2024-11-13 12:00:18 +01:00
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# Age Caching Simulation
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2024-11-08 21:38:45 +01:00
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Client -> TTL Cache -> Database
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2024-11-13 12:00:18 +01:00
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Capacity C (C = n (example: 100))
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2024-11-08 21:38:45 +01:00
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TTL increases on cache hit
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Age of information / Age of the entry in the cache
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Database has latest object, cache entry may be old (we don't know)
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Age of entry should have low age of information
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Update function from cache to refresh based on mu (refresh rate)
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Loss function based on TTL and age in cache called beta(i)
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Event based simulation
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lambda(i) is zipf distribution describing the rate the client requests the object "i"
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Inter arrival time of each object => exponential
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2024-11-13 12:00:18 +01:00
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Hit rate and the average age of the object based on TTL
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2024-11-13 16:46:36 +01:00
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## Notes 11/13/2024
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### Limitations of time
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Run the simulation not time based but based on when the least ranked object is requested
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at least times for example.
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Least ranked object -> least zipf value
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2-3h
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### mu
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Simulate lambda and mu to see what increases the cost function
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### Bandwidth
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Finite bandwidth between cache and server
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miss requests and cache updates should not go over the bandwidth
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### Two versions
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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>
2024-11-27 16:31:46 +01:00
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- [x] Default
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2024-11-13 16:46:36 +01:00
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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>
2024-11-27 16:31:46 +01:00
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- Do Refresh
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- Do Request
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2024-11-13 16:46:36 +01:00
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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>
2024-11-27 16:31:46 +01:00
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- [x] No Refresh
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2024-11-13 16:46:36 +01:00
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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>
2024-11-27 16:31:46 +01:00
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- Just Request
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- No Refresh
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2024-11-13 16:46:36 +01:00
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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>
2024-11-27 16:31:46 +01:00
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- [x] Infinite TTL
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2024-11-13 16:46:36 +01:00
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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>
2024-11-27 16:31:46 +01:00
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- LRU
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- Infinite TTL
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- No Refresh
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2024-11-13 16:46:36 +01:00
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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>
2024-11-27 16:31:46 +01:00
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- [x] Random Eviction
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2024-11-13 16:46:36 +01:00
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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>
2024-11-27 16:31:46 +01:00
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- Random eviction
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- Regular TTL
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- With Refresh
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2024-11-13 16:46:36 +01:00
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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>
2024-11-27 16:31:46 +01:00
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- [x] Random Eviction w/o Refresh
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- Random eviction
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- Regular TTL
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- No Refresh
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2024-11-13 16:46:36 +01:00
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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>
2024-11-27 16:31:46 +01:00
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| Name | Cache Capacity | MAX_REFRESH_RATE | cache_type | CACHE_TTL |
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| -------------------- | -------------------- | ---------------- | ------------------------- | --------- |
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| Default | DATABASE_OBJECTS | 1< | CacheType.LRU | 5 |
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| No Refresh | DATABASE_OBJECTS | 0 | CacheType.LRU | 5 |
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| Infinite TTL | DATABASE_OBJECTS / 2 | 0 | CacheType.LRU | 0 |
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| Random Eviction (RE) | DATABASE_OBJECTS / 2 | 1< | CacheType.RANDOM_EVICTION | 5 |
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| RE without Refresh | DATABASE_OBJECTS / 2 | 0 | CacheType.RANDOM_EVICTION | 5 |
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2024-11-13 16:46:36 +01:00
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### Runtime
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CPU times: user 3min 46s, sys: 43 s, total: 4min 29s
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Wall time: 4min 29s
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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>
2024-11-27 16:31:46 +01:00
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for ACCESS_COUNT_LIMIT = 10_000 # Total time to run the simulation
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2024-11-29 21:49:59 +01:00
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## Notes 11/29/2024
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C_m = cost for cache miss
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C_delta = cost for refresh
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C = Cm + C_delta over all objects summarized
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We wanna minimize cost function
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N = number of objects
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B is cache size
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C_f roughly equals C_m
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