Edge Compute Orchestration for Latency and Cost Trade-offs
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Solution Overview
Problem
Current distributed computing architectures face challenges in efficiently and cost-effectively deploying computing resources to meet the processing and latency requirements of user equipment devices across diverse geographies, balancing node performance and efficiency while minimizing resource localization and maintenance costs.
Innovation Solution
An edge compute orchestration system that selectively assigns edge compute tasks to edge compute nodes based on real-time and historical characterization data, using a node selection policy to balance node performance and efficiency, optimizing the distribution of workloads across a network of nodes with varying computing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If computing resources are deployed closer to user equipment devices to reduce latency, then processing speed and responsiveness are improved, but deployment and maintenance costs increase
Solution Approach 1:
The patent implements local quality by deploying computing resources at specific edge locations closer to user equipment that require low-latency processing, rather than uniformly distributing resources. The edge compute nodes are strategically positioned at network edges where they can serve specific geographic regions with reduced latency, while maintaining a centralized cloud infrastructure for less time-sensitive workloads. This selective local deployment optimizes processing speed for latency-sensitive applications without proportionally increasing overall deployment costs.
2Adaptability or versatility
If more edge compute nodes are deployed across diverse geographies to serve more users, then network coverage and accessibility are improved, but maintenance complexity and costs increase
Solution Approach 1:
The patent applies segmentation by dividing the computing infrastructure into distinct layers: centralized cloud compute resources handle bulk processing and less time-sensitive workloads, while distributed edge compute nodes handle latency-sensitive applications. This segmentation allows the system to achieve wide geographic coverage through edge nodes without proportionally increasing maintenance complexity, as the centralized cloud layer provides management and orchestration capabilities that simplify edge node operations.
Solution Approach 2:
The patent introduces an intermediary orchestration layer that manages the distribution and coordination of workloads between centralized cloud resources and distributed edge nodes. This intermediary system handles the complexity of managing geographically dispersed edge compute nodes by providing automated provisioning, monitoring, and resource allocation, thereby enabling wide network coverage without linearly increasing maintenance complexity.
3Loss of time
If computing tasks are processed locally at edge nodes, then latency is reduced, but the need for sophisticated workload distribution and node selection mechanisms increases
Solution Approach 1:
The patent implements dynamics by creating a flexible, adaptive workload distribution system that can dynamically assign tasks to either edge compute nodes or centralized cloud resources based on real-time conditions. The system evaluates task characteristics, node availability, and performance requirements to dynamically determine the optimal execution location, enabling low-latency processing when appropriate while maintaining manageable complexity through automated decision-making algorithms.
Solution Approach 2:
The patent applies parameter changes by using a node selection policy that evaluates multiple parameters including task type, latency requirements, node capacity, and geographic location to determine optimal workload distribution. By changing and adjusting these parameters based on real-time system state and task requirements, the system achieves low-latency processing for time-sensitive applications while maintaining overall system complexity within manageable bounds through automated parameter optimization.
Data Source
AI summary
An exemplary edge compute orchestration system that is communicatively coupled with a set of edge compute nodes in a communication network receives a task assignment request generated by a user equipment (“UE”) device coupled to the network. The request is associated with an edge compute task that is to be performed in furtherance of an application executing on the UE device. The system also accesses node characterization data for the set of nodes and manages a node selection policy configured to facilitate a balancing of node performance and node efficiency when assigning edge computing tasks to different nodes in the set. The system selects a node for performance of the edge compute task from the set of nodes in response to the request, based on the node characterization data, and in accordance with the selection policy. The system assigns the edge compute task to be performed by the selected node.