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Trace-Driven Analysis of Power Proportionality in Storage Systems. Sara Alspaugh and Arka Bhattacharya. Why trace-driven analysis. Lots of published proposals Giant design space. Some r elated work. Method. Laboratory. Production. - PowerPoint PPT Presentation
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Trace-Driven Analysis of Power Proportionality in Storage SystemsSara Alspaugh and Arka Bhattacharya
Why trace-driven analysis
• Lots of published proposals
• Giant design space
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
Method
EvaluationLaboratory Production Implementation is
infeasible when considering many system types.
AnalysisComponents
Traces
Algorithms
?
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
AnalysisComponents
Traces
Algorithms
CharacteristicsRequest RateInterarrival TimesRead-Write Mix...
Quantifying Inherent Opportunity• gain =
diff(peak x length, sum(bandwidth)) /peak x length
• waste factor = peak x length / sum(bandwidth)
• waste factor = peak:avg
time
band
widt
h
time
band
widt
h
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
unit = disks
Band
widt
h (b
ytes
/ se
c )
Capacity (bytes)
partition
replicate
~ 500 GB
~ 50 MB/s
laptop NFS filer
DB server
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
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)
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
Challenges• Case 1: writes• Case 2: latency to inactive
components• Case 3: both of the above, set cover
problem
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
time
band
widt
h
requests
active units write-offloading
active units write-through
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
QUESTIONS?The End.