Geographic Application Deployment to Edge Nodes
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Solution Overview
Problem
Edge computing nodes, which have limited processing resources compared to hub devices, face challenges in efficiently processing applications due to their smaller computing capacity and memory, leading to suboptimal service delivery and resource utilization.
Innovation Solution
A system and method for geographically deploying applications to edge computing nodes based on demand, using a hub device to receive requests, generate heat maps, and deploy applications to areas exceeding a threshold demand, while deleting underutilized applications to free resources, thereby optimizing processing and memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If applications are processed at edge computing nodes, then service delivery speed is improved, but resource utilization deteriorates due to limited processing capacity
Solution Approach 1:
The patent implements geographic deployment of applications to edge computing nodes based on localized demand patterns. Heat maps are generated to identify specific geographic areas with high application demand, and applications are deployed only to edge nodes within those areas. This ensures that edge nodes have the appropriate applications locally available (improving service delivery speed) while avoiding unnecessary deployments to nodes without demand (preserving resource utilization).
Solution Approach 2:
The system dynamically adjusts application deployment based on changing demand patterns. The hub computing device continuously monitors application requests from edge nodes and updates heat maps accordingly. Applications can be deployed to or removed from edge nodes based on real-time demand analysis, allowing the system to adapt to varying workloads and optimize resource utilization while maintaining fast service delivery where needed.
2Reliability
If applications are deployed to all edge computing nodes, then service availability is improved, but memory consumption increases
Solution Approach 1:
Instead of uniformly deploying applications to all edge computing nodes, the patent uses heat map analysis to identify specific geographic regions with high application demand. Applications are then deployed only to edge nodes located in those high-demand regions. This selective deployment approach ensures service availability in areas where it is most needed while significantly reducing overall memory consumption across the edge computing infrastructure.
Solution Approach 2:
The system implements partial deployment of applications rather than comprehensive deployment to all nodes. By analyzing demand patterns and deploying applications only to the subset of edge nodes where they are actually needed, the system achieves sufficient service availability without the excessive memory consumption that would result from universal deployment.
3Productivity
If applications are deployed based on high demand, then processing efficiency is improved, but device complexity increases
Solution Approach 1:
The patent introduces a hub computing device as an intermediary that centralizes the complexity of demand analysis and deployment decision-making. The hub receives application request data from all edge nodes, generates heat maps to identify high-demand geographic areas, and determines which applications should be deployed to which edge nodes. This intermediary approach improves processing efficiency at edge nodes (as they can execute deployed applications locally) while consolidating deployment management complexity at the hub rather than distributing it across all edge nodes.
Data Source
AI summary
An example system for geographic deployment of applications to edge computing nodes includes: a memory storing an application; a receive engine to receive, from edge computing nodes, indications of requests for the application as received at the edge computing nodes from edge clients, the indications being indicative of geographic demand for the application; a demand engine to determine a geographic area where demand for the application exceeds a threshold demand; and an application deployment engine to deploy the application to the edge computing nodes within the geographic area where the demand for the application exceeds the threshold demand.