Power-efficient server provisioning in server farms
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Transcript of Power-efficient server provisioning in server farms
Power-efficient server provisioning in server farms
Anshul Gandhi (Carnegie Mellon University)
Varun Gupta (CMU), Mor Harchol-Balter (CMU)Michael Kozuch (Intel, Pittsburgh)
Motivation
Server farms are important for today’s IT infrastructure (Amazon, Google, IBM, HP, …)
However, server farms cost a lot of money to power ($4 billion in 2006)
Server FarmRequests
High-level problem statement
How many servers, given request rate ? Don’t want to waste power
RequestsServer Farm
Outline
1. Server farm model
2. Provisioning for fixed arrival rate
3. Provisioning for unpredictable, time-varying arrival rate
4. Future work
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Server farms
IDLE servers consume a lot of power
~ 60 % of BUSY
BUSY
BUSY
BUSY
IDLE
IDLE
OFF
OFF
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Server farms
Turn IDLE servers OFF to save power
BUSY
BUSY
BUSY
OFF
OFF
OFF
OFF
HOWEVER
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Setup cost
To turn on an OFF server ..
BUSYOFF SETUP
Time delay (setup time)• 1 min – 5 mins
and
Power penalty • peak power during setup time
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Setup cost
To turn on an OFF server ..
BUSYOFF SETUP
Should we ever turn servers OFF ?
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Server model
Server states:BUSY PBUSY 240 WIDLE PIDLE 150 WOFF POFF 0 WSETUP PSETUP 240 W
Setup times:TOFF→ON 200 sTON→OFF 0 s
Intel Xeon E5320• 2 X 1.86 GHz quad-core• 4GB memory
ON
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Server farm model
Poisson arrival process: λ(t) requests/sec Exponentially distributed job sizes: E[S] secs Load: ρ(t) = λ(t) E[S]∙
Minimum # servers to handle incoming load
RequestsFCFS Server Farm
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Metric
Interested in response time and power conumption
Perf/W = 1/(Mean RT X Mean Power)
Maximize Perf/W
Outline
1. Server farm model
2. Provisioning for fixed arrival rate
3. Provisioning for unpredictable, time-varying arrival rate
4. Future work
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Provisioning for fixed arrival rate
Existing solutions: prediction based, reactive controllers.
Is there a simple, yet, near-optimal solution ?
Poisson arrivals
Server Farm
Max. Perf/W
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NEVEROFF
Keep n servers always ON (M/M/n) Servers are BUSY or IDLE
*n
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Perf/W for NEVEROFF
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INSTANTOFF
Turn servers OFF when IDLE Servers are BUSY, OFF or in SETUP
*n
Auto-scales if n is high
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Perf/W for INSTANTOFF
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NEVEROFF vs. INSTANTOFF
TON→OFF < γ E[S]/√ρ
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Near-optimality
Best of {NEVEROFF, INSTANTOFF} is optimal for single-server
Multi-server ?
For ρ > 10, we are within 20% of OPT
Outline
1. Server farm model
2. Provisioning for fixed arrival rate
3. Provisioning for unpredictable, time-varying arrival rate
4. Future work
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Unpredictable, time-varying demand
Data center demand has daily variations
INSTANTOFF can auto-scale
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Unpredictable, time-varying demand
NEVEROFF requires continual updates based on predicted load
Predictions are not always accurate
Can we find a simple traffic-oblivious policy? Auto-scaling in nature
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DELAYEDOFF
Like INSTANTOFF, except we wait for twait seconds before turning IDLE servers OFF
Routing ?
MRB routing is crucial !
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twait
Rule of thumb: twait P∙ IDLE = TOFF→ON P∙ ON
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Near-optimality
Worse at higher frequencies
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Auto-scaling capabilities
1998 World Cup Soccer trace (ITA)
Outline
1. Server farm model
2. Provisioning for fixed arrival rate
3. Provisioning for unpredictable, time-varying arrival rate
4. Future work
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Future work
Experimental evaluation of proposed schemes Preliminary experiments on 15-server testbed using
CPU-bound workload and sinusoidal arrival pattern Experimental results agree with analysis Web workloads:▪ What does the experimental setup look like ?
Try out various arrival traces and workloads
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Thank You!
Anshul Gandhi, Varun Gupta, Mor Harchol-Balter, Michael KozuchOptimality analysis of energy-performance trade-off for server farm management, PERFORMANCE 2010
Anshul Gandhi, Mor Harchol-Balter, Ivo AdanServer farms with setup costs, PERFORMANCE 2010
Anshul Gandhi, Varun Gupta, Mor Harchol-Balter, Michael KozuchEnergy-efficient dynamic capacity provisioning in server farms, CMU technical report CMU-CS-10-108