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Power Cost Reduction in Distributed Data Centers

Yuan YaoUniversity of Southern California

Joint work: Longbo Huang, Abhishek Sharma, LeanaGolubchik and Michael Neely

IBM Student Workshop for Frontiers of Cloud Computing 2011Paper to appear on Infocom 2012

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Background and motivation• Data centers are growing in number and size…– Number of servers: Google (~1M)– Data centers built in multiple locations

• IBM owns and operates hundreds of data centers worldwide

• …and in power cost!– Google spends ~$100M/year on power– Reduce cost on power while considering QoS

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Existing Approaches• Power efficient hardware design

• System design/Resource management– Use existing infrastructure– Exploit options in routing and resource management of

data center

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Existing Approaches• Power cost reduction through algorithm design– Server level: power-speed scaling [Wierman09]– Data center level: rightsizing [Gandhi10, Lin11]– Inter data center level: Geographical load balancing

[Qureshi09, Liu11]

$5/kwh $2/kwh

job

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Our Approach: SAVE• We provide a framework that allows us to exploit options in all

these levels

+

Temporal volatility of

power prices =

StochAstic power redUctionschEme(S

AVE)

Server levelData center level

Inter data center level

Job arrived Job served

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Our Model: data center and workload• M geographically distributed data centers• Each data center contain a front end server and a back end cluster• Workloads Ai(t) (i.i.d) arrive at front end servers and are routed to

one of the back end clusters

µji(t)

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Our Model: server operation and cost • Back end cluster of data center i contain Ni servers

– Ni(t) servers active

• Service rate of active servers: bi (t) [0, b∈ max]• Power price at data center i: pi(t) (i.i.d) • Powerusage at data center i:• Power cost at data center i:

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Our Model: two time scale• The system we model is two time scale– At t=kT, change the number of active servers Nj(t)– At all time slots, change service rate bj(t)

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Our Model: summary• Input: power prices pi(t), job arrival Ai(t)• Two time Scale Control Action: • Queue evolution:

• Objective: Minimize the time average power cost

subject to all constraints on Π, and queue stability

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SAVE: intuitions• SAVE operates at both front end and back end• Front end routing:– When , choose μij(t)>0

• Back end server management:– Choose small Nj(t) and bj(t) to reduce the power costfj(t) – When is large, choose large Nj(t) and bj(t) to stabilize

the queue

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SAVE: how it works• Front end routing: – In all time slot t, choose μij(t) maximize

• Back end server management: Choose V>0– At time slot t=kT, choose Nj(t) to minimize

– In all time slots τ choose bj(τ) to minimize

• Serve jobs and update queue sizes

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SAVE: performance• Theorem on performance of our approach:– Delay of SAVE ≤ O(V)– Power cost of SAVE ≤ Power cost of OPTIMAL + O(1/V)– OPTIMAL can be any scheme that stabilizes the queues

• V controls the trade-off between average queue size (delay) and average power cost.

• SAVE suited for delay tolerant workloads

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Experimental Setup• We simulate data centers at 7 locations– Real world power prices– Possion arrivals

• We use synthetic workloads that mimics MapReduce jobs• Power Cost

Power consumption of active servers

Power usage effectiveness

Power consumption of servers in sleep

Power price

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Experimental Setup: Heuristics for comparison• Local Computation– Send jobs to local back end

• Load Balancing– Evenly split jobs to all back ends

• Low Price (similar to [Qureshi09])– Send more jobs to places with low power prices

All servers are activated

• Instant On/Off– Routing is the same as Load Balancing– Data center i tune Ni(t) and bi(t) every time slot to minimize its

power cost– No additional cost on activating/putting to sleep servers

Unrealistic

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Experimental Results

• As V increases, power cost reduction grows from ~0.1% to ~18%

• SAVE is more effective for delay tolerant workloads.

relative power cost reduction as compared to Local Computation

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Experimental Results: Power Usage

• Our approach saves power usage

• We record the actual power usage (not cost) of all schemes in our experiments

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Summary• We propose atwo time scale, non work conserving control

algorithm aimed atreducing power costin distributed data centers.

• Our work facilitating an explicit power cost vs. delay trade-off

• We derive analytical bounds on the time average power cost and service delay achieved by our algorithm

• Through simulations we show that our approach can reduce the power cost by as much as 18%, and our approach reduces power usage.

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Future work• Other problems on power reduction in data centers– Scheduling algorithms to save power– Delay sensitive workloads– Virtualized environment, when migration is available

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Questions?• Please check out our paper:– "Data Centers Power Reduction: A two Time Scale

Approach for Delay Tolerant Workloads” to appear on Infocom 2012

• Contact info:yuanyao@usc.eduhttp://www-scf.usc.edu/~yuanyao/