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Optimization of computing Power Network Routing Strategies based on Multi-Agent Soft Actor-Critic 9

Optimization of Computing Power Network Routing Strategies b...

引用

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

作者： Chen, Zhongyue Zhang, Shicong Tang, Ying Xu, Mengke Wu, Xiaoqing Ye, Minchao Zhang, Zhaojuan Qi, Yaping Lu, Huijuan Huo, Wanli China Jiliang University Zhejiang Hangzhou China Novel Internet Exchange Zhejiang Hangzhou China Division of Ionizing Radiation Metrology Zhejiang Hangzhou China

ISBN: (纸本)9798350376548

Despite rapid developments in cloud computing, big data, and artificial intelligence, the computing Power Network, as a new type of information infrastructure supporting the digital transformation and upgrading of various industries, faces significant challenges in efficiently scheduling, and managing computing and network resources. this paper introduces an optimization method for the computingRoute algorithm based on the Multi-Agent Soft Actor-Critic algorithm, operating within a Software-Defined networking (SDN) architecture to address the efficiency issues in data transmission within the computing power network. the method leverages collaboration among multiple intelligent agents and employs deep reinforcement learning to optimize routing paths. Experimental results indicate that the MASAC algorithm, compared to traditional Actor-Critic algorithms, shows a 43.63 % improvement in stable average reward values, demonstrating better learning efficiency and performance. Furthermore, the soft policy updates, entropy regularization, and the use of target networks in the MASAC algorithm enhance its stability, improving training efficiency and performance. In performance comparisons with other computing power network routing strategies, the proposed computingRoute solution minimizes computing power requirements while reducing latency by 39.06% compared to DRL-TE and 21.42% compared to SmartPath. However, computingRoute exhibits a slightly higher packet loss rate than SmartPath. © 2024 IEEE.

关键词： computing power

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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...

引用

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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Zero-Touch MEC Resources for Connected Autonomous Vehicles Managed by Federated Learning 9

Zero-Touch MEC Resources for Connected Autonomous Vehicles M...

引用

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

作者： Ruiz de Mendoza, Carlos Cervello-Pastor, Cristina Univ Politecn Cataluna Dept Network Engn Barcelona Spain

ISBN: (纸本)9798350399806

this paper presents a Ph.D. thesis proposal for a novel solution in optimizing the placement of Connected Autonomous Vehicles (CAVs) Virtual Network Functions (VNFs) requests in Edge computing (EC) resources. Our Federated Deep Reinforcement Learning (FDRL) proposal will be designed to improve computation efficiency while minimizing service rejections and maximizing resource utilization, and ensuring the least costly path for CAVs. this approach will also be privacy-preserving, ensuring sensitive data remains secure and enables reliable, low-latency communication between CAVs, EC nodes, and the federated server. By utilizing distributed learning capabilities, FDRL allows multiple vehicles to learn from their local experience and make collective decisions, improving network systems performance.

关键词： B5G 6G Virtual Network Function Multi-access Edge computing A2C PPO DQN Federated Learning Deep Reinforcement Learning

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A Policy Conflict Detection Mechanism for Intent-based networking 9

A Policy Conflict Detection Mechanism for Intent-based Netwo...

引用

9th IEEE international conference on Cloud computing and Intelligence Systems, CCIS 2023

作者： Li, Bo Deng, Xiaozhi Zhang, Peiming Power Dispatching Control Center Guangdong Power Grid Co Ltd Guangzhou China

ISBN: (纸本)9798350304428

As a new network paradigm, Intent-Based networking (IBN) can complete network automated policy configuration according to user intent without human involvement, and adapt to network business dynamic needs and the complexity of network function deployment. In IBN, the policy conflict mainly performs linear detection in units of flow rules sent to the switch. If a conflict is detected, the flow rule will be withdrawn, otherwise it will be issued. the realization of an intent requires multiple flow rules to work together. If a single flow rule is used as the detection unit, some flow rules may be issued without the network intent taking effect. In addition, most of the existing methods detect conflict in real time without considering time constraints. However, network administrators may issue intentions to take effect delayed or periodically, so time constraints need to be considered during detection. For IBN, this paper designed three logical expressions to analyze the relationship between policies, combined with the action field to determine the type of policy conflict. Finally, whether the intention is conflicting can be determined. this method realizes the conflict detection in units of network intentions, solves the problem that the existing methods do not consider time constraints, and can effectively detect delayed intentions and periodic intentions. Experiments show that this method can effectively detect hundreds of pairs of network policies within 1s without excessive CPU consumption. © 2023 IEEE.

关键词： Software defined networking

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GEWO: An Efficient Prioritised Task Scheduling in Cloud Fog computing Environment 9

GEWO: An Efficient Prioritised Task Scheduling in Cloud Fog ...

引用

9th international conference on Wireless Communications, Signal Processing and networking, WiSPNET 2024

作者： Medishetti, Santhosh Kumar Swapna, G. Anusha, K. Karri, Ganesh Reddy Kuppusamy, P. VIT-AP University Amaravathi India Anurag University Hyderabad India Marri Laxman Reddy Institute of Technology India

ISBN: (纸本)9798350350845

the rapid expansion of Cloud-Fog computing (CFC) underscores the need for effective task scheduling (TS) strategies to optimize resource utilization and bolster system performance. this paper introduces a novel optimization algorithm, Golden Eagle Whale Optimization (GEWO), which combines the unique behaviors of Golden Eagles and Whales by integrating Golden Eagle Optimization (GEO) and Whale Optimization Algorithm (WOA). GEWO strives to achieve a balance between global exploration, emulating the soaring capabilities of Golden Eagles, and local exploitation, inspired by the strategic diving behaviors of Whales. Specifically designed to tackle the challenges posed by the dynamic and heterogeneous nature of cloud and fog computing environments, GEWO takes into account factors such as task characteristics, resource availability, and network conditions. By incorporating these elements into the optimization process, GEWO enhances convergence speed and solution quality, presenting a promising solution for efficient TS in CFC. To evaluate GEWO's efficacy, comprehensive experiments were conducted using diverse benchmark tasks and real-world CFC scenarios. Comparative analyses against state-of-the-art optimization algorithms reveal the superior performance of GEWO, showcasing a 26% reduction in task completion time, a 32 % improvement in resource utilization, and a 29% increase in energy efficiency. GEWO contributes to ongoing efforts aimed at enhancing the efficiency and scalability of cloud-fog systems, marking advancements in resource management and overall system performance. © 2024 IEEE.

关键词： Fog computing

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2023 IEEE 9th international conference on Network Softwarization: Boosting Future Networks through Advanced Softwarization, NetSoft 2023 - Proceedings

2023 IEEE 9th International Conference on Network Softwariza...

引用

9th IEEE international conference on Network Softwarization, NetSoft 2023

ISBN: (纸本)9798350399806

the proceedings contain 92 papers. the topics discussed include: link quality estimation in wireless software defined network with a reliable control plane;energy optimization of distributed video processing system using genetic algorithm with Bayesian attractor model;deep reinforcement learning edge workload orchestrator for vehicular edge computing;a multi-hop-aware user to edge-server association game;providing fine-grained network metrics for monitoring applications using in-band telemetry;MANTRA: an edge-computing framework based on multi-armed bandit for latency- and energy-aware job offloading in vehicular networks;domain adaptation of reinforcement learning agents based on network service proximity;in-situ proof-of-transit for path-aware programmable networks;and adaptive services function chain orchestration for digital health twin use cases: heuristic-boosted Q-learning approach.

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Preserving Performance and Reducing Energy Consumption: A Two-Phase Scheduling Approach in Cloud computing Environments 9

Preserving Performance and Reducing Energy Consumption: A Tw...

引用

Joint 9th international conference on Digital Arts, Media and Technology with 7th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： thanavanich, thanawut Kantawong, Krittika Yaibuates, Mayoon Duangtang, Pumipong Siri, Atikhom Sriwanna, Kittakorn Chiang Rai Rajabhat University Faculty of Digital Technology Department of Computer Engineering Chiang Rai Thailand University of Phayao School of Information and Communication Technology Department of Digital Business Phayao Thailand

ISBN: (纸本)9798350318241

Cloud computing has evolved significantly, transitioning from traditional on-demand architectures to decentralized, multi-provider models. this shift is pivotal for enhancing connectivity in the Internet of things and data-intensive computing. While accessing shared online resources, effective task scheduling is crucial for optimal service performance. However, the challenge persists in balancing resource optimization with potential issues like energy wastage or service performance degradation. this study proposes a two-phase scheduling approach in distributed computing, addressing the balance between performance preservation and energy reduction. the method seamlessly integrates with existing algorithms, employing a prescheduling phase followed by a model to estimate processors, minimizing underutilization. Experimental results highlight its contribution to energy-efficient scheduling, emphasizing a nuanced balance between performance and energy consumption. the approach employs a mathematical model to determine suitable processors, providing versatile options for computational resource providers, aiming for energy-conscious and performance-oriented solutions. © 2024 IEEE.

关键词： Cloud computing

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Communication Analysis for Multidimensional Parallel Training of Large-scale DNN Models 25

Communication Analysis for Multidimensional Parallel Trainin...

引用

作者： Lai, Zhiquan Hao, Yanqi Li, Shengwei Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798350330014

Multidimensional parallel training has been widely applied to train large-scale deep learning models like GPT-3. the efficiency of parameter communication among training devices/processes is often the performance bottleneck of large model training. Analysis of parameter communication mode and traffic has important reference significance for the research of interconnection network design and computing task scheduling to improve the training performance. In this paper, we analyze the parametric communication modes in typical 3D parallel training (data parallelism, pipeline parallelism, and tensor parallelism), and model the traffic in different communication modes. Finally, taking GPT-3 as an example, we present the communication in its 3D parallel training. © 2023 IEEE.

关键词： Deep neural networks

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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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Learning at the Edge: Mobile Edge computing and Reinforcement Learning for Enhanced Web Application Performance 9

Learning at the Edge: Mobile Edge Computing and Reinforcemen...

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9th international conference on Web Research, ICWR 2023

作者： Moghaddasi, Komeil Rajabi, Shakiba Urmia Branch Islamic Azad University Department of IT and Computer Engineering Urmia Iran

ISBN: (纸本)9798350399691

Over the recent years, the adoption of Mobile Edge computing (MEC) has increased due to its ability to bring computing resources closer to end-users, which includes storage, computing, and networking at the network's edge. this approach results in faster and more efficient data processing, reduced latency, and better overall performance for mobile device applications. Our aim in this study is to evaluate the effectiveness of using reinforcement learning algorithms, namely Deep Q-Network (DQN) and Asynchronous Advantage Actor-Critic (A3C), in optimizing the performance of web applications in MEC environments, such as latency, CPU usage, and memory utilization. We conducted experiments using a sample dataset and compared the performance of models with and without MEC. the results demonstrate that the use of MEC substantially improves the performance of web applications. Both DQN and A3C algorithms exhibit promising results in improving the latency of web applications in MEC environments. However, the A3C algorithm outperforms the DQN algorithm in terms of CPU utilization and memory usage. Overall, our study highlights the potential of reinforcement learning algorithms in improving the performance of MEC-based web applications. © 2023 IEEE.

关键词： Reinforcement learning

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