Edge Node Selection for Low-Latency On-Site Service
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Solution Overview
Problem
Current cloud-based service solutions face inefficiencies due to transmission delays and the inability to utilize location-independent resources effectively, leading to unsatisfactory media processing and high real-time demands in networks.
Innovation Solution
A system and method for providing on-site services using a network of nodes with modules for neighborhood node set generation, candidate service node selection, and service scheduling, which dynamically selects the most suitable node based on quality of service (QoS) differences and resource availability to process service requests efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If cloud service is used for media processing, then centralized resource management is achieved, but transmission delay increases and real-time processing capability deteriorates
Solution Approach 1:
The patent segments the centralized cloud service into distributed edge computing nodes. Instead of one centralized cloud server, multiple edge nodes are deployed throughout the network, each capable of independent service execution. This segmentation reduces transmission delay by processing data locally at the edge rather than transmitting to a distant cloud center, while maintaining resource management capabilities through coordinated node operation.
Solution Approach 2:
The patent introduces a spatial dimension to service deployment by placing computing nodes at the network edge rather than concentrating them in a single cloud data center. This dimensional change from centralized to distributed architecture enables simultaneous proximity to multiple users, reducing transmission delay while maintaining resource management through the service node selection module that coordinates across the distributed network.
2Device complexity
If cloud computing resources are centrally deployed, then resource management is simplified, but service location independence increases leading to inability to meet location-based processing requirements
Solution Approach 1:
The patent applies local quality by enabling each edge node to provide services tailored to its local geographic context. The service node selection module selects execution nodes based on location proximity to users, ensuring that services are delivered from the most appropriate local node. This maintains simplified resource management through automated selection while achieving location-based adaptability by prioritizing geographically proximate nodes.
Solution Approach 2:
The patent introduces dynamics through the service node selection module that continuously adapts service execution node selection based on real-time conditions. The system dynamically determines which edge node should execute a service request by evaluating multiple factors including location proximity, current resource availability, and service type requirements. This dynamic adaptation enables the system to balance simplified resource management with location-based service capabilities.
3Reliability
If service requests are processed through centralized cloud, then uniform service quality is maintained, but processing efficiency for large-scale data decreases
Solution Approach 1:
The patent segments large-scale data processing tasks across multiple distributed edge nodes rather than processing everything through a single centralized cloud. The service node selection module divides service requests and routes them to appropriate edge nodes based on data size, node capacity, and service type. This segmentation maintains service quality consistency through standardized selection criteria while dramatically improving processing efficiency by parallelizing operations across multiple nodes.
Solution Approach 2:
The patent applies partial action by having the service node selection module select only the necessary subset of edge nodes for processing each service request, rather than involving all available nodes. This selective approach maintains service quality through controlled node selection while improving efficiency by avoiding unnecessary resource involvement and reducing coordination overhead.
4Loss of time
If edge computing nodes are deployed, then transmission delay is reduced, but resource utilization efficiency deteriorates due to scattered and unused resources
Solution Approach 1:
The patent applies universality by designing edge computing nodes with multi-functional capabilities that can handle various service types. The service node selection module selects nodes based on their ability to perform multiple functions, allowing a single edge node to serve multiple users and service types. This increases resource utilization efficiency by maximizing the use of deployed edge nodes while maintaining the low transmission delay benefits of distributed architecture.
Solution Approach 2:
The patent implements feedback through the service node selection module that continuously monitors edge node status, resource availability, and service performance. This feedback mechanism enables the system to dynamically adjust service request routing to optimize resource utilization. Nodes that are underutilized receive more service requests, while overloaded nodes are bypassed, thereby improving overall resource utilization efficiency while maintaining the low-latency benefits of edge computing.
Data Source
Figure 1
AI summary
The present invention provides a system and method for providing an on-site service, said system containing a plurality of nodes, each node containing: a neighborhood node set generation module, used for generating a neighborhood node set on the basis of the bidirectional link bandwidth between a local node and a neighboring node; a neighborhood information index table generation module, used for generating a neighborhood information index table of the local node; a candidate service point selection module, used for selecting according to a selection function a candidate service node from the set of neighboring nodes; the definition of said selection function being: for a current service request, computing the difference between the QoS of the neighboring node i executing the service request and the QoS of the local node executing the service request; if the computed difference is smaller than a set threshold, the neighboring node i serving as the candidate service node; a service scheduling module, used for receiving status information and feedback information provided in real time by the candidate service node, and selecting, on the basis of this information, a candidate node or the local node to serve as the service-executing node.