ARC: A SELF-TUNING, LOW OVERHEAD REPLACEMENT CACHEIntroduction (I)Introduction (II)Our Model (I)Our Model (II)Previous Work (I)Previous Work (II)ExamplePrevious Work (III)ARC (I)ARC (II)ARC (III)ARC (IV)ARC (V)Experimental ResultsARC: A SELF-TUNING,LOW OVERHEAD REPLACEMENT CACHENimrod Megiddo Dharmendra S. ModhaIBM Almaden Research CenterIntroduction (I)•Caching is widely used in–storage systems–databases –web servers –processors –file systems –disk drives–RAID controllers – operating systemsIntroduction (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 workloadsOur Model (I)Cache/Main Memory(pages)Secondary Storage(pages)on demandreplacement policyOur 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 policyPrevious 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 modelPrevious 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 problemExampleTimeLast two references to pages A and BXAXBXAXBLRU expels B because A was accessed after this last reference to BLRU -2 expels A because B was accessed twice after this next to last reference to APrevious 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 parameterARC (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 cacheARC (II)L-1T1B1T2B2|T1| + |T2| = c“Ghost caches”(not in memory)L-2ARC (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 otherwiseARC (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)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!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
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