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Anshul Gandhi (Carnegie Mellon University) Varun Gupta (CMU), Mor Harchol-Balter (CMU) Michael Kozuch (Intel, Pittsburgh). Power-efficient server provisioning in server farms. Motivation. Server farms are important for today’s IT infrastructure (Amazon, Google, IBM, HP, …)

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slide1
Anshul Gandhi (Carnegie Mellon University)

Varun Gupta (CMU), Mor Harchol-Balter (CMU)

Michael Kozuch (Intel, Pittsburgh)

Power-efficient server provisioning in server farms

motivation
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 Farm

Requests

high level problem statement
High-level problem statement
  • How many servers, given request rate ?
    • Don’t want to waste power

Server Farm

Requests

outline
Outline
  • Server farm model
  • Provisioning for fixed arrival rate
  • Provisioning for unpredictable, time-varying arrival rate
  • Future work
server farms
Server farms

IDLE servers consume a lot of power

~ 60 % of BUSY

server farms1
Server farms

Turn IDLE servers OFF to save power

HOWEVER

setup cost
Setup cost

To turn on an OFF server ..

  • BUSY
  • OFF
  • SETUP
  • Time delay (setup time)
  • 1 min – 5 mins
  • and
  • Power penalty
  • peak power during setup time
setup cost1
Setup cost

To turn on an OFF server ..

  • BUSY
  • OFF
  • SETUP

Should we ever turn servers OFF ?

server model
Server model
  • Server states:

BUSY PBUSY 240 W

IDLE PIDLE 150 W

OFF POFF 0 W

SETUP PSETUP 240 W

  • Setup times:

TOFF→ON 200 s

TON→OFF 0 s

ON

  • Intel Xeon E5320
  • 2 X 1.86 GHz quad-core
  • 4GB memory
server farm model
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

Server Farm

Requests

FCFS

metric
Metric
  • Interested in response time and power conumption
  • Perf/W = 1/(Mean RT X Mean Power)
  • Maximize Perf/W
outline1
Outline
  • Server farm model
  • Provisioning for fixed arrival rate
  • Provisioning for unpredictable, time-varying arrival rate
  • Future work
provisioning for fixed arrival rate
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

neveroff
NEVEROFF
  • Keep n servers always ON (M/M/n)
    • Servers are BUSY or IDLE
instantoff
INSTANTOFF
  • Turn servers OFF when IDLE
    • Servers are BUSY, OFF or in SETUP

Auto-scales if n is high

neveroff vs instantoff
NEVEROFF vs. INSTANTOFF

TON→OFF < γ E[S]/√ρ

near optimality
Near-optimality
  • Best of {NEVEROFF, INSTANTOFF} is optimal for single-server
  • Multi-server ?

For ρ > 10, we are within 20% of OPT

outline2
Outline
  • Server farm model
  • Provisioning for fixed arrival rate
  • Provisioning for unpredictable, time-varying arrival rate
  • Future work
unpredictable time varying demand
Unpredictable, time-varying demand
  • Data center demand

has daily variations

  • INSTANTOFF can auto-scale
unpredictable time varying demand1
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
delayedoff
DELAYEDOFF
  • Like INSTANTOFF, except we wait for twait seconds before turning IDLE servers OFF
  • Routing ?

MRB routing is crucial !

t wait
twait
  • Rule of thumb:twait ∙ PIDLE = TOFF→ON ∙ PON
near optimality1
Near-optimality

Worse at higher frequencies

auto scaling capabilities
Auto-scaling capabilities
  • 1998 World Cup Soccer trace (ITA)
outline3
Outline
  • Server farm model
  • Provisioning for fixed arrival rate
  • Provisioning for unpredictable, time-varying arrival rate
  • Future work
future work
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
thank you
Thank You!
  • Anshul Gandhi, Varun Gupta, Mor Harchol-Balter, Michael Kozuch

Optimality analysis of energy-performance trade-off for server farm management, PERFORMANCE 2010

  • Anshul Gandhi, Mor Harchol-Balter, Ivo Adan

Server farms with setup costs, PERFORMANCE 2010

  • Anshul Gandhi, Varun Gupta, Mor Harchol-Balter, Michael Kozuch

Energy-efficient dynamic capacity provisioning in server farms,

CMU technical report CMU-CS-10-108

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