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CARPO: C orrelation- A wa R e P ower O ptimization in Data Center Networks

CARPO: C orrelation- A wa R e P ower O ptimization in Data Center Networks. Xiaodong Wang 1 , Yanjun Yao 2 , Xiaorui Wang 1 , Kefa Lu 2 and Qing Cao 2. 1 Electrical and Computer Engineering, The Ohio State University. 2 Electrical Engineering and Computer Science

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CARPO: C orrelation- A wa R e P ower O ptimization in Data Center Networks

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  1. CARPO: Correlation-AwaRe Power Optimization in Data Center Networks Xiaodong Wang1, Yanjun Yao2, Xiaorui Wang1, Kefa Lu2 and Qing Cao2 1Electrical and Computer Engineering, The Ohio State University 2Electrical Engineering and Computer Science University of Tennessee, Knoxville

  2. Introduction • Data centers require tremendous power for daily operations • Total power requirement is around 31 GW in 2011. A 19%projected increase in 2012.[1] • Data center networking power consumption accounts for 10%-20% of the computing power. How to make data center network energy-proportional? [1] DCD Industry Census 2011: Forecasting Energy Demand

  3. Data Center Switch and Link Usage • Data center network switch power profiling (48-port)[1] • Idle power consumption: 70w – 150w • Port power: 1w – 2 w • Switch power profiling verification: • 48-port pronto 3240 switch power profiling • Links usually have low utilizations • About 8% at the aggregation layer, 20% and 40% at edge and core layer [2] [1] Mahadevan et al. IFIP 2008 [2] Benson et al. SIGCOMM Computer Communication Review 2010

  4. Related Works • Link rate adaptation: • Adapt the link rate according to the workload of each flow. • Relatively small power savings. (Dynamic power range is small.) • [Abts et al. ISCA 2010 ] [Nedevschi et al. NSDI 2008] • Traffic consolidation: • ElasticTree [Heller et al. NSDI 2010] consolidates traffic flows onto small sets of links and switches • Assuming traffic data rates are approximately constants in each consolidation period. (Traffic data rate can be variant.)

  5. Traffic Analysis in Data Center Networks • Statistical analysis on data center traffic from Wikipedia data center: • Traffic data rate can have large variations. • The data rates of most flows are less than 50% of their peak value at 90% of the time. • Using non-peak data rate (e.g. 90-percentile) to consolidate traffic flow can save more energy. 90% CDF 50% Normalized Data Rate

  6. Correlation of Traffic Flows • Consolidate traffic flows with small correlations using the non-peak rate : • Low probability to peak together, thus link capacity violation is unlikely to happen. Pearson Correlation = 1 switches 90-percentile possible link capacity violation Pearson Correlation = -1 servers

  7. Traffic Correlation Analysis • 90% of the pair-wise correlation values are less than 0.5 • Flows are loosely correlated together in data center networks • Correlation relationship is stable 90% CDF 0.5 Pair-wise correlation

  8. Problem Formulation • Optimal flow consolidation and rate configuration • Minimize the total power consumption within a consolidation period based on traffic correlation and non-peak data rate • Mixed-integer programming problem • High computational complexity

  9. Correlation Aware Power Optimization • CARPO periodically consolidates traffic and adapts link rate • Predicts the future correlation based on the previous time period. • Consolidates loosely correlated traffic flows to a small set of links, based on non-peak (e.g. 90-percentile) data rate of the previous period. (Based on greedy bin-packing algorithm.) • Turns off un-used switches to save power. • Link rate adaptation for remaining active links

  10. Hardware Testbed • 48-port Pronto 3240 switch used: • OpenFlow-enabled. • Divided into 10 virtual switches • 8 servers with 4 pair-wise data flows • Flows are from Wikipeida data • Baselines: • ElasticTree • Pure link rate adaptation (GoogleP) • CARPO without link rate adaptation (CARPO-C)

  11. Hardware Testbed Evaluation • Hardware testbed experiment results: • CARPO has the lowest power consumption and most power savings. It has closest performance to near-optimal solution. • Reason: CARPO consolidates traffics based on none-peak data rate, and features link rate adaptation.

  12. Simulation • Simulation setup: • Simulation in OPNET • Fat-tree topology: 8 core switches, 32 aggregation switches and 32 edge switches. • 61 data flows from Wikipedia data center • Simulation result

  13. Performance Degradation • Minor degradation in delay and packet drop ratio performance from CARPO • Caused by more aggressive energy savings

  14. Conclusion • Data center network flows use small portion of the link capacity at most of the time. • Data center network flows are loosely correlated (do not peak together). • CARPO achieves significant network power savings • It consolidates traffic flows using 90-percentile data rate based on the correlation values. • Only minor degradation in packet delay and drop ratio

  15. Acknowledgement • NSF CAREER Award CNS-1143607 • Grants CNS-0720663, CNS-0915959 • ONR Young Investigator Program (YIP) Award (N00014-11-1-0630). Q&A

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