feat: add cache simulation with experiments for TTL and eviction strategies

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>

experiments/README.md

@@ -0,0 +1,15 @@
+# Experiments: No Refresh with variable TTL
+
+Explanation for files in each experiment:
+
+- `details.csv`: Access Count, Hit, Miss, Mu, Lambda and Hit Rate for each object
+- `hit_age.csv`: Shows hit rate/average age at time of request for each object.
+- `lambda_distribution.pdf`: Lambda Distribution across all objects/discrete
+  values of the Zipf distribution
+- `objects_in_cache_over_time.pdf`: Amount of cache entries at given time.
+- `overall_hit_age.csv`: Cumulative description of `hit_age.csv`
+
+Length of simulation doesn't change much since we're not touching the request
+rate across the simulations.
+Break condition for the simulation is when the each database object has been
+requested at least `ACCESS_COUNT_LIMIT` (i.e. 10) times.