Smart Polling Frequency for Datastore Refresh
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Solution Overview
Problem
Current polling operations face challenges such as unpredictable detection of data changes, increased computing resource loads, network traffic, and scalability issues, particularly in scenarios like OpenStack-based applications where frequent polling of entire datasets leads to usability and scalability problems.
Innovation Solution
Implementing smart polling frequencies in datastores that are dynamically adjusted based on recent system activity and datastore characteristics, increasing frequencies for volatile data and decreasing them when scalability issues arise, with the ability to temporarily change frequencies based on user actions, environmental factors, and predefined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent polling operations are performed to refresh datastores, then data freshness is improved, but computing resource load and network traffic increase
Solution Approach 1:
The patent implements dynamic polling frequencies that automatically adjust based on system conditions. When computing resource load is high or network traffic is heavy, the polling frequency is reduced. When resources are abundant and data changes frequently, the frequency increases. This dynamic adaptation resolves the contradiction by making the polling rate flexible rather than fixed, allowing the system to optimize between data freshness and resource consumption in real-time.
Solution Approach 2:
The system changes the polling frequency parameter based on monitored system conditions such as resource availability, network state, and data volatility. By adjusting this key parameter dynamically, the system can achieve high data freshness when needed while reducing resource load during normal operations, effectively resolving the technical contradiction between measurement precision and energy use.
2Speed
If polling frequency is increased to detect data changes quickly, then detection speed is improved, but system scalability deteriorates
Solution Approach 1:
The patent applies dynamic adjustment of polling frequencies to enable the system to scale. Different datastores can have different polling frequencies assigned based on their characteristics and system conditions. This allows the system to maintain fast detection speed for critical datastores while using lower frequencies for less critical ones, thereby achieving both high detection speed and system scalability simultaneously.
3Device complexity
If uniform polling frequency is applied to all datastores, then system simplicity is maintained, but resource efficiency decreases
Solution Approach 1:
The patent implements local quality by assigning different polling frequencies to different datastores based on their specific characteristics such as data volatility, criticality, and access patterns. Instead of a uniform approach, each datastore receives a customized polling frequency that optimizes resource usage for that specific data source while maintaining overall system functionality. This resolves the contradiction by making the system slightly more complex in configuration but dramatically more efficient in resource usage.
Data Source
AI summary
For smart polling frequency in datastores by a processor device in a computing environment, individual polling operations are performed for refreshing each one of the datastores according to polling frequencies. Each one of the polling frequencies depends on recent system activity and each one of the datastores. Each of the polling frequencies are dynamically adjusted for each one of the datastores.