Virtual Distributed Antenna System Dynamic Scaling
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Solution Overview
Problem
Current distributed antenna systems (DAS) face challenges in dynamically scaling capacity to accommodate fluctuating network traffic, especially during peak hours or maintenance periods, which can lead to inefficiencies and reduced performance.
Innovation Solution
A virtualized DAS (vDAS) system with a computing node that implements virtual network functions (NFs) and uses containers to scale capacity by instantiating or deleting additional containers based on periodic capacity usage reports and threshold limits, allowing for flexible scaling in response to changing traffic demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional DAS systems use fixed capacity allocation, then system simplicity is maintained, but the system cannot adapt to fluctuating network traffic demands during peak hours or maintenance periods
Solution Approach 1:
The patent implements dynamic capacity allocation by allowing the DAS system to automatically adjust the number of active vDAS containers based on real-time traffic conditions. The system transitions from fixed capacity to dynamic scaling where containers can be instantiated or deleted according to demand thresholds, enabling the system to adapt to fluctuating network traffic while managing complexity through automation.
Solution Approach 2:
The system changes the capacity parameter dynamically by monitoring traffic conditions and adjusting the number of active vDAS containers accordingly. When traffic exceeds thresholds, additional containers are instantiated to increase capacity; when traffic decreases, containers are deleted to reduce resource consumption. This parameter change approach allows the system to balance adaptability with operational simplicity.
2Speed
If the system manually scales capacity, then control precision is maintained, but response time to traffic changes is delayed
Solution Approach 1:
The vDAS system performs self-scaling by automatically monitoring its own capacity usage and traffic conditions. The system evaluates whether to instantiate or delete containers based on predefined thresholds and traffic patterns, eliminating the need for manual intervention. This self-service mechanism dramatically improves response speed to traffic changes while reducing operational complexity.
Solution Approach 2:
The system implements feedback-driven scaling by continuously monitoring traffic conditions and capacity usage, then using this information to automatically adjust the number of active containers. The feedback loop ensures the system responds promptly to changing demands while maintaining control through automated decision-making based on real-time metrics.
3Reliability
If additional vDAS containers are instantiated to increase capacity, then network performance is improved, but resource consumption increases
Solution Approach 1:
The system applies partial scaling by instantiating only the necessary number of additional containers when traffic exceeds thresholds, rather than maintaining maximum capacity continuously. This partial action approach improves network performance during peak demand while minimizing resource consumption during normal operations, achieving an optimal balance between reliability and efficiency.
Solution Approach 2:
The system dynamically adjusts resource allocation by transitioning between different numbers of active containers based on real-time traffic conditions. When performance is needed, containers are instantiated; when resources should be conserved, containers are deleted. This dynamic approach ensures network performance is maintained only when necessary, optimizing the trade-off between reliability and resource utilization.
Data Source
AI summary
A computing system having a vDAS compute node implementing at least one virtual network function (NF) in a virtualized distributed antenna system (vDAS) having a plurality of radio units (RUs). The computing system includes server and vDAS compute node. The vDAS compute node includes vDAS container(s) running on a first subset of cores. The server: receives periodic capacity usage reports from the vDAS compute node(s); compares scaling metric data derived from the periodic capacity usage reports to threshold limits to determine if any threshold limits have been reached by any scaling metric data for the vDAS compute node; when any threshold limits have been reached by any scaling metric data for at least one vDAS compute node: cause vDAS compute node to scale capacity by either instantiating or deleting additional vDAS container on second subset of cores of the at least one vDAS compute node.