79 lines
1.4 KiB
Markdown
79 lines
1.4 KiB
Markdown
# Age Caching Simulation
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Client -> TTL Cache -> Database
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Capacity C (C = n (example: 100))
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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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Hit rate and the average age of the object based on TTL
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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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1.
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- Do Refresh
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- Do Request
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2.
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- Just Request
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- No Refresh
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3.
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- LRU
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- Infinite TTL
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- No Refresh
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4.
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- Random eviction
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- Regular TTL
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- With Refresh
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5.
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- Random eviction
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- Regular TTL
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- Without Refresh
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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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for ACCESS_COUNT_LIMIT = 10_000 # Total time to run the simulation |