Dynamic Service Function Chain Migration for RAN Resource Optimization

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Solution Overview

Problem

Current wireless communication systems face challenges in efficiently managing computing resources due to varying load conditions in wireless networks, leading to suboptimal usage of hardware resources and potential service degradation.

Innovation Solution

An apparatus and method for monitoring load conditions of service function chain instances, determining threshold load conditions, and migrating transmission contexts from one deployment configuration to another to optimize resource allocation and reduce hardware usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If computing resources are allocated statically in wireless networks, then device simplicity and operational ease are improved, but resource utilization efficiency deteriorates due to varying load conditions

Engineering Contradiction:

Improveoperational simplicityVSAvoidresource utilization efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The patent implements dynamic resource allocation by monitoring load conditions and automatically migrating service function chain instances between deployment configurations. The system transitions from static to dynamic resource management by continuously adapting the deployment configuration based on real-time load monitoring, thereby improving resource utilization efficiency while maintaining operational simplicity through automation.

Inventive Principle:

Principle #15Dynamics

2Reliability

If more hardware resources are allocated to handle peak load conditions, then service quality is improved, but hardware resource usage efficiency deteriorates during low load periods

Engineering Contradiction:

Improveservice qualityVSAvoidhardware resource usage efficiency

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system dynamically adjusts hardware resource usage by migrating service function chain instances between different deployment configurations based on load conditions. During peak load, resources are allocated to maintain service quality; during low load, resources are released or consolidated, reducing energy consumption and improving hardware resource usage efficiency while maintaining reliable service delivery.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent changes the deployment configuration parameter based on load conditions. By monitoring load metrics and transitioning between deployment configurations (e.g., from first to second configuration), the system optimizes the balance between service quality and resource efficiency, ensuring adequate resources during high load while minimizing waste during low load periods.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If service function chain instances are migrated between deployment configurations, then resource allocation efficiency is improved, but system complexity increases

Engineering Contradiction:

Improveresource allocation efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system implements self-service automation by autonomously monitoring load conditions and performing service function chain instance migrations without manual intervention. The automated load monitoring and migration process manages the increased system complexity internally, allowing the organization to benefit from improved resource allocation efficiency while avoiding the operational burden of manual management.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent implements feedback mechanisms through continuous load monitoring that informs migration decisions. The system monitors load conditions, uses this feedback to determine when migration is needed, and automatically executes migrations to optimize resource allocation. This feedback-driven approach manages system complexity by providing clear decision criteria and automated execution.

Inventive Principle:

Principle #23Feedback

4Adaptability or versatility

If virtualization of network functions is implemented, then resource flexibility is improved, but measurement and monitoring complexity increases

Engineering Contradiction:

Improveresource flexibilityVSAvoidmonitoring complexity

Core Design Contradiction:

Adaptability or versatilityVSDifficulty of detecting and measuring

Solution Approach 1:

The patent addresses monitoring complexity in virtualized networks by implementing structured feedback mechanisms through load monitoring. The system monitors load conditions across virtualized network functions and uses this feedback to trigger automated migrations between deployment configurations. This approach manages monitoring complexity by providing clear metrics and automated decision-making based on monitored data.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20230362728A1Allocation of Computing Resources for Radio Access Networks

Publication Date: 2023.11.09 NOKIA SOLUTIONS & NETWORKS OY

AI summary

A method comprising monitoring load condition of a first service function chain instance deployed in a first deployment configuration, determining that a threshold load condition is met, wherein the threshold load condition corresponds to a trigger for a migration, determining a second deployment configuration, deploying a second service function instance in the second deployment configuration, migrating a plurality of transmission contexts from the first service function chain instance to the second service function instance, and removing the plurality of transmission contexts from the first service function chain instance.