Independent Node-Agent Load Balancing via Capacity-Aware Data Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in efficiently balancing data processing loads across processing nodes to prevent node agent overload, leading to potential failures and data loss due to excessive resource consumption.
Innovation Solution
A method involving a first agent on a processing node determining its capacity, retrieving a list of nodes with sufficient capacity from a lookup service, and transferring data to a second agent on a suitable node for processing, considering factors like geographical location, latency, and security policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is processed locally by the agent on the processing node, then processing speed is improved, but node overload occurs when capacity is exceeded
Solution Approach 1:
The system dynamically adjusts data processing location based on real-time node capacity conditions. The agent checks node capacity and dynamically switches between local processing and remote processing to other nodes, making the system adaptable to changing load conditions and preventing node overload
Solution Approach 2:
The patent introduces an intermediary mechanism (capacity checking and node selection logic) that mediates between local processing needs and distributed load balancing. The agent acts as an intermediary that routes data to appropriate processing nodes based on capacity assessment
2Productivity
If data is transferred to another processing node, then load balancing is improved, but system complexity increases
Solution Approach 1:
The agent performs self-service by autonomously checking node capacity conditions and selecting appropriate processing nodes without external intervention. This self-service capability simplifies the overall system architecture by eliminating the need for complex centralized scheduling mechanisms
3Productivity
If node capacity threshold is set low, then more nodes can process data, but processing precision may be compromised
Solution Approach 1:
The system changes the parameter of node selection criteria by considering multiple factors beyond just capacity threshold, including geographical location, latency, and security policies. This multi-parameter approach ensures that data is routed to nodes that meet both productivity and quality requirements
Data Source
Figure 1
Figure 2
Figure 3
AI summary
In one example, an agent of a first processing node can receive data from a data provider. The agent can determine whether the first processing node has at least a threshold amount of computing capacity. In response to determining that the first processing node has less than the threshold amount of computing capacity, the agent can receive, from a lookup service, a list of one or more processing nodes in the computing cluster that have at least the threshold amount of computing capacity. The agent them can select, from the list, a second processing node that has at least the threshold amount of computing capacity. Having selected the second processing node, the agent can cause the data to be transmitted to a second agent of the second processing node, the second agent being configured to process the data and provide the processed data to a backend server system.