# 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

1.

- Do Refresh
- Do Request

2.

- Just Request
- No Refresh

3.

- LRU
- Infinite TTL
- No Refresh

4.

- Random eviction
- Regular TTL
- With Refresh

5.

- 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