Control System for Energy Efficient Data Centers Ozlem Bilgir

Outline Motivation System Design Simulation Results Conclusion & Future Work

Motivation Take advantage from energy cost differences at different geographical regions Different server architectures Different cooling schemes Adaptive control methodologies …..

Motivation (cont.) If we know the request rate, we can adjust processing rate and/or number of servers in order to achieve desired performance & energy dissipation Ex. Desired avg. latency =0.5 s Predicted load rate = 8 req/s Processing rate = 10 req/s Obtained latency = 0.5 s

Motivation (cont.) Nothing is perfect.. – Prediction errors !!! time Load rate Predicted Load rate Actual Load rate

System Design What if we have an adaptive control system 1 q(t+Δt) –q(t) =(λ(t)- μ(t)) x Δt 1. Q. Wu, P. Juang, M. Martonosi, and D. W. Clark, "Formal Online Methods for Voltage/Frequency Control in Multiple Clock Domain Microprocessors“, 2004

System Design(cont.)

Kp’ = 0.6 Ki’ = 0.2

Results Load Rate time -Load rate -Proc. rate

Motivation (cont.) If we know the request rate, we can adjust processing rate and/or server number in order to achieve desired performance & energy dissipation Ex. Desired avg. latency =0.5 s Predicted load rate = 8 req/s Processing rate = 10 req/s Obtained latency = 0.5 s

Results Load Rate time -Load rate -Proc. rate

Effect of Q-REF λλ latency Latency is not bounded!! It is not under our control!! Q-ref latency

2-Loop System Design w desired

2-Loop System Design(cont.) Kp’ = Ki’ = 0.241

Results lambda Energy vs Lambda lambda

Conclusion & Future Work Energy consumption can be reduced by using an adaptive control system Latency can be fixed to a some level Multi-server case Real system design

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