Latency Injection for Consistent User Experience
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Solution Overview
Problem
Current systems experience unpredictable and variable latency, leading to a poor user experience, as users encounter fluctuations in latency even for identical scenarios, making it frustrating and affecting perceived performance.
Innovation Solution
The technique involves calculating and selecting latencies from various transactions to inject a consistent latency, either by averaging, using the highest latency, or other aggregate statistics, to smooth out latency variance, thereby improving the perceived performance by making latency more predictable and consistent across multiple transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If actual network latency is reduced, then response speed improves, but latency variability increases leading to unpredictable performance
Solution Approach 1:
The patent introduces an intermediary latency injection mechanism that mediates between the actual network latency and the perceived user experience. By inserting artificial delays based on historical latency patterns, the system creates a buffer that smooths out variability while maintaining overall responsiveness. The intermediary layer decouples the actual network performance from the user-perceived performance, allowing the system to hide latency fluctuations.
Solution Approach 2:
The system dynamically changes the latency parameter by injecting artificial delays that are calculated based on historical latency data. Instead of maintaining a fixed latency or directly optimizing for minimum latency, the system adjusts the perceived latency parameter in real-time to achieve consistency. This parameter transformation converts variable actual latency into consistent perceived latency.
2Reliability
If latency is made consistent through injection, then perceived performance improves, but actual response time increases
Solution Approach 1:
The system applies partial latency injection rather than full compensation. Instead of adding delays for every single transaction, the system selectively injects latency based on historical patterns and thresholds. The latency injection is excessive only when needed to compensate for variability, and partial otherwise, thereby minimizing overall time loss while achieving consistency where it matters most.
Solution Approach 2:
The system performs preliminary analysis of historical latency patterns before injecting latency into actual transactions. By pre-calculating expected latency variations and preparing compensation strategies in advance, the system can inject latency proactively rather than reactively. This preliminary action allows the system to smooth latency variability without adding excessive delays to each individual transaction.
3Ease of operation
If latency injection is applied, then user experience becomes more predictable, but system complexity increases
Solution Approach 1:
The system implements self-service latency management by automatically analyzing its own historical performance data and making decisions about when and how much latency to inject. The latency injection mechanism monitors its own output and adjusts parameters without external intervention. This self-service approach reduces the need for complex external control systems while maintaining predictable performance.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor actual latency measurements and compare them against target latency patterns. Based on this feedback, the system dynamically adjusts latency injection parameters to maintain consistency. The feedback mechanism simplifies control by using reactive adjustments based on actual performance rather than requiring complex predictive models.
Data Source
AI summary
Techniques described herein provide a system and methods for adding latency to improve the perceived performance of computing devices. For instance, the techniques may calculate transaction latencies for a given session based on the time between sending requests for content and receiving the requested content. The calculated latencies may be aggregated or averaged in such a way that a specific latency may be selected for improving the perceived performance. A client device may then render subsequent content based on the selected latency or a server may serve subsequent content based on the selected latency. This artificial addition of latency may enhance the user experience by creating a more consistent environment.