Opportunistic Compute Placement in Edge Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Edge computing networks face challenges in providing resiliency and performance due to limited horizontal scaling, heterogeneous resources, and security concerns, especially in 5G/6G networks, where resources may be geographically isolated and dynamically change, leading to issues with latency and resource management.
Innovation Solution
The implementation of a resource hierarchy from the Radio Access Network (RAN) to the cloud for performance optimization, using resource management controllers to dynamically discover and allocate resources to resiliency pools based on availability, and employing edge networking with enhanced resource management to improve performance and resiliency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If compute resources are placed at the edge of the network to reduce latency, then network latency is reduced, but resource management complexity increases due to heterogeneous resources and limited horizontal scaling
Solution Approach 1:
The patent segments compute resources into different types (stateful and stateless) and places them at different hierarchical levels (edge nodes, cloud data centers). This segmentation enables independent management of resource types while reducing latency by placing stateless compute closer to users. The resource manager divides placement decisions into local (edge) and centralized (cloud) components, simplifying management.
Solution Approach 2:
The patent introduces a hierarchical dimension to resource placement, organizing resources across multiple levels (edge tier, cloud tier) rather than a single flat layer. This dimensional approach allows the system to leverage both local edge resources for low-latency operations and centralized cloud resources for comprehensive management, resolving the complexity-latency tradeoff.
2Adaptability or versatility
If resources are dynamically allocated to meet varying network demands, then service availability improves, but security and trust issues arise due to operational state changes in memory
Solution Approach 1:
The patent introduces a resource manager as an intermediary component that mediates between compute resource allocation and security concerns. This manager centrally coordinates placement decisions, monitors resource states, and ensures secure operation by maintaining awareness of operational states across heterogeneous resources, thereby enabling dynamic allocation while mitigating security risks.
Solution Approach 2:
The system implements feedback mechanisms where the resource manager continuously monitors operational states of compute resources and adjusts allocations accordingly. This feedback loop enables the system to respond to changing conditions while maintaining security by detecting and preventing unauthorized or unsafe state changes in memory and processing resources.
3Adaptability or versatility
If compute resources are distributed across geographically isolated locations, then horizontal scaling is enabled, but network reliability decreases due to connectivity issues and resource unavailability
Solution Approach 1:
The patent implements dynamic resource allocation where the resource manager can flexibly assign compute resources to different locations based on real-time availability and demand. This dynamic approach enables the system to adapt to changing network conditions, maintaining reliability while leveraging geographic distribution for scaling capabilities.
Solution Approach 2:
The system performs preliminary actions by pre-positioning stateless compute resources at multiple edge locations and maintaining inventory awareness of available resources. This preparation enables faster deployment and higher reliability when resources are needed, as the system can quickly allocate from pre-positioned resources rather than provisioning from scratch across isolated locations.
Data Source
AI summary
Various systems and methods for providing opportunistic placement of compute in an edge network are described herein. A node in an edge network may be configured to access a service level agreement related to a workload, the workload to be orchestrated for a user equipment by the node; modify a machine learning model based on the service level agreement; implement the machine learning model to identify resource requirements to execute the workload in a manner to satisfy the service level agreement; initiate resource assignments from a resource provider, the resource assignments to satisfy the resource requirements; construct a resource hierarchy from the resource assignments; initiate execution of the workload using resources from the resource hierarchy; and monitor and adapt execution of the workload based on the resource hierarchy in response to the execution of the workload.