1. ARC: A SELF-TUNING, LOW OVERHEAD REPLACEMENT CACHE
Nimrod Megiddo Dharmendra S. Modha
IBM Almaden Research Center

2. Introduction (I) Caching is widely used in storage systems databases
web servers
processors
file systems
disk drives
RAID controllers
operating systems

3. Introduction (II) ARC is a new cache replacement policy:
Scan-resistant:
Better than LRU
Self-tuning:
Avoids problem of many recent cache replacement policies
Tested on numerous workloads

4. Our Model (I) Cache/ Main Memory (pages) Secondary Storage (pages)
on demand
replacement policy
Secondary Storage
(pages)

5. Our Model (II) Caches stores uniformly sized items (pages)
On demand fetches into cache
Cache expulsions decided by cache replacement policy
Performance metrics include
Hit rate (= 1 - miss rate)
Overhead of policy

6. Previous Work (I)
Offline Optimal (MIN): replaces the page that has the greatest forward distance
Requires knowledge of future
Provides an upper-bound
Recency (LRU):
Most widely used policy
Frequency (LFU):
Optimal under independent reference model

7. Previous Work (II)
LRU-2: replaces page with the least recent penultimate reference
Better hit ratio
Needs to maintain a priority queue
Corrected in 2Q policy
Must still decide how long a page that has only been accessed once should be kept in the cache
2Q policy has same problem

8. LRU expels B because A was accessed after this last reference to B
Example
Last two references to pages A and B X A
X B
X B X A
Time
LRU -2 expels A because B was accessed twice after this next to last reference to A
LRU expels B because A was accessed after this last reference to B

9. Previous Work (III) Low Inter-Reference Recency Set (LIRS)
Frequency-Based Replacement (FBR)
Least Recently/Frequently Used(LRFU): subsumes LRU and LFU
All require a tuning parameter
Automatic LRFU (ALRFU)
Adaptive version of LRFU
Still requires a tuning parameter

10. ARC (I) Maintains two LRU lists
Pages that have been referenced only once (L1)
Pages that have been referenced at least twice (L2)
Each list has same length c as cache
Cache contains tops of both lists: T1 and T2
Bottoms B1 and B2 are not in cache

11. ARC (II) L-1 L-2 T1 T2 “Ghost caches” (not in memory) B1 B2
|T1| + |T2| = c
“Ghost caches”
(not in memory) B2

12. ARC (III) ARC attempts to maintain a target size target_T1 for list T1
When cache is full, ARC expels
LRU page from T1 if
|T1|  target_T1
LRU page from T2 otherwise

13. ARC (IV)
If missing page was in bottom B1 of L-1, ARC increases target_T1
target_T1= min(target_T1+max(|B2|/|B1|,1),c)
If missing page was in bottom B2 of L-2, ARC decreases target_T1
target_T1= max(target_T1-max(|B1|/|B2|,1),0)

14. ARC (V) Overall result is Two heuristics compete with each other
Each heuristic gets rewarded any time it can show that adding more pages to its top list would have avoided a cache miss
Note that ARC has no tunable parameter
Cannot get it wrong!

15. Experimental Results Tested over 23 traces: Always outperforms LRU
Performs as well as more sophisticated policies even when they are specifically tuned for the workload
Sole exception is 2Q
Still outperforms 2Q when 2Q has no advance knowledge of the workload characteristics.