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Trends and Effects of Energy Proportionality on Server Provisioning in Data Centers

Trends and Effects of Energy Proportionality on Server Provisioning in Data Centers. Georgios Varsamopoulos , Zahra Abbasi , and Sandeep Gupta Impact Laboratory School of Computing, Informatics and Decision Systems Engineering Arizona State University http://impact.asu.edu/.

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Trends and Effects of Energy Proportionality on Server Provisioning in Data Centers

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  1. Trends and Effects of Energy Proportionality onServer Provisioning in Data Centers GeorgiosVarsamopoulos, Zahra Abbasi, and Sandeep Gupta Impact Laboratory School of Computing, Informatics and Decision Systems Engineering Arizona State University http://impact.asu.edu/ Funded in parts by NSF CNS grants and by Intel Corp

  2. Introduction-Motivation Projected Electricity Use of data centers\, 2007 to 2011 Historical energy use Future energy use projection - current efficiency trend [Source: EPA] Typical data center energy end use [Source: Department of energy] • The magnitude of data center energy consumption keeps growing • Addressing energy saving for internet Data Center • Energy proportionality (power performance) awareness • Thermal awareness

  3. Talk outline • Energy saving in DCs through workload management • Characterizing Energy Proportionality of systems • Introducing IPR and LDR to measure energy proportionality of systems • Effect of energy proportionality on thermal aware workload management (TASP and TAWD) • Simulation Study • Under real data profile, current trend of energy proportionality, and truly hypothetic trend of energy proportionality • Conclusion

  4. Energy Consumption in Datacenter • Total energy consumption in a datacenter = Computing energy + Cooling energy[ref] • Techniques used to reduce total energy consumed by datacenters: • Server provisioning + Thermal awareness [Chase et al. SOSP ’01] , [Chen et al. NSDI ’08], [Kusic et al. CCJ ’09] , [Abbasi et al. HPDC’10], [Moore et al. ATEC ’05], and [Tang et al. T-PDS ’08] • Workload distribution +Thermal awareness [Abbasi et al. HPDC’10]

  5. Thermal aware server provisioning (TASP) and workload distribution (TAWD) Heat recirculation contribution Computing capabilities of machines Computing power efficiency λ request/sec Number of requests TASP Tier (Epochs) Time index (every 5 second) Traffic flow Parameters Control data TAWD Tier (Slots) HTTP requests over time, 1998 FIFA World Cup {λi} Load Dispatcher λN λ2 λ3=0 λN-1=0 λ1=0 ∑λi= λ On/Off Control Server 1 Server 1 Server 2 Server 3 Server 3 Server N Server N-1 …… Server N-1

  6. Fully Energy Proportional Systems PPeak Power Consumption (Watts) PIdle 0 100 % utilization Energy consumption should be proportional to the system workload. At 0% utilization level system should consume no power and power consumption should linearly increase with increasing utilization

  7. Project objective • Characterizing energy proportionality of systems • Introducing metrics to measure energy proportionality of systems • Will the ideal energy proportionality render server provisioning unnecessary technique?

  8. Power performance behavior of modern systems SPECpower_ssj2008 observations: -Systems consume power at idle -The power-utilization curve is not linear

  9. Energy Proportionality Metrics Linear power curve Proportional power curve Actual power curve Excessive power consumption under low utilization Ppeak • Measure of Energy Proportionality: • How close to origin power curve • starts Power Linear deviation ratio Pidle Idle to peak ratio • How close to linear it is • Measuring through a hypothetical line 0 W 0% 100% System utilization

  10. Metric-based examples Low IPR, Low LDR High IPR, Low LDR Low IPR, high LDR high IPR, high LDR

  11. Trend of computing systems LDR IPR for over past 3 yrs By 2008 By 2009 By Dec. 2010

  12. Why characterizing/exploiting energy proportionality is important? Ideal systems Old systems No workload management is required. (If thermal impact of servers is negligible) higher power efficiency at higher utilization levels. Recent systems Recent systems Higher power efficiency at lower utilization levels Significantly better energy efficiency by utilization at the maximum power efficiency

  13. The region of interests…

  14. Design of experiments-Investigating the effect of EP on the performance of TASP and TAWD • How does the current trend of energy proportionality (LDR and IPR value) affect the performance of TASP and TAWD? • Nonlinearity of power-utilization curve/non zero idle power • How does the (true) power proportionality (IPR trend) affect the performance of TASP and TAWD?

  15. IPR-LDR Scatter plot Case 1 : 0≤IPR ≤ 1, 0 ≤ LDR ≤ 1 Current trend of Energy Proportional systems Case 2: 0 ≤ IPR ≤ 1, LDR =0 Hypothetical true Energy Proportional Systems Synthesized power curves for case 2. Synthesizedpower curves for case 1.

  16. Simulation Setup Setup: Thermal profile of ASU HPCI Datacenter (Dell 1855,1955 Servers) Workload: Synthesizing SPECweb2009 + FIFA World CUP 1998

  17. Heterogenous Homogenous Case 1 Server Provisioning over no server provisioning Heterogenous Case1 What we are trying to achieve? Will Server provisioning give energy savings under current and ideal trend of energy proportionality for computing systems? Homogenous Case 2 Heterogenous Case2 IPR = 0 IPR = 0

  18. Heterogenous Homogenous Case 1 Server Provisioning over no server provisioning Heterogeneous Case1 Significant energy saving even for IPR=0 Homogenous Case 2 Heterogenous Case2 Case 1: (current trend: IPR →0, i.e. zero power at idle, LDR →1, i.e. non linear power-utilization curve) • Homogenous case • 30% energy saving at IPR=0 due to nonlinearity of power curve • around 5-10% energy saving at IPR=0 due to thermal-awareness under different power density • Heterogeneous case • 50% energy saving at IPR=0 due to nonlinearity of power curve and heterogeneity • around 7-10% energy saving at IPR=0 due to thermal-awareness under different power density IPR = 0 IPR = 0

  19. Heterogenous Homogenous Case 1 Server Provisioning over no server provisioning Heterogeneous Case1 Case 2: (Ideal trend: IPR →0, i.e. zero power at idle and LDR=0, i.e. linear power-utilization curve) • Heterogeneous case • 30% energy saving at IPR=0 due to heterogeneity • around 7-20% energy saving at IPR=0 due to thermal-awareness under different power density • Homogenous case • around 5-20% energy saving at IPR=0 due to thermal-awareness under different power density energy saving for IPR=0 : Thermal awareness Significant energy saving even for IPR=0: Thermal awareness, Heterogeneity Homogenous Case 2 Heterogeneous Case2 IPR = 0

  20. Homogenous Case 1 Work load distribution over Load Balancing Heterogeneous Case1 What we are trying to achieve? Will workload management (fine scale) give energy savings under current and ideal trend of energy proportionality for computing systems? Significant energy saving Heterogeneous Case2 Homogenous Case 2

  21. Homogenous Case 1 Work load distribution over Load Balancing Heterogeneous Case1 Significant energy savings • Case 1: (current trend: IPR →0, i.e. zero power at idle, LDR →1, i.e. non linear power-utilization curve) • TAWD saves energy up to 45% for both homogenous and heterogeneous case • The saving increases by increasing the nonlinearity of power-util curve • TAWD savings come from utilizing servers at their high energy efficiency level through short time granularity workload management • TAWD can be envisioned as a technique to compensate energy wasting due to nonlinear power-util curve of systems Heterogeneous Case2 Homogenous Case 2

  22. Homogenous Case 1 Work load distribution over Load Balancing Heterogeneous Case1 • Case 2: (Ideal trend: IPR →0, i.e. zero power at idle and LDR=0, i.e. linear power-utilization curve) • TAWD’s performance come from thermal awareness only • Energy savings of TAWD increases under higher power density, heterogeneity and higher proportionality • The fraction of computing energy decreases at IPR=0 Energy savings through thermal awareness Heterogeneous Case2 Homogenous Case 2

  23. Conclusion • Server provisioning scheme even for ideal energy proportional systems saves energy through thermal awareness • The performance of server provisioning is higher for the heterogeneous case • Current trends of energy proportionality motivates to revise the power aware workload management schemes • Trend: non-linear power-utilization curve • Solution: utilizing servers at their high energy efficiency level through fine granularity workload management • The savings of TASP and TAWD for being thermal aware is important for future high power dense data centers

  24. Questions?

  25. References [Moore et al. ATEC ’05] J. Moore, J. Chase, P. Ranganathan, and R. Sharma, “Making scheduling "cool": temperature-aware workload placement in data centers,” in ATEC ’05: Proceedings of the annual conference on USENIX Annual Technical Conference. [Tang et al. T-PDS ’08] Q. Tang, S. K. S. Gupta, and G. Varsamopoulos, “Energy-ecient thermal-aware task scheduling for homogeneous high-performance computing data centers: A cyber-physical approach,” IEEE Trans. Parallel Distrib. Syst., vol. 19, no. 11, pp. 1458–1472, 2008. [Chase et al. SOSP ’01] J. Chase, D. Anderson, P. Thakar, A. Vahdat, and R. Doyle, “Managing energy and server resources in hosting centers,” in SOSP ’01: Proceedings of the eighteenth ACM symposium on Operating systems principles. New York, NY, USA: ACM, 2001, pp. 103–116. [Chen et al. NSDI ’08] Y. Chen, A. Das, W. Qin, A. Sivasubramaniam, Q. Wang, and N. Gautam, “Managing server energy and operational costs in hosting centers,” SIGMETRICS Performance Evaluation Review, vol. 33, no. 1, pp. 303–314, 2005. [Ranganathan et al. ISCA ’06] P. Ranganathan, P. Leech, D. Irwin, and J. Chase, “Ensemble-level power management for dense blade servers,”. ISCA ’06. 33rd International Symposium in Computer Architecture, 2006, pp. 66–77. [Kusic et al. CCJ ’09] D. Kusic, J. O. Kephart, J. E. Hanson, N. Kandasamy, and G. Jiang, “Power and performance management of virtualized computing environments via lookahead control,” Cluster Computing, vol. 12, pp. 1–15, 2009. [Abbasi et al.HPDC’10] Abbasi, Z., Varsamopoulos, G., and Gupta, S. K. S. 2010. Thermal aware server provisioning and workload distribution for internet data centers. In ACM International Symposium on High Performance Distributed Computing (HPDC10). Chicago, IL.

  26. Energy efficiency for various proportionality cases • Non Energy Proportional System • Low energy efficiency at lower utilization Level • Energy Proportional System • Energy Efficiency same for all utilization levels.

  27. Energy efficiency for various proportionality cases • LDR >0 • Efficiency higher at higher utilization levels • LDR <0 • Efficiency higher at lower utilization levels

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