Application Preloading via Time-Interval Priority Lists
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Solution Overview
Problem
Existing application preloading methods based on context information face validity issues due to frequent changes in context, requiring frequent updates and incurring overhead, which affects the efficiency of application execution in electronic devices.
Innovation Solution
An electronic device determines priority lists for application preloading based on usage history across multiple time intervals, preloading applications from specific lists during designated conditions such as boot completion, cycle arrival, or memory state changes, optimizing the number of preloaded applications according to memory capacity and usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If application preloading is performed based on context information, then application execution speed is improved, but preloading validity decreases due to frequent context changes
Solution Approach 1:
The time period is divided into multiple discrete time intervals, with separate priority lists created for each interval. This segmentation allows the system to maintain specialized preloading strategies for different time periods, improving both execution speed and validity by selecting applications appropriate to each specific time interval rather than using a single static list.
Solution Approach 2:
The system dynamically generates different priority lists for different time intervals based on historical usage patterns. Instead of using a fixed preloading strategy, the priority lists adapt to temporal changes in application usage, maintaining reliability while improving execution speed by loading the most relevant applications for each time period.
2Measurement precision
If priority lists are frequently updated to reflect context changes, then preloading accuracy is improved, but system overhead increases
Solution Approach 1:
The system updates priority lists periodically at the boundaries of time intervals rather than continuously responding to every context change. This periodic update mechanism maintains preloading accuracy by reflecting temporal usage patterns while significantly reducing system overhead compared to continuous updates.
Solution Approach 2:
The system performs preliminary analysis of application usage history to generate priority lists in advance for each time interval. By pre-calculating and storing these lists before they are needed, the system achieves high preloading accuracy without incurring overhead during actual preloading operations, as the prioritization work is done beforehand.
Data Source
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AI summary
An embodiment discloses an electronic device including: a first memory in which multiple applications are stored; a second memory; and at least one processor operatively connected to the first memory and the second memory. The processor(s) is configured to determine, based on a history of usage of the multiple applications for a first period of time, a priority of the multiple applications over multiple time intervals included in a second period of time. The processor(s) is further configured to preload a predetermined first number of applications into the second memory based on the priority if a designated condition being satisfied; and preload a first list of applications into the second memory if the designated condition is satisfied in a first time interval, and preload a second list of applications into the second memory if the designated condition is satisfied in a second time interval.