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標(biāo)題: Titlebook: Bringing Machine Learning to Software-Defined Networks; Zehua Guo Book 2022 The Author(s), under exclusive license to Springer Nature Sing [打印本頁(yè)]

作者: 涌出 時(shí)間: 2025-3-21 17:06
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作者: 分期付款 時(shí)間: 2025-3-21 21:28

作者: 步履蹣跚 時(shí)間: 2025-3-22 02:38
Deep Reinforcement Learning-Based Flow Scheduling for Power-Efficient Data Center Networks,ally collects traffic distribution from switches to train its DRL model. The well-trained DRL agent of SmartFCT can quickly analyze the complicated traffic characteristics and adaptively generate an action for scheduling flows and deliberately configuring margins for different links. Following the g

作者: cutlery 時(shí)間: 2025-3-22 08:22

作者: 游行 時(shí)間: 2025-3-22 10:18
2191-5768 earning.Includes case studies that illustrate how to use macEmerging machine learning techniques bring new opportunities to flexible network control and management. This book focuses on using state-of-the-art machine learning-based approaches to improve the performance of Software-Defined Networking

作者: 紳士 時(shí)間: 2025-3-22 16:13

作者: diskitis 時(shí)間: 2025-3-22 20:30
C. Martius,H. Tiessen,P. L. G. Vlekrmation. DeepWeave learns from the historic workload trace to train the neural networks of the DRL agent and encodes the scheduling policy in the neural networks, which make coflow scheduling decisions without expert knowledge or a pre-assumed model.

作者: 雜役 時(shí)間: 2025-3-22 21:54

作者: 干涉 時(shí)間: 2025-3-23 04:55

作者: Detoxification 時(shí)間: 2025-3-23 07:00
Book 2022achine learning-based approaches to improve the performance of Software-Defined Networking (SDN). It will apply several innovative machine learning methods (e.g., Deep Reinforcement Learning, Multi-Agent Reinforcement Learning, and Graph Neural Network) to traffic engineering and controller load bal

作者: abnegate 時(shí)間: 2025-3-23 09:49
Book 2022y taking advantage of existing machine learning techniques. The book elaborates on the formulation of each problem, explains design details for each scheme, and provides solutions by running mathematical optimization processes, conducting simulated experiments, and analyzing the experimental results.

作者: legislate 時(shí)間: 2025-3-23 17:40

作者: Fermentation 時(shí)間: 2025-3-23 19:38

作者: DRILL 時(shí)間: 2025-3-24 01:24

作者: photopsia 時(shí)間: 2025-3-24 04:01
Cecilia Pahlberg,Magnus Perssonamework for each agent in the MARL model. The DRL-based solution takes the workload pattern in the control plane as input and generates the migration decision as the output. When training is done, the DRL agent can quickly and accurately decide how to migrate switches among the controllers.

作者: 期滿(mǎn) 時(shí)間: 2025-3-24 07:05

作者: 使痛苦 時(shí)間: 2025-3-24 12:13

作者: JAMB 時(shí)間: 2025-3-24 15:04

作者: NAIVE 時(shí)間: 2025-3-24 22:25
Multi-Agent Reinforcement Learning-Based Controller Load Balancing in SD-WANs,amework for each agent in the MARL model. The DRL-based solution takes the workload pattern in the control plane as input and generates the migration decision as the output. When training is done, the DRL agent can quickly and accurately decide how to migrate switches among the controllers.

作者: 脫離 時(shí)間: 2025-3-25 00:41
Amjad Hadjikhani,Pervez Ghauri,Jan JohansonIn this chapter, we introduce software-defined networking, and its two typical application scenarios: wide area networks and data center networks. We also briefly introduce emerging machine learning techniques to improve network performance that are used in the rest of this book.

作者: Truculent 時(shí)間: 2025-3-25 06:57
Machine Learning for Software-Defined Networking,In this chapter, we introduce software-defined networking, and its two typical application scenarios: wide area networks and data center networks. We also briefly introduce emerging machine learning techniques to improve network performance that are used in the rest of this book.

作者: 西瓜 時(shí)間: 2025-3-25 09:04
Bringing Machine Learning to Software-Defined Networks978-981-19-4874-9Series ISSN 2191-5768 Series E-ISSN 2191-5776

作者: 圓錐 時(shí)間: 2025-3-25 12:18