Operator Engines Assign Global Resource Limits by Relative Load
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Solution Overview
Problem
In container-orchestrated environments, the scaling of applications and resource pooling applications leads to a high number of requests for shared hardware resources, causing inefficiencies and slowdowns, particularly in database connections, resulting in a decline in processing efficiency.
Innovation Solution
A system comprising multiple application engines that share a global resource limit, with operator engines monitoring and sharing load metrics to determine relative loads and assign portions of the resource limit to each application engine based on its load, ensuring efficient use of resources like database connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If applications are scaled by adding more instances, then the system can handle more demand, but the number of requests to shared hardware resources increases causing operation to decline and slowdown
Solution Approach 1:
The patent implements dynamic resource allocation where the resource pooling application continuously monitors metrics from application instances and adjusts their resource assignments in real-time. This dynamic adjustment allows the system to adapt to changing load conditions, assigning more resources to high-demand instances and fewer to low-demand instances, thereby maintaining processing efficiency while supporting system scaling.
Solution Approach 2:
The system changes the allocation parameters of hardware resources based on monitored metrics. By adjusting the number of database connections and other resource parameters dynamically according to each application instance's load, the system optimizes resource utilization and prevents the slowdown that would occur with static or uniform resource distribution.
2Adaptability or versatility
If more application instances are added to handle increased demand, then the system becomes more adaptable, but the hardware resource operates at declining efficiency due to excessive requests
Solution Approach 1:
The resource pooling application implements a feedback mechanism where it monitors performance metrics from application instances and uses this information to adjust resource allocations. This closed-loop feedback system ensures that resource distribution responds to actual demand conditions, maintaining hardware operation stability and preventing the decline in efficiency that would result from unmonitored scaling.
3Device complexity
If a resource pooling application is used to manage database connections, then resource sharing is enabled, but scaling the pooling application itself increases the number of requests to hardware resources
Solution Approach 1:
The resource pooling application performs preliminary actions by proactively monitoring metrics and calculating optimal resource allocations before hardware resource requests are made. By pre-calculating and assigning resources based on predicted or current load patterns, the system reduces the number of actual requests to hardware resources and optimizes their utilization, thereby maintaining productivity despite the presence of the pooling application.
Data Source
AI summary
A device, system and method for assigning portions of a global resource limit to application engines based on relative load is provided. A system comprises a plurality of application engines that share a global resource limit; and a plurality of operator engines. The plurality of operator engines are each configured to: monitor a respective metric representative of respective load at a respective application engine; share the respective metric with others of the plurality of operator engines; determine a relative load at the respective application engine based on the respective metric and respective metrics received from the others of the plurality of operator engines; and assign a portion of the global resource limit to the respective application engine based on the relative load.