# Hyperbolic Caching: Flexible Caching for Web Applications

### Presentation 

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* Multi-tier data center 
* Cache tier: e.x. redis or memcache 
  * Properties: web-like request pattern, various item cost, item expiration and so on 

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* Number of accesses: Frequency captures independent draws property of workloads 
* Time since i entered cache: Addresses problem of LFU by measuring relative popularity 

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Item requests: A A B C C 

* t = 1: pr(A) = 1 
* t = 2: pr(A) = 1 
* t = 3: pr(A) = 2/3, pr(B) = 1 
* t = 4, pr(A) = 2/4, pr(B) = 1/2, pr(C) = 1 
* t = 5, pr(A) = 2/5, pr(B) = 1/3, pr(C) = 2/2 = 1 

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* Static workload 

![](https://2097630930-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MVORxAomcgtzVVUqmws%2F-M_fIP7yx1wfCPSJsZIl%2F-M_fZwcAKNYmS14TzKWK%2Fimage.png?alt=media\&token=015ac5b2-801f-434d-8b3f-b7cd1770f89c)

* caching as a service company 
* 16 applications with cache sizes chosen by the application developers 
  * Only a few which Hyperbolic performs worse 

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* item costs 
  * CPU / DB load: experience a miss, cause the item is the result of the join rather than simple look up 

![](https://2097630930-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MVORxAomcgtzVVUqmws%2F-M_fIP7yx1wfCPSJsZIl%2F-M_f_zdWuOYhhFsWkTJb%2Fimage.png?alt=media\&token=a9e9a403-33e8-4047-8a5f-2f52e5d6b5a4)

* GreedyDual 
  * Incorporating cost into LRU 

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* Stress one of the backend and measure tail latency 
* Tracking costs per item and per class 
  * Load one of the backend, takes time to adjust to the knowledge --> spike in latency that is not experienced by per-class 

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