Automated Pricing-based Provisioning of SDN/NFV Services in Distributed Multi-access Edge Computing using Cooperative Multi-Agent Deep Reinforcement Learning

Keyphrases

• Network Function Virtualization 100%
• Software-defined Networking 100%
• Mobile Edge Computing 100%
• Multi-agent Deep Reinforcement Learning (MADRL) 100%
• Cooperative multi-agent Systems 100%
• Charge Model 75%
• Network Architecture 50%
• Service Provider 50%
• Virtual Network 50%
• Telecom Network 50%
• Co-op 50%
• MAPPO 50%
• Quality of Service 25%
• Architecture-centric 25%
• New Business Models 25%
• Virtual Network Function 25%
• Operational Efficiency 25%
• Decision-making Approach 25%
• Failure Rate 25%
• Business Model 25%
• Telecom 25%
• Virtualization Technology 25%
• Management Model 25%
• Multi-agent Systems 25%
• Computing Resource Allocation 25%
• Reinforcement Learning Algorithm 25%
• Edge Computing System 25%
• Deep Reinforcement Learning (deep RL) 25%
• Operating Profit 25%
• Virtualization Infrastructure 25%
• Proximal Policy Optimization Algorithm 25%
• Virtual Infrastructure Management 25%
• Real-time Charging 25%
• Operational Profitability 25%
• Alternative Decisions 25%
• Flexible Charging 25%
• NFV Orchestrator 25%
• Multi-agent Actor-critic 25%
• Actor Model 25%

Computer Science

• Software-Defined Network 100%
• Network Function Virtualization 100%
• Multi-Access Edge Computing 100%
• Multi-Agent Deep Reinforcement Learning 100%
• Charging Model 75%
• Network Architecture 50%
• Business Models 50%
• Service Provider 50%
• Decision-Making 25%
• Quality of Service 25%
• Computing Environment 25%
• Virtual Networks 25%
• Computing Resource 25%
• Operational Efficiency 25%
• Optimization Algorithm 25%
• Virtualization Technology 25%
• multi agent 25%
• Multi Agent Systems 25%
• Deep Reinforcement Learning 25%
• Resource Allocation 25%
• Virtual Infrastructure 25%
• Network Function 25%