ML-Based SFC Resource Provisioning for Network Efficiency
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Solution Overview
Problem
Existing solutions for provisioning service function chain (SFC) resources rely on static rules and policies, leading to either over-provisioning or under-provisioning, resulting in inefficient resource usage and performance degradation.
Innovation Solution
The implementation of machine learning systems that learn resource consumption tendencies of SFCs to accurately determine the required resources and predict adjustments, optimizing resource allocation through a unified platform combining SDN and NFV technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static rules and policies are used for provisioning SFC resources, then resource provisioning is simplified and easier to manage, but resource usage efficiency deteriorates due to over-provisioning or under-provisioning
Solution Approach 1:
The patent transitions from static resource provisioning rules to dynamic machine learning-based resource allocation. The system continuously learns from historical resource consumption data and traffic patterns to dynamically adjust resource provisioning decisions, enabling adaptive optimization without manual intervention while improving resource usage efficiency.
Solution Approach 2:
The patent implements a feedback mechanism where the machine learning system continuously monitors actual resource consumption and performance metrics, then uses this feedback to refine future resource provisioning decisions. This closed-loop approach allows the system to learn from past decisions and improve resource allocation accuracy over time.
2Device complexity
If static rules and policies are used for provisioning SFC resources, then the system complexity is reduced, but resource allocation accuracy deteriorates leading to performance degradation
Solution Approach 1:
The patent replaces traditional mechanical rule-based provisioning systems with machine learning algorithms. The ML models process historical data and traffic patterns to make intelligent resource allocation decisions, achieving high accuracy without requiring complex manual rule configurations. The system automatically learns optimal provisioning strategies from data.
Solution Approach 2:
The machine learning system performs self-learning and self-optimization by automatically analyzing resource consumption patterns and adjusting provisioning decisions without external intervention. The system serves itself by continuously improving its models based on accumulated data, reducing the need for manual system configuration and maintenance.
3Productivity
If machine learning systems are implemented for dynamic resource provisioning, then resource usage efficiency is improved through accurate prediction, but system complexity increases
Solution Approach 1:
The patent segments the resource provisioning system into distinct functional modules: data collection components, machine learning model training components, prediction components, and execution components. This modular architecture allows each segment to be developed and optimized independently, managing overall system complexity while enabling advanced ML-based resource allocation.
4Measurement precision
If machine learning systems are implemented for dynamic resource provisioning, then resource allocation accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent implements preliminary action by pre-training machine learning models offline using historical resource consumption data and traffic patterns. Once trained, the models can make rapid predictions during runtime without requiring extensive real-time computation. This separates the computationally intensive training phase from the time-sensitive prediction phase, achieving both accuracy and speed.
Data Source
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AI summary
A method implemented by a computing device to optimize resource usage of service function chains (SFCs) in a network using machine learning. The method includes obtaining, from an autoscale machine learning (ML) system associated with a virtual network function (vNF), a suggested adjustment to an amount of resources provisioned for the vNF. The autoscale ML system is trained online using machine learning to predict an amount of resources to be utilized by the vNF. The autoscale ML system is configured to receive as input an amount of resources currently utilized by the vNF and an amount of resources currently available to the vNF, determine using machine learning the suggested adjustment to the amount of resources provisioned for the vNF based on the input, and output the suggested adjustment. The method further includes providing the suggested adjustment to a resource re-allocator component.