Abstract:
Service quality assurance is of capital importance in modern cloud and network infrastructures, especially in multi-domain scenarios, where multiple operators collaborate...Show MoreMetadata
Abstract:
Service quality assurance is of capital importance in modern cloud and network infrastructures, especially in multi-domain scenarios, where multiple operators collaborate to provide end-to-end (E2E) services. However, due to the dynamics of the multiple infrastructures and deployed services, it may be difficult to identify which domains need to perform (re-)configuration operations, named actuations, to keep the quality of the E2E services. In this regard, Machine Learning (ML) techniques appear as an interesting solution for guiding the actuation systems in multi-domain scenarios. With this in mind, we present a novel approach for self-optimised multi-domain service provisioning, leveraging the capacities of Deep Reinforcement Learning (DRL), with a focus on E2E service quality assurance. Running away from traditional approaches, the presented proposal tries to minimize the number of domains that need to actuate rather than determining the exact domain-specific actuations. We compare our proposal to existing strategies in terms of performance, scalability and applicability in real scenarios.
Published in: IEEE Transactions on Network and Service Management ( Volume: 20, Issue: 3, September 2023)
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- IEEE Keywords
- Index Terms
- Quality Assurance ,
- Service Quality Assurance ,
- Service Quality ,
- Actuator ,
- Machine Learning Techniques ,
- Network Infrastructure ,
- Deep Reinforcement Learning ,
- Cloud Infrastructure ,
- Complex Systems ,
- State Space ,
- Computational Resources ,
- State Value ,
- Multi-core ,
- Key Performance Indicators ,
- Reward Function ,
- Combination Of States ,
- Centralized Approach ,
- Source Of Problems ,
- Control Architecture ,
- Optimal Action ,
- Virtual Network Functions ,
- Number Of Actuators ,
- Quality Of Transmission ,
- Virtual Link ,
- Network Slicing ,
- Network Metrics ,
- Optical Networks ,
- Representational State Transfer ,
- Wavelength Division Multiplexing ,
- Memory Usage
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Quality Assurance ,
- Service Quality Assurance ,
- Service Quality ,
- Actuator ,
- Machine Learning Techniques ,
- Network Infrastructure ,
- Deep Reinforcement Learning ,
- Cloud Infrastructure ,
- Complex Systems ,
- State Space ,
- Computational Resources ,
- State Value ,
- Multi-core ,
- Key Performance Indicators ,
- Reward Function ,
- Combination Of States ,
- Centralized Approach ,
- Source Of Problems ,
- Control Architecture ,
- Optimal Action ,
- Virtual Network Functions ,
- Number Of Actuators ,
- Quality Of Transmission ,
- Virtual Link ,
- Network Slicing ,
- Network Metrics ,
- Optical Networks ,
- Representational State Transfer ,
- Wavelength Division Multiplexing ,
- Memory Usage
- Author Keywords