Trace-Driven Analysis of Power Proportionality in Storage Systems
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Trace-Driven Analysis of Power Proportionality in Storage SystemsSara Alspaugh and Arka Bhattacharya
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Why trace-driven analysis
• Lots of published proposals
• Giant design space
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Some related workScheme Block
Device / RAID Level
File System Level
Fixed Thresh-hold
Pred-ictive
Erasure Codes (RAID5)
Mirror-ing (RAID1)
Write Logging
Access Freq.-Based Layout
Solid State Devices
Multi-speed Disks
Hybrid / Tiered
DIV-ACC X X X X
EERAID1 X X X X
EERAID5 X X X X X
RIMAC X X X X
PARAID X X X X
PDC X X X
PA-LRU X X X
PB-LRU X X X X
HIBERN X X X X X X
DPRM X X X X
WOL X X X X X X
MAID X X X X
SSD-RAID X X X X X X
EED X X X X X X
SIERRA X X X X
RABBIT X X X X X
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Method
EvaluationLaboratory Production Implementation is
infeasible when considering many system types.
AnalysisComponents
Traces
Algorithms
?
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Trace Type Citation
Wikipedia HTTP SOCC ‘10NetApp, Harvard NFS USENIX ‘08, LISA ‘03MSR Cambridge Block Device FAST ‘08Facebook Analytics Hadoop MapReduce EuroSys ‘11Google Web Search ISCA ‘11
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AnalysisComponents
Traces
Algorithms
CharacteristicsRequest RateInterarrival TimesRead-Write Mix...
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Quantifying Inherent Opportunity• gain =
diff(peak x length, sum(bandwidth)) /peak x length
• waste factor = peak x length / sum(bandwidth)
• waste factor = peak:avg
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time
band
widt
h
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time
band
widt
h
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data set size (B)
band
widt
h re
quire
men
ts
(B/s
)
data set size (B)
band
widt
h re
quire
men
ts (B
/s)
data set size (B)ba
ndwi
dth
requ
irem
ents
(B
/s)
data set size (B)
band
widt
h re
quire
men
ts
(B/s
)
bw_app >> bw_componentcap_app < cap_component
bw_app <= bw_{components}cap_app >> cap_component
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unit = disks
Band
widt
h (b
ytes
/ se
c )
Capacity (bytes)
partition
replicate
~ 500 GB
~ 50 MB/s
laptop NFS filer
DB server
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unit = servers
band
widt
h
bytes
partition
replicate
~ 12 TB (disk)
memory cache
DFS
~ 32 GB (RAM)
DB server
~ 200 MB/s
~ 1 GB/s
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data set size (B)ba
ndwi
dth
requ
irem
ents
(B/s
)
data set size (B)
band
widt
h re
quire
men
ts
(B/s
)
data set size (B)
band
widt
h re
quire
men
ts
(B/s
)
NAS / NFS (NetApp), disk arrays
web farms (Wikipedia)
data analytics, DFS (Hadoop)
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data set size (B)
band
widt
h re
quire
men
ts
(B/s
)
data set size (B)
band
widt
h re
quire
men
ts (B
/s)
data set size (B)ba
ndwi
dth
requ
irem
ents
(B
/s)
data set size (B)
band
widt
h re
quire
men
ts
(B/s
)
bw_app >> bw_componentcap_app < cap_component
bw_app <= bw_{components}cap_app >> cap_component
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Challenges• Case 1: writes• Case 2: latency to inactive
components• Case 3: both of the above, set cover
problem
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write through: to all components (even if requires waking some)
write offloading: to active components only (propagate on wake)
write log: propagate when ~full reaper: to all components but only wake when queue is full
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time
band
widt
h
requests
active units write-offloading
active units write-through
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Next steps• data not pictured
here– latencies– ramp times– unit sizes– etc.
• ways to slice it• how to visualize it
• more workloads• go back to related
work to compare• case 3– object popularity
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QUESTIONS?The End.