vRAN L1 Pipeline Segmentation for CPU Resource Allocation
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Solution Overview
Problem
Existing virtualized radio access network (vRAN) systems face inefficiencies in resource management due to over-dimensioning of computational capacity to handle peak demands, leading to substantial cost inefficiencies and limited understanding of computational behavior and resource dynamics.
Innovation Solution
The method involves dividing the L1 layer processing pipeline into main and subordinate processing pipelines across multiple virtualized radio access points (vRAPs), with a centralized CPU scheduler allocating tasks to dedicated or shared CPUs based on real-time or best-effort requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computational capacity is over-dimensioned to handle peak demands in real-time workloads, then service reliability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The L1 layer processing pipeline is divided into main processing pipelines and subordinate processing pipelines. Main pipelines handle critical real-time tasks and are allocated dedicated CPUs to ensure service reliability. Subordinate pipelines handle less critical tasks and are allocated shared CPUs to improve resource utilization efficiency. This segmentation allows the system to meet peak demands reliably while avoiding over-dimensioning of computational capacity.
2Speed
If dedicated CPUs are allocated to all processing pipelines, then processing speed is improved, but device complexity and cost increase
Solution Approach 1:
Different CPU allocation strategies are applied to different processing pipelines based on their quality of service requirements. Main processing pipelines that handle critical real-time tasks receive dedicated CPU allocation to ensure processing speed and reliability. Subordinate processing pipelines that handle less critical tasks receive shared CPU allocation to reduce device complexity and cost. This local quality approach optimizes the balance between processing speed and system complexity.
3Productivity
If computational resources are centralized in a cloud location, then resource pooling efficiency is improved, but latency in task execution increases
Solution Approach 1:
The centralized CPU pool is segmented into dedicated CPUs for main pipelines and shared CPUs for subordinate pipelines. This segmentation enables the system to maintain resource pooling efficiency while providing guaranteed processing capacity for time-critical tasks through dedicated CPUs, thereby reducing task execution latency for real-time workloads.
Solution Approach 2:
The system pre-allocates dedicated CPUs for main processing pipelines that handle real-time tasks. This preliminary action ensures that when real-time tasks arrive, dedicated computational resources are already available, reducing task execution latency while maintaining the benefits of centralized resource pooling.
Data Source
AI summary
A method for managing radio and computing resources of a virtualized radio access network (vRAN) includes a number of virtualized radio access points (vRAPs) that share a common pool of central processing units (CPUs). The method includes dividing, per vRAP, an L1 layer processing pipeline into at least one main processing pipeline and into a number of subordinate processing pipelines, and coordinating the execution of the pipelines across multiple vRAPs. The coordinating includes allocating tasks of the main processing pipelines to dedicated CPUs, and allocating tasks of the subordinate processing pipelines to shared CPUs.