ARC:%20A%20SELF-TUNING,%20LOW%20OVERHEAD%20REPLACEMENT%20CACHE

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ARC A SELF-TUNING,LOW OVERHEAD REPLACEMENT CACHE

• Nimrod Megiddo Dharmendra S. Modha
• IBM Almaden Research Center

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Introduction (I)

• Caching is widely used in
• storage systems
• databases
• web servers
• processors
• file systems
• disk drives
• RAID controllers
• operating systems

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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

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

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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

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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

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

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ARC (I)

• Maintains two LRU lists
• Pages that have been referenced only once (L1)
• Pages that have been referencedat 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

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ARC (II)
L-2
T2
T1 T2 c
Ghost caches (not in memory)
B2
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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

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ARC (IV)

• If missing page was in bottom B1 of L-1, ARC
  increases target_T1
• target_T1 min(target_T1max(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)

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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!

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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.