-Simulation 17

SimuScale: Optimizing Parameters for Autoscaling of Serverle...

引用

ieee 17th international conference on Cloud computing (ieee CLOUD)

作者： Raith, Philipp Nastic, Stefan Dustdar, Schahram TU Wien Distributed Syst Grp Vienna Austria

ISBN: (纸本)9798350368543;9798350368536

Serverless Edge computing is growing in popularity, and while commercial providers are starting to offer edgeoriented products, much research is still being done on orchestrating functions (e.g., autoscaling). These approaches range from threshold- to AI-based strategies and support various Service Level Objectives (SLOs), such as Round-Trip-Time (RTT) and resource usage. However, the Quality of Service (QoS) continuously deteriorates due to the dynamic edge-cloud continuum and static parameterization of orchestration strategy parameters. Platforms must adapt the orchestration parameters during runtime to counteract this drift that causes SLO violations. To this end, we introduce the Orchestration Parameter Optimization Problem (OPOP), which aims to find parameters for orchestration strategies to minimize SLO violations. We propose a novel selfadaptive Simulation-based Scaling (SimuScale) approach that uses co-simulation to solve OPOP for autoscalers during runtime. SimuScale uses live monitoring data to feed the simulation and perform parameter optimization. Our Proof of Concept is integrated with Kubernetes and evaluated on a real-world edge-cloud testbed. While this work focuses on a threshold-based autoscaler, it can be extended to optimize other orchestration components (e.g., schedulers). Our experimental results show that SimuScale finds parameters that decrease RTT SLO violations between 15% and 40%. SimuScale also can reduce resource usage by 29.87% while maintaining the target 95th RTT percentile. Moreover, it can reduce variance caused by different request patterns, making orchestration strategies more resilient in realistic scenarios.

关键词： Serverless Edge computing Co-Simulation Self-Adaptive System Autoscaler Edge-Cloud Continuum

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GreenLight: Green Traffic Signal Control using Attention-based Reinforcement Learning on Fog computing Network 15

GreenLight: Green Traffic Signal Control using Attention-bas...

引用

15th international Green and Sustainable computing conference

作者： Tang, Chengyu Baskiyar, Sanjeev Auburn Univ Dept Comp Sci & Software Engn Auburn AL 36849 USA

ISBN: (纸本)9798331507879;9798331507862

Traffic congestion is a pervasive problem causing severe environmental and economic issues. In recent years, traffic signal control using reinforcement learning (RL) has come a long way. Most existing studies focus on using distributed agents with data exchange among neighbors, which, however, increases network complexity and usage and still suffers from the lack of broader coordination. Meanwhile, the attention mechanism has achieved tremendous success, and advances in vehicle-to-infrastructure (V2I) communications have enabled real-time collections of granular data. However, integrating these technologies into traffic signal control remains under-explored. Therefore, we present GreenLight, a forward-thinking and eco-friendly traffic signal control framework that can be applied to V2I-equipped fog computing environments. For a large urban area, traffic signals are divided into clusters, each coordinated by a fog node with an RL agent. Intra-cluster indexed self-attention is applied to extract context-aware features that the fog-residing RL agent utilizes to determine the proper signal control command. Results of simulation experiments using both synthetic and real-world scenarios show that the presented framework yields lower waiting time, emissions, and fuel consumption compared to baseline methods, indicating its potential for next-generation transportation systems.

关键词： Traffic Signal Control Reinforcement Learning Self-Attention Emissions Fuel Consumption Fog computing V2I

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Enhancing Simulation Efficiency in Semiconductor Optical Amplifier (SOA) Models: A Comparative Study of First and Second Order Approaches 24

Enhancing Simulation Efficiency in Semiconductor Optical Amp...

引用

24th ieee international conference on Communication Technology, ICCT 2024

作者： Qi, Yingchuan Zhao, Wenbo Dong, Jincheng Wang, Shuo Ma, Shuanger Zhang, Qian Zhang, Lu Shandong Zhike Intelligence Computing Inc. Jinan China Shandong University Jinan China Zhejiang University Hangzhou China

ISBN: (纸本)9798350363760

This study evaluates the numerical performance of first and second-order nonlinear dynamic semiconductor optical amplifier (SOA) models, including the distributed traveling-wave (TW) and lumped models. We find that while the first-order method is sufficient for the lumped SOA model, the second-order method significantly improves simulation efficiency for the TW-SOA model. These insights enhance the optimization of SOA applications in various advanced optical systems. © 2024 ieee.

关键词： Optical transmitters

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Network Economics -enabled Edge computing in UAV-assisted Public Safety systems 18

Network Economics -enabled Edge Computing in UAV-assisted Pu...

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18th Annual international conference on distributed computing in Sensor systems (DCOSS)

作者： Hossain, Md Sahabul Sangoleye, Fisayo Poudyal, Oshan Tsiropoulou, Eirini Eleni Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA

ISBN: (纸本)9781665495127

In this paper, the joint problem of agents, e.g., police officers, firefighters, etc., to Unmanned Aerial Vehicles (UAVs) association and optimal partial task offloading is addressed based on the principles of reinforcement learning and contract theory, respectively, in public safety scenarios. A two -layers approach is followed. At the first layer, the agents act as learning automata in order to learn their most beneficial UAV selection to optimize their long-term reward in terms of processing their offloaded data, while respecting their delay constraints and tolerance stemming from their requested computing service and the public safety scenario that they serve. At the second layer, a contracttheoretic model is proposed to determine the agents' optimal amount of offloaded data to the selected UAV and the UAV's optimal portion of allocated computing capacity to each agent's computing tasks, while considering the urgency of the agents' requested service. A detailed set of numerical and comparative simulation results demonstrates the drawbacks and benefits of the proposed framework under rea-life public safety scenarios.

关键词： Terms Reinforcement Learning Contract Theory Public Safety Delay Tolerance

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Spectral Clustering Based Resource Allocation Strategy for distributed Large-scale Network Emulation 25

Spectral Clustering Based Resource Allocation Strategy for D...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data Science and systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Jiyan Gu, Rentao Bai, Lin Sch. of Information and Communication Engineering Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350330014

Nowadays, networks are increasingly reliant on software frameworks and virtualization. To obtain relevant pre-dictions of the behavior of their protocols, network emulation tools play a crucial role. However, as networks grow in size and complexity, the emulation task demands more physical resources such as CPU and memory. Consequently, a single physical machine is no longer sufficient to handle large-scale network emulation. Instead, distributed network emulation tools must be employed for resource-intensive network emulation tasks. In resource-intensive distributed network emulation, the physical resource allocation is NP-hard to perform appropriately. In this paper, we propose SCBG (spectral clustering based greedy node placement algorithm) as a means to enhance the bandwidth utilization between physical nodes in distributed emulation. SCBG aims to address the communication bottleneck in distributed network simulation, surpassing the algorithm proposed by Distrinet, while also improving the algorithm's execution speed. Additionally, SCBG incorporates optimization for time dilation when physical bandwidth resources are severely insufficient. This optimization leads to a reduction in the additional time required for network simulation after applying time dilation. Experimental results demonstrate that SCBG surpasses the algorithm in Distrinet across a diverse range of intricate large-scale networks. © 2023 ieee.

关键词： Optimization

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Towards Intelligent Data Protocols for the Edge 7

Towards Intelligent Data Protocols for the Edge

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7th ieee international conference on Edge computing and Communications (ieee EDGE) / ieee World Congress on Services (SERVICES)

作者： Donta, Praveen Kumar Dustdar, Schahram TU Wien Distributed Syst Grp A-1040 Vienna Austria

ISBN: (纸本)9798350304831

The computing continuum is growing because multiple devices are added daily. Edge devices play a key role in this because computation is decentralized or distributed. Edge computing is advanced by using AI/ML algorithms to become more intelligent. Besides, Edge data protocols are useful for transmitting or receiving data between devices. Since, computation efficiency is possible when the data is received at the Edge timely, and it is possible only when the data protocols are efficient, reliable and fast. Most edge data protocols are defined with static set of rules and their primary purpose is to provide standardized and reliable data communications. Edge devices need autonomous or dynamic protocols that enable interoperability, autonomous decision making, scalability, and adaptability. This paper examines the limitations of popular data protocols used in edge networks, the need for intelligent data protocols, and their implications. We also explore possible ways to simplify learning for edge devices and discuss how intelligent data protocols can mitigate challenges such as congestion, message filtering, message expiration, prioritization, and resource handling.

关键词： Data protocols Intelligent protocols Edge computing computing Continuum Edge Intelligence Autonomous decision-making

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Hybridized Shield: A Framework for Backdoor Detection in Secure Federated Learning systems 7

Hybridized Shield: A Framework for Backdoor Detection in Sec...

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7th ieee international conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Rahman, Gul Saeed-Uz-Zaman Li, Bin Muzamal, Junaid Hussain Yangzhou University School of Information Engineering Jiangsu China National University of Computer and Emerging Sciences Fast School of Computing Lahore Pakistan

ISBN: (纸本)9798350352009

In the context of Federated Learning, the vulnerability to backdoor attacks poses a significant threat to the integrity and reliability of distributed learning systems. This research introduces a novel defense framework, the' Hybridized Shield,' designed to safeguard Federated Learning environments robustly against such insidious threats. The proposed mechanism integrates Oblivious Random Grouping, Partial Parameter Disclosure, and differential outlier analysis, forming a comprehensive defense strategy that addresses the unique challenges of Federated Learning's decentralized and collaborative nature. The results reveal a significant improvement in the resilience of Federated Learning systems against backdoor attacks. Specifically, the proposed method maintains high accuracy on clean data (97.92% on MNIST and CIFAR-10) while significantly reducing the effectiveness of poisoned data attacks to a mere 10.11% accuracy, compared to the baseline Federated Learning model without defense mechanisms, which showed a 100% accuracy on poisoned data and an 18.56% attack success rate. These findings underscore the efficacy of the' Hybridized Shield' in preserving the integrity and security of Federated Learning systems, thereby contributing to the advancement of secure and reliable distributed machine learning methodologies. © 2024 ieee.

关键词： Federated learning

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A Moving Target Defense Approach for the distributed Dynamic Network 21

A Moving Target Defense Approach for the Distributed Dynamic...

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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： Zhang, Lin Guo, Yunchuan Leng, Siyuan Li, Zifu Li, Fenghua Fang, Liang Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350329223

The distributed dynamic network is vulnerable to scanning attacks due to the openness of wireless channels. Traditional defense systems tend to be passive and exhibit delayed responses. A moving target defense approach, namely distributed Network Address Shuffling (DNAS), is proposed to thwart attackers' network scanning through the shuffling of network addresses. To resolve address conflicts resulting from this shuffling, DNAS employs a dynamic diffusion method of allocated addresses before the shuffling process to reduce the probability of conflict generation, and utilizes a passive detection based conflict elimination algorithm after the shuffling process to eliminate any generated conflicts. To select low-risk addresses, DNAS leverages an artificial feature selection based Fully Connected Neural Network (FCNN) to recognize the attacker's scanning policy, and identifies low-risk addresses based on the scanning range of the policy. Empirical experiments and theoretical analysis indicate that DNAS significantly reduces the probability of address conflict generation at a minimal cost. It effectively eliminates all generated address conflicts within an average conflict resolution time of less than 500ms. Furthermore, DNAS exhibits an accuracy of 99.45% in recognizing scanning policies, surpassing pseudorandom address hopping in diminishing the success rate of sequential, local random, and mixed scanning. © 2023 ieee.

关键词： Network security

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