NUMA Processor Pool Allocation for Cellular Network Traffic Loads
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Solution Overview
Problem
Existing cellular network architectures face challenges in efficiently allocating computational resources, particularly in NUMA systems, leading to underutilization and inefficiencies in managing high traffic loads and specialized services.
Innovation Solution
A system and method for dynamically allocating computational resources using Non-Uniform Memory Access (NUMA) processors, which includes creating dedicated processor sets, priority pools, and scheduling tasks based on service type and traffic load, enhanced by artificial intelligence for predictive resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computational resources are allocated using traditional methods in cellular networks, then resource management is simplified, but resource utilization efficiency deteriorates leading to underutilization during low traffic and overload during high traffic
Solution Approach 1:
The patent implements dynamic resource allocation where the resource allocation manager continuously monitors traffic load and computational requirements, then adjusts the allocation of computational resources to processor pools in real-time. This dynamic approach allows the system to adapt to varying traffic conditions, improving resource utilization efficiency while managing complexity through automated control mechanisms.
Solution Approach 2:
The system employs feedback mechanisms where the resource allocation manager receives information about current traffic load, service type requirements, and processor pool status, then uses this feedback to make informed decisions about resource allocation. This closed-loop control enables the system to optimize resource utilization while maintaining manageable complexity through data-driven decision making.
2Adaptability or versatility
If computational resources are dynamically allocated based on traffic load, then resource utilization improves, but system complexity increases
Solution Approach 1:
The patent segments computational resources into multiple processor pools, each specialized for specific service types or traffic patterns. The resource allocation manager divides the overall resource management task into smaller sub-tasks, allocating resources to different pools based on service requirements. This segmentation enables adaptive resource allocation while keeping the complexity of individual allocation decisions manageable.
Solution Approach 2:
The resource allocation manager implements a universal control mechanism that handles multiple service types and traffic conditions through a single coordinated system. This multi-functional approach allows the system to adapt to various scenarios while maintaining a unified management structure, balancing adaptability with controlled complexity.
3Reliability
If dedicated processor sets are created for specific services, then service quality improves, but resource flexibility deteriorates
Solution Approach 1:
The patent applies local quality by creating dedicated processor pools with specific characteristics optimized for particular service types. Each processor pool is tailored to handle specific service requirements, ensuring high service quality for specialized workloads. The resource allocation manager then provides the flexibility to allocate to different pools based on current needs, maintaining both service quality and resource flexibility.
Solution Approach 2:
The system introduces an additional dimension of management by separating service specialization (horizontal dimension) from resource allocation decisions (vertical dimension). Processor pools are organized by service type, while the resource allocation manager operates in the allocation dimension, deciding which pool receives resources based on current traffic patterns. This dimensional separation maintains service quality through specialization while preserving flexibility through dynamic allocation decisions.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a computational platform for a communication network, having: a plurality of non-uniform memory access (NUMA) processors; a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: creating a first configuration that dedicates a first set of NUMA processors of the plurality to servicing a first service function of the communication network; creating a first pool of NUMA processors of the plurality having an assigned priority, wherein NUMA processors in the first pool are not members of the first set; and scheduling a computational task for service by the first pool of NUMA processors. Other embodiments are disclosed.