6e8a742705
Introduce a new simulation for Age of Information (AoI) cache management, focusing on varying TTL values and eviction strategies (LRU and Random Eviction). This includes: - New Python script for event-driven cache simulations using . - Experiments for "No Refresh" across multiple TTL configurations (, , ..., ) with: - Hit rate and object age tracking (, , etc.). - Visualizations (e.g., , ). - Updated to describe experimental setup and configurations. - Log export file () for simulation results. - Refactor of with detailed strategy configurations and runtime notes. ### Reason The commit enhances the project by enabling detailed experiments with configurable cache parameters, supporting analysis of cache efficiency and AoI under varying conditions. This provides a foundation for more sophisticated simulations and insights. ### Performance - Runtime: ~4m 29s for . Co-authored experiments introduce structured data files and visualizations, improving clarity for future iterations. Signed-off-by: Tuan-Dat Tran <tuan-dat.tran@tudattr.dev>
16 lines
743 B
Markdown
16 lines
743 B
Markdown
# Experiments: No Refresh with variable TTL
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Explanation for files in each experiment:
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- `details.csv`: Access Count, Hit, Miss, Mu, Lambda and Hit Rate for each object
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- `hit_age.csv`: Shows hit rate/average age at time of request for each object.
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- `lambda_distribution.pdf`: Lambda Distribution across all objects/discrete
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values of the Zipf distribution
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- `objects_in_cache_over_time.pdf`: Amount of cache entries at given time.
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- `overall_hit_age.csv`: Cumulative description of `hit_age.csv`
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Length of simulation doesn't change much since we're not touching the request
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rate across the simulations.
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Break condition for the simulation is when the each database object has been
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requested at least `ACCESS_COUNT_LIMIT` (i.e. 10) times.
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