Anshul Gandhi (Carnegie Mellon University)
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Power-efficient server provisioning in server farms PowerPoint PPT Presentation


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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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Power-efficient server provisioning in server farms

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Power efficient server provisioning in server farms

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


Perf w for neveroff

Perf/W for NEVEROFF


Instantoff

INSTANTOFF

  • Turn servers OFF when IDLE

    • Servers are BUSY, OFF or in SETUP

Auto-scales if n is high


Perf w for instantoff

Perf/W for INSTANTOFF


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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