1.4 KiB
Age Caching Simulation
Client -> TTL Cache -> Database Capacity C (C = n (example: 100))
TTL increases on cache hit Age of information / Age of the entry in the cache Database has latest object, cache entry may be old (we don't know)
Age of entry should have low age of information
Update function from cache to refresh based on mu (refresh rate)
Loss function based on TTL and age in cache called beta(i)
Event based simulation
lambda(i) is zipf distribution describing the rate the client requests the object "i"
Inter arrival time of each object => exponential
Hit rate and the average age of the object based on TTL
Notes 11/13/2024
Limitations of time
Run the simulation not time based but based on when the least ranked object is requested at least times for example. Least ranked object -> least zipf value
2-3h
mu
Simulate lambda and mu to see what increases the cost function
Bandwidth
Finite bandwidth between cache and server miss requests and cache updates should not go over the bandwidth
Two versions
- Do Refresh
- Do Request
- Just Request
- No Refresh
- LRU
- Infinite TTL
- No Refresh
- Random eviction
- Regular TTL
- With Refresh
- Random eviction
- Regular TTL
- Without Refresh
Runtime
CPU times: user 3min 46s, sys: 43 s, total: 4min 29s Wall time: 4min 29s for ACCESS_COUNT_LIMIT = 10_000 # Total time to run the simulation