Preloading Virtual Devices to Reduce Connection Latency
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Solution Overview
Problem
Users of physical devices experience latency when accessing information or content associated with virtual devices due to processing and networking delays, which existing methods have not adequately addressed.
Innovation Solution
A system and method that preloads virtual devices in anticipation of connection requests, predicts optimal loading times based on connection patterns, and selects virtual device platforms with the lowest latency to reduce the time it takes to load and communicate information between physical and virtual devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If virtual devices are loaded on-demand when connection requests are received, then system resource usage is optimized, but user-perceived latency increases due to processing and loading delays
Solution Approach 1:
The system performs preliminary actions by preloading virtual devices into memory in anticipation of future connection requests. Connection patterns are analyzed to predict which virtual devices will be needed, and they are loaded beforehand, eliminating latency when users actually connect. This resolves the contradiction by accepting increased resource usage during idle periods in exchange for reduced latency during active use.
2Loss of time
If virtual devices are preloaded into memory in advance, then latency is reduced, but system memory consumption increases
Solution Approach 1:
The system applies partial preloading by loading only certain virtual devices into memory based on predicted connection patterns, rather than loading all virtual devices. The connection pattern analysis module identifies which virtual devices are most likely to be requested, and only those are preloaded. This partial action reduces memory consumption compared to full preloading while still achieving significant latency reduction for the most frequently accessed virtual devices.
3Measurement precision
If connection pattern analysis is performed to predict loading needs, then preloading accuracy improves, but system complexity increases
Solution Approach 1:
The connection pattern analysis module performs self-service by automatically analyzing historical connection data and identifying patterns without requiring manual configuration or intervention. The system autonomously determines which virtual devices should be preloaded based on the analyzed patterns, reducing the need for complex manual setup while maintaining high prediction accuracy. This self-service approach manages system complexity by automating the analytical process.
Data Source
AI summary
Latency experienced by a user of a client device may be reduced by preloading virtual devices in anticipation of a connection request from the client device. For example, a plurality of virtual devices may be partially loaded prior to a connection request from a client device. In response to the connection request from the client device, a user associated with the client device may be identified and user profile information associated with the user may be retrieved. Based on the retrieved user profile information associated with the user, a virtual device, from among the plurality of preloaded virtual devices, may be loaded, such that the loading of the virtual device is complete and is specific to the user of the client device.