[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Xu et al., 2018 - Google Patents

Living with artificial intelligence: A paradigm shift toward future network traffic control

Xu et al., 2018

Document ID
8961088284322554997
Author
Xu J
Wu K
Publication year
Publication venue
Ieee Network

External Links

Snippet

Future Internet is expected to meet explosive traffic growth and extremely complex architecture, which tend to make the traditional NTC strategies inefficient and even ineffective. Inspired by the latest breakthroughs of AI and its power to address large-scale …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50Computer-aided design
    • G06F17/5009Computer-aided design using simulation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance or administration or management of packet switching networks
    • H04L41/14Arrangements for maintenance or administration or management of packet switching networks involving network analysis or design, e.g. simulation, network model or planning
    • H04L41/145Arrangements for maintenance or administration or management of packet switching networks involving network analysis or design, e.g. simulation, network model or planning involving simulating, designing, planning or modelling of a network
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor

Similar Documents

Publication Publication Date Title
Shi et al. Machine learning for large-scale optimization in 6g wireless networks
CN111191934B (en) A Multi-objective Cloud Workflow Scheduling Method Based on Reinforcement Learning Strategy
Xu et al. Living with artificial intelligence: A paradigm shift toward future network traffic control
CN109818786B (en) Method for optimally selecting distributed multi-resource combined path capable of sensing application of cloud data center
CN113098714A (en) Low-delay network slicing method based on deep reinforcement learning
CN112685165B (en) Multi-target cloud workflow scheduling method based on joint reinforcement learning strategy
Liu et al. Deep generative model and its applications in efficient wireless network management: A tutorial and case study
CN113919485A (en) Multi-agent reinforcement learning method and system based on dynamic hierarchical communication network
WO2024066675A1 (en) Multi-agent multi-task hierarchical continuous control method based on temporal equilibrium analysis
CN114710439B (en) Network energy consumption and throughput joint optimization routing method based on deep reinforcement learning
CN112990485A (en) Knowledge strategy selection method and device based on reinforcement learning
CN110083748A (en) A kind of searching method based on adaptive Dynamic Programming and the search of Monte Carlo tree
Wei et al. GRL-PS: Graph embedding-based DRL approach for adaptive path selection
CN116614394A (en) A service function chain placement method based on multi-objective deep reinforcement learning
Liu et al. GA-DRL: Graph neural network-augmented deep reinforcement learning for DAG task scheduling over dynamic vehicular clouds
Afifi et al. Machine learning with computer networks: Techniques, datasets and models
Zhang et al. Decision Transformers for Wireless Communications: A New Paradigm of Resource Management
CN114722946B (en) Synthesis of Asynchronous Action and Cooperative Strategy for Unmanned Aerial Vehicle Based on Probabilistic Model Checking
CN115562746A (en) A MEC associated task offloading method with limited computing resources
CN115022231A (en) Optimal path planning method and system based on deep reinforcement learning
Li et al. Online coordinated NFV resource allocation via novel machine learning techniques
CN116955151A (en) EFSM test sequence generation method based on deep learning and ant colony algorithm
CN116367190A (en) A digital twin function virtualization method for 6G mobile network
CN115759199A (en) Multi-robot environment exploration method and system based on hierarchical graph neural network
CN115150335A (en) Optimal flow segmentation method and system based on deep reinforcement learning