Polling Parameter Adjustment for Software Adapter Queue Balancing
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Solution Overview
Problem
In data transfer using a pull model, existing software adapters struggle to dynamically adjust polling parameters based on the rate of event consumption and generation, leading to inefficient data flow and potential queue accumulation.
Innovation Solution
A method and system that dynamically adjust polling parameters by comparing the number of events in the listening application's queue with the endpoint application's queue, allowing for real-time adjustments in polling quantity and frequency to optimize data transfer efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the software adapter polls the EIS at regular intervals using fixed polling parameters, then the data transfer schedule is simple and predictable, but the data transfer efficiency deteriorates when event consumption rate changes
Solution Approach 1:
The patent applies dynamics by transitioning from fixed polling parameters to dynamic polling parameters that automatically adjust based on real-time queue depth measurements. The polling frequency and quantity are modified according to the actual event consumption rate and generation rate, allowing the system to adapt to changing conditions while maintaining high data transfer efficiency.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring queue depths at both the EIS and the listening application, comparing these values to determine event consumption patterns, and using this information to adjust polling parameters. This closed-loop feedback ensures optimal polling behavior that responds to actual system conditions.
2Productivity
If the polling quantity is increased to retrieve more events per poll cycle, then the data transfer throughput improves, but the queue accumulation risk increases
Solution Approach 1:
The system uses feedback from real-time queue depth measurements to dynamically adjust polling quantity. By continuously monitoring the number of events in the EIS queue and listening application queue, the system can increase polling quantity when queues are shallow (improving throughput) and decrease it when queues are deep (preventing accumulation), thus balancing both objectives.
Solution Approach 2:
The patent applies parameter changes by modifying the polling quantity parameter based on the ratio of queue depths. When the EIS queue is shallow relative to the listening application queue, the system increases polling quantity to maximize throughput. Conversely, when the EIS queue is deep, the system reduces polling quantity to prevent excessive accumulation, thereby dynamically optimizing both throughput and accumulation risk.
3Measurement precision
If the polling frequency is increased to retrieve events more frequently, then the data freshness improves, but the system resource consumption increases
Solution Approach 1:
The system dynamically adjusts polling frequency based on real-time queue depth conditions. When the EIS queue is shallow and event generation is high, the system increases polling frequency to maintain data freshness. When the queue is deep or event generation is low, the system reduces polling frequency to minimize resource consumption, thus optimizing the trade-off between freshness and energy usage.
Solution Approach 2:
The patent implements parameter changes in polling frequency based on the ratio of event generation rate to consumption rate. By monitoring queue depth changes over time, the system can increase frequency when events are being generated faster than consumed (maintaining freshness) and decrease frequency when consumption exceeds generation (reducing resource waste), thereby adaptively optimizing both data freshness and energy consumption.
Data Source
AI summary
An approach, executed by a computer, includes receiving at least an initial polling quantity and an initial polling frequency and polling an endpoint application using the initial polling quantity and the initial polling frequency. The approach includes determining a first number of events not consumed in a queue of a listening application and a second number of events generated at the endpoint application and remaining in a queue at the endpoint application and comparing the number of events in each queue. The approach includes adjusting at least one of the initial polling quantity and the initial polling frequency based, at least in part, on the comparison of the first number of events not consumed in the queue of the listening application and the second number of events generated at the endpoint application and remaining in the queue at the endpoint application.