Edge Computing Latency Optimization via Dynamic Resource Allocation
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Solution Overview
Problem
Next-generation applications requiring near-real-time responses, such as machine learning and autonomous vehicles, face challenges in achieving low latency due to the limitations of traditional computing systems, which often result in high latency times across edge, regional, and central network nodes.
Innovation Solution
A distributed computing architecture that includes edge nodes, regional nodes, and central nodes, where resources such as computation, storage, and network resources are dynamically allocated to optimize latency by determining latency criteria and assigning resources from edge, regional, or central nodes to minimize processing time, using techniques like API query optimization, data placement optimization, and traffic routing optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If computing power is deployed at network edges in distributed architecture, then response time is reduced, but system complexity increases
Solution Approach 1:
The patent segments the computing system into three distinct layers: edge nodes for local data processing, regional nodes for intermediate computation, and central nodes for centralized management. This segmentation allows latency-sensitive operations to occur at the edge while maintaining system coordination through hierarchical structure, thereby reducing overall latency without creating monolithic complexity.
Solution Approach 2:
The patent introduces a hierarchical dimension to the distributed system architecture, organizing nodes across multiple levels (edge, regional, central) rather than a flat structure. This dimensional organization enables local autonomy at the edge for low-latency responses while providing centralized coordination capabilities, effectively managing complexity through structured hierarchy.
2Productivity
If resources are dynamically allocated across distributed nodes, then processing speed is improved, but resource management complexity increases
Solution Approach 1:
The patent implements dynamic resource allocation where computing, storage, and network resources are flexibly assigned across edge, regional, and central nodes based on real-time workload demands. This dynamic allocation allows the system to optimize processing speed for latency-sensitive applications while adapting resource distribution patterns as conditions change.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor system performance, resource utilization, and latency metrics across the distributed network. This feedback enables automated resource management decisions, where the system adjusts resource allocation based on observed conditions, reducing the complexity of manual resource management while maintaining high processing speeds.
3Reliability
If multiple network nodes are used for distributed computing, then reliability is improved, but communication overhead increases
Solution Approach 1:
The patent applies local quality by enabling edge nodes to independently process and analyze data locally before determining what requires further processing at regional or central nodes. This localized processing reduces the volume of data that needs to be communicated across the network, thereby maintaining reliability through distributed processing while minimizing communication overhead.
Solution Approach 2:
The patent implements preliminary action by performing initial data processing, filtering, and analysis at the edge nodes before data is transmitted to regional or central nodes. This preliminary processing reduces the amount of data requiring network communication, decreasing communication overhead while maintaining the reliability benefits of distributed processing across multiple nodes.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a method in which a processing system receives data at an edge node of a network that also includes regional nodes and central nodes. The processing system also determines a latency criterion associated with an application for processing the data; the application corresponds to an application programming interface. The method also includes processing the data in accordance with the application, monitoring a latency associated with the processing, and determining whether the latency meets the latency criterion. The processing system dynamically assigns data processing resources so that the latency meets the latency criterion; the resources include computation, network and storage resources of the edge node, a central node, and a regional node in communication with the edge node and the central node. Other embodiments are disclosed.