lirs : an efficient replacement policy to improve buffer cache performance song jiang 1 and xiaodong...
TRANSCRIPT
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LIRS : An Efficient Replacement Policy
to Improve Buffer Cache Performance
Song Jiang1 and Xiaodong Zhang1,2
1College of William and Mary2National Science Foundation
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The Problem of LRU Replacement
• File scanning: one-time accessed blocks are not replaced timely;
• Loop-like accesses: blocks to be accessed soonest can be unfortunately replaced;
• Accesses with distinct frequencies: Frequently accessed blocks can be unfortunately replaced.
Inability to cope with weak access locality
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Why does LRU Fail Sometimes?
• A recently used block is not necessarily to be
used again soon.
• Can not deal with working set larger than
available cache size
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LRU Merits
• Simplicity: affordable implementation
• Adaptability: responsive to access pattern changes
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Our Objectives
• Address the limits of LRU fundamentally.
• Retain the low overhead and adaptability merits of LRU.
Significant efforts have been made to improve LRU, but
• Case by case; or
• High runtime overhead
Our objectives:
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Outline
• Related Work
• The LIRS Algorithm
• LIRS Implementation Using LRU Stack
• Performance Evaluation
• Sensitivity and Overhead Analysis
• Conclusions
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Related Work
• Aided by user-level hints
• Detection and adaptation of access regularities
• Tracing and utilizing deeper history information
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User-level Hints
• Application-controlled file caching [Cao et al, USENIX’94]
• Application-informed prefetching and caching [Patterson et al, SOSP’96]
Rely on users’ understanding of data access patterns
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Detection and Adaptation of Regularities• SEQ: sequential access pattern detection
[Glass et al, Sigmetrics’97]
• EELRU: on-line analysis of aggregate recency distributions of referenced blocks [Smaragdakis et al, Sigmetrics’97]
• DEAR: detection of multiple block reference patterns [Choi et al, USENIX’99]
• AFC: Application/File-level Characterization [Choi et al, Sigmetrics’00]
• UBM: Unified Buffer Management [Kim et al, OSDI’00]
Case-by-case oriented approaches
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Tracing and Utilizing Access History
• LRFU: combine LRU and LFU [Lee et al, Sigmetrics’99]
• LRU-K: replacement decision based on the time of the Kth-to-last reference [ O'Neil et al, Sigmod’93]
• 2Q: use two queues to quickly remove cold blocks [Johnson et al, VLDB’94]
Either high implementation cost, or
workload dependent performance
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Outline
• Related Work
• The LIRS Algorithm
• LIRS Implementation Using LRU Stack
• Performance Evaluation
• Sensitivity and Overhead Analysis
• Conclusions
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Observation of Data Flow in LRU Stack
• Blocks are ordered by recency in the LRU stack;
• Blocks enter from stack top, and leave from its bottom;
A block evicted from the bottom of the stack should have been evicted much earlier !
1
6
32
5
LRU stack
.
.
.
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Inter-Reference Recency (IRR)
IRR of a block: number of other unique blocks accessed between two consecutive references to the block.
Recency: number of other unique blocks accessed from last reference to the current time.
1 2 3 4 3 1 5 6 5
IRR = 3
R = 2
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Principles of Our Replacement
If a block’s IRR is high, its next IRR is likely to be high again. We select the blocks with high IRRs for replacement .
Once IRR is out of date, we rely on the recency.
LIRS: Low Inter-reference Recency Set Replacement Policy We keep the blocks with low IRRs in cache.
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Basic LIRS Idea: Keep LIR Blocks in Cache Low IRR (LIR) block and High IRR (HIR) block
LIR block set
(size is Llirs )
HIR block set
Cache size
L = Llirs + LhirsLhirs
Llir
s
Physical CacheBlock Sets
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An Example for LIRS
Llirs=2, Lhirs=1
V time /Blocks
1 2 3 4 5 6 7 8 9 10 R IRR
A X X X 1 1
B X X 3 1
C X 4 inf
D X X 2 3
E X 0 inf
LIR block set = {A, B}, HIR block set = {C, D, E}
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C
D
E
HIR block set
A
B
A
B
E
LIR block set
Resident blocks
Mapping to Cache Block Sets
Lhirs=1
Llirs=2
Physical Cache
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D is referenced at time 10
V time /Blocks
1 2 3 4 5 6 7 8 9 10 R IRR
A X X X 1 1
B X X 3 1
C X 4 inf
D X X XX 0 3
E X 1 Inf
The resident HIR block (E) is replaced !
Which Block is replaced ? Replace a HIR Block
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V time /Blocks
1 2 3 4 5 6 7 8 9 10 R IRR
A X X X 2 1
B X X 3 1
C X 4 inf
D X X XX 0 2
E X 1 Inf
How LIR Set is Updated ? Recency of LIR Block Used
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V time /Blocks
1 2 3 4 5 6 7 8 9 10 R IRR
A X X X 2 1
B X X 3 1
C X 4 inf
D X X XX 0 2
E X 1 Inf
After D is Referenced at Time 10
E is replaced, D enters LIR set
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V time /Blocks
1 2 3 4 5 6 7 8 9 10 R IRR
A X X X 2 1
B X X 4 1
C X XX 0 4
D X X 3 3
E X 1 Inf
If Reference is to C at Time 10 . . . . . .
E is replaced, C can not enter LIR set
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The Power of LIRS Replacement
• File scanning: one-time accessed blocks will be replaced timely;
• Loop-like accesses: blocks to be accessed soonest will NOT be replaced;
• Accesses with distinct frequencies: Frequently accessed blocks will NOT be replaced.
Capability to cope with weak access locality
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Outline
• Related Work
• The LIRS Algorithm
• LIRS Implementation Using LRU Stack
• Performance Evaluation
• Sensitivity and Overhead Analysis
• Conclusions
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LIRS Efficiency: O(1)
Rmax
(Maximum Recency of LIR blocks)
IRR HIR
(New IRR of the
HIR block)
This efficiency is achieved by our LIRS stack.
LRU stack + LIR block with Rmax recency in its bottom ==> LIRS stack.
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Differences between LRU and LIRS Stacks
resident block
LIR block
HIR block
Cache size
L = 5
3216
5
LRU stack
53216948
LIRS stack
Llir = 3
Lhir =2
• Stack size of LRU decided by cache size, and fixed; Stack size of LIRS decided by LIR block with Rmax recncy, and varied.• LRU stack holds only resident blocks; LIRS stack holds any blocks whose recencies are no more than Rmax.
• LRU stack does not distinguish “hot” and “cold” blocks in it; LIRS stack distinguishes LIR and HIR blocks in it, and dynamically maintains their statues.
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Rmax (Maximum Recency of LIR blocks)
IRR HIR
(New IRR of the HIR block)
Blocks in the LIRS stack ==> IRR < Rmax
Other blocks ==> IRR > Rmax
LIRS Stack
How does LIRS Stack Help?
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LIRS Operations
resident in cache
LIR block
HIR block
Cache size
L = 5Llir =
3
Lhir =2
53216948
LIRS stack S
53
Resident HIR Stack Q
• Initialization: All the referenced blocks are given an LIR status until LIR block set is full.
We place resident HIR blocks in Stack Q
• Upon accessing a LIR block (a hit)
• Upon accessing a resident HIR block (a hit)
• Upon accessing a non-resident HIR block (a miss)
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Access a LIR block (a Hit)
53216948
S
53
Q
532169
4
8
S
53
Q
Access 4 Access 8
resident in cache
LIR block
HIR block
Cache size
L = 5Llir =
3
Lhir =2
5321
48
S
53
Q
69
S
d
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Access a HIR Resident block (a Hit)
5321
48
S
53
Q
Access 3 Access 5
1
348
S
5
Q
5
resident in cache
LIR block
HIR block
Cache size
L = 5Llir =
3
Lhir =2
3
1
48
S
5
Q
52
S
d
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Access a Non-Resident HIR block (a Miss)
Access 7
5
348
S
7
Q
7
5
1
348
S
5
Q
5resident in cache
LIR block
HIR block
Cache size
L = 5Llir =
3
Lhir =2
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Access a HIR Non-Resident block (a Miss) (Cont)
resident in cache
5 block number LIR block
HIR block
Cache size
L = 5Llir =
3
Lhir =2
Access 9
5
348
S
7
Q
7
5
7
348
S
9
Q
9
75
Access 5
4
S Q
8
9
87
5
3
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Outline
• Related Work
• The LIRS Algorithm
• LIRS Implementation Using LRU Stack
• Performance Evaluation
• Sensitivity and Overhead Analysis
• Conclusions
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Workload Traces
•cpp is a GNU C compiler pre-processor trace
• cs is an interactive C source program examination tool trace.
• glimpse is a text information retrieval utility trace.
• postgres is a trace of join queries among four relations in a relational
database system
• sprite is from the Sprite network file system
• mulit1 is obtained by executing two workloads, cs and cpp, together.
• multi2 is obtained by executing three workloads, cs, cpp, and
postgres, together.
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Representative Access patterns
• Looping references: all blocks are accessed repeatedly
with a regular interval;
• Temporally-clustered references: blocks accessed more
recently are the ones more likely to be accessed again soon.
• Probabilistic references: each block has a stationary
reference probability, and all blocks are accessed
independently with the associated probabilities.
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Cache Partition
• 1% of the cache size is for HIR blocks
• 99% of the cache size is for LIR blocks
• Performance is not sensitive to a partition.
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Looping Pattern: cs (Time-space map)
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Looping Pattern: cs (Hit Rates)
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Looping Pattern: postgres (Time-space map)
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Looping Pattern: postgres (Hit Rates)
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Looping Pattern: postgres (Hit Rates)
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Probabilistic Pattern: cpp (Time-space map)
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Probabilistic Pattern: cpp (Hit Rates)
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Temporally-Clustered Pattern: sprite (Time-space map)
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Temporally-Clustered Pattern: sprite (Hit Rates)
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Mixed Pattern: multi1 (Time-space map)
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Mixed Pattern: multi1 (Hit Rates)
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Mixed Pattern: multi2 (Time-space map)
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Mixed Pattern: multi2 (Hit Rates)
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Outline
• Related Work
• The LIRS Algorithm
• LIRS Implementation Using LRU Stack
• Performance Evaluation
• Sensitivity and Overhead Analysis
• Conclusions
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Sensitivity to the Change of Lhirs
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Sensitivity to the Change of Lhirs
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LIRS with Limited Stack Sizes
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LIRS with Limited Stack Sizes
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Conclusions
• Effectively use deeper access history without explicit
regularity detection and high cost operations.
• Outperform exiting replacement policies.
• Its implementation as simple as LRU.
• Applicable to virtual memory and database buffer
management.