Edge Compute Mesh Capacity Scaling for Low-Latency Workloads
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Solution Overview
Problem
Existing wireless devices face limitations in processing power, memory, and battery life, despite advancements in hardware and communication technologies, necessitating improved edge computing solutions to enhance performance and reduce latency.
Innovation Solution
Implementing a virtual Edge Enhanced Computing (vEEC) system that dynamically scales network resources, redistributes tasks among edge computing nodes (ECNs), and offloads computational tasks to more powerful servers, utilizing AI/ML for predictive analysis and proactive adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If edge computing nodes are deployed to reduce latency and enhance processing capabilities, then application performance is improved, but network complexity and resource management difficulty increase
Solution Approach 1:
The patent introduces a centralized controller as an intermediary component that manages resource allocation and task distribution across edge computing nodes. This controller simplifies network complexity by centralizing management functions, enabling the system to maintain low latency while reducing the operational burden on individual nodes and administrators.
Solution Approach 2:
The system segments network resources into dedicated edge computing nodes distributed at various locations. Each node operates as an independent unit with specialized functions, allowing the system to achieve low latency locally while managing complexity through modular architecture. The segmentation enables selective activation of nodes based on demand, simplifying resource management.
2Reliability
If computational tasks are offloaded to cloud resources for ECNs facing resource limitations, then system reliability is improved, but network bandwidth consumption and data backhaul increase
Solution Approach 1:
The patent implements dynamic resource allocation where edge computing nodes can flexibly switch between local processing mode and cloud offloading mode based on real-time conditions. The system dynamically adjusts the degree of offloading to optimize reliability while minimizing data backhaul, activating cloud resources only when necessary and to the extent needed.
Solution Approach 2:
The system changes operational parameters of edge nodes by adjusting the offloading ratio and resource allocation distribution. By modifying these parameters dynamically, the system can maintain reliability through selective cloud offloading while reducing unnecessary data transmission to the cloud, thereby minimizing energy consumption and backhaul requirements.
3Productivity
If network resources are dynamically scaled based on real-time requirements, then resource utilization efficiency is improved, but system response time and control complexity increase
Solution Approach 1:
The patent implements preliminary resource allocation where edge computing nodes are pre-configured with default resource settings and capacity reserves. This preliminary preparation enables rapid response to demand changes without requiring time-consuming resource provisioning, improving both productivity and response time. The system can quickly adjust within pre-established parameters rather than building from scratch.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor resource utilization and demand patterns. This feedback enables automated adjustments to resource allocation, improving efficiency while reducing response time through closed-loop control. The feedback-driven optimization allows the system to learn from historical data and make faster, more accurate resource decisions.
4Adaptability or versatility
If AI/ML algorithms are implemented for predictive analysis and proactive adjustments, then adaptive capability is improved, but computational overhead and processing requirements increase
Solution Approach 1:
The patent applies local quality by implementing lightweight AI/ML models at edge nodes tailored to local specificities rather than using heavy centralized models. Each edge node runs optimized predictive algorithms suited to its local environment, enabling adaptive capability while minimizing computational overhead. The models are customized to process only locally relevant data, reducing processing requirements compared to centralized approaches.
Data Source
AI summary
Various methods and edge computing systems are disclosed that implement a versatile elastic edge compute (vEEC) system that includes a computing mesh including two or more edge computing nodes (ECNs). The ECNs may be configured to identify ECNs in the vEEC and their capabilities, determine resource requirements for one or more software applications or tasks within the vEEC system, dynamically scale network resources based on the determined requirements, resolve network congestion by redistributing tasks among ECNs based on network traffic analysis, implement failover to cloud resources for ECNs that face resource limitations, offload computational tasks from edge devices, monitor network performance and resource utilization for adjustments, and refine resource allocation models and system configurations based on feedback and performance metrics.