power cost reduction in distributed data centers
DESCRIPTION
Power Cost Reduction in Distributed Data Centers. Yuan Yao University of Southern California. Joint work: Longbo Huang, Abhishek Sharma, LeanaGolubchik and Michael Neely. IBM Student Workshop for Frontiers of Cloud Computing 2011 Paper to appear on Infocom 2012. - PowerPoint PPT PresentationTRANSCRIPT
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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:[email protected]://www-scf.usc.edu/~yuanyao/