Exploring Workload Patterns for Saving Power

Transcription

1 Exploring Workload Patterns for Saving Power Evgenia Smirni College of William & Mary joint work with Andrew Caniff, Lei Lu, Ningfang Mi (William and Mary), Lucy Cherkasova, HP Labs Robert Birke and Lydia Chen, IBM Zurich Lab

2 Overview Mo*va*on (big picture view) Data centers: workload characteriza*on Is burs*ness for real? Burs*ness (focus on the small picture) Index of dispersion: to capture burs*ness Alloca*on algorithm: Fastract Performance evalua*on Open problems 2

3 Motivation Traffic surges are frequent but chanllenging Slashdot effect : a web page linked by a popular blog or media site suddenly experices a huge increase of number of hits Auction sites (e.g., ebay), special offers or marketing campains Burstiness or temporal surges leads to uncontrolled increase of response time or resource overprovisioning Image from 3

4 Motivation Traffic surges are frequent but chanllenging Slashdot effect : a web page linked by a popular blog or media site suddenly experices a huge increase of number of hits Auction sites (e.g., ebay), special offers or marketing campains Burstiness or temporal surges leads to uncontrolled increase of response time or resource overprovisioning 4

5 Motivation: looking at mature Data Centers ALL IBM data centers (thousands of servers) Two year study (June 2009 to May 2011) Global view Empirical distributions of resource utilizations Different time scales Two year time frame Macro view vs micro view Small one: 393 servers Large one: 3672 servers Expected: overprovisioning Seasonality: patterns Resource demand trends: patterns, burstiness 5

6 Motivation: looking at mature Data Centers Utilizations across basic resources Hidden CPU peaks Measurement granularities 6

7 Motivation: looking at mature Data Centers Looking at CPU Hidden CPU peaks Some fitting (elementary) 7

8 Motivation: looking at mature Data Centers Micro view: two specific enterprises 8

9 Motivation: looking at mature Data Centers Macro view: two specific data centers Some shared servers, some not Small data center view Large data center view 9

10 Motivation: looking at mature Data Centers Small data center: enterprise 1 10

11 Motivation: looking at mature Data Centers Small data center: enterprise 2 11

12 Motivation: looking at mature Data Centers Small data center: enterprise 3 12

13 Motivation: looking at mature Data Centers Large data center: enterprise 5 13

14 Motivation: looking at mature Data Centers Large data center: enterprise 6 14

15 Summary Big differences on how each client uses the DC CPU is consistently overprovisioned Disk utilizations are usually very high Dips in across time graphs! Upgrades/where the money is! 15

16 Summary (cont.) Overprovisioning Utilization vs. other measures Arrival/service processes Google data center data Google cloud clusters See: code.google.com/p/googleclusterdata/wiki/ ClusterData2011_1 Arrival process is clearly bursty (high autocorrelation) 16

17 Summary (cont.) Overprovisioning Utilization vs. other measures Arrival/service processes Google data center data Google cloud clusters See: code.google.com/p/googleclusterdata/wiki/ ClusterData2011_1 Arrival process is clearly bursty (high autocorrelation) 17

18 Challenge Back into the micro view... [Related work] Multi-tiered systems Single-tier dynamic provisioning Bottleneck switch Determining how many servers to provision and where is far more complex. Bursty/seasonal workloads Design an analytical model of multi-tier application to capture the required capacity of different tiers Burstiness precludes the use of classic analytical models Need simple models of seasonality 18

19 Outline Mo*va*on & Challenges Index of dispersion: Capture burs*ness Alloca*on algorithm: Fastract Performance Evalua*on Future work & Conclusions 19

20 Index of Dispersion: Capture Burstiness A metric measures variability and temporal structure Variability Temporal structure Squared Coeffient of Varia9on Lag k autocorrela9on I = SCV (1 + 2 ρk ) k = 1 20

21 Index of Dispersion Definition [Cox1966][Gusella1991] Given a time series of random variables {Xn}, where n = 0,...,, the index of dispersion is defined: I = SCV (1 + 2 ρk ) k = 1 2 where, quantifies the variability in the series, = C ov( X, X ) / Var( X ) lag-k autocorrelation is the cross-correlation of the time series variable against a k stepshifted version of itself. SCV = Var( X ) / E ( X ) ρ k i i+ k 21

22 Index of Dispersion: Example (1) Poisson Process I =1 Renewal process (i.i.d.) (e.g., HyperExponential) 2 I = SCV = Var( X ) / E ( X ) Markov Modulated Poisson Process (MMPP(2)) I 2 σ σ ( λ λ ) = 1 + ( σ σ ) ( λ σ λ σ ) I = Classic FIFA World Cup trace 22

23 Index of Dispersion: Example (2) Same mean and SCV Hyper Exponential (iid) versus MAP I = 20 I =

24 Challenge: What to do with? Using batch size K = avg arrival rate C (we use C = 2000) Adding the first 10% of K instead of I value detects the workload change I = I = 1~107 I = I = 3~

25 Detect Burstiness: a close view Algorithm for detecting burstiness Together with arrival rate to know whether a burst starts or burst ends Detec9ng Burst Burst Start Burst Ends 1. current_i calculate I value for current batch 2. current_rate arrival rate for current *me 3. Update total average arrival rate and SCV 4. IF ( current_i old_i > 2*SCV ) AND (old_i / current_i> 2 OR old_i/current_i < 0.5) I = I = 1~107 I = IF (current_rate > total_rate) burst starts 6. ELSE burst I = ends 3~

26 Detect Burstiness: a big picture Capture the beginning and end of most bursty periods -- not really perfect Slower in the detection of a quiet period Taking longer to gather after the conclusion of burst 26

27 Talk Outline Mo*va*on Index of dispersion: Capture burs*ness Alloca*on algorithm: Fastract Performance Evalua*on Future work & Conclusions 27

28 System Overview TCP-W 2-tier implementation 8 potential front servers, 1 database server Focus on front tier allocation Users Web + App server Dispatcher Database Front server pool 28

29 Fastract algorithm (1) Proactive adjustment Reactive adjustment Proac9ve Adjustment (Primary Resource Alloca9on for request burst Management ) 1. Increase front servers when burst starts and front server is the bovleneck 2. Decrease front serves when burst ends Burst starts!! Users Database Dispatcher Front server pool 29

30 Fastract algorithm (2) Proactive adjustment Reactive adjustment Proac9ve Adjustment (Primary Resource Alloca9on for request burst Management ) 1. Increase front servers when burst starts and front server is the bo`leneck 2. Decrease front serves when burst ends Burst ends! Users Database Dispatcher Front server pool 30

31 Fastract algorithm (3) Proactive adjustment Reactive adjustment Reac9ve Adjustment (Secondary Resource Alloca9on for SLA guarantees) 1. Increase front servers, when SLO is violated, front server is the bovleneck and current number of front servers can not sa9sfy SLO 2. Decrease front servers, when SLO is sa*sfied and front server is not the bo`leneck Users SLO Database Dispatcher Front server pool 31

32 Fastract algorithm (4) Proactive adjustment Reactive adjustment Reac9ve Adjustment (Secondary Resource Alloca9on for SLA guarantees) 1. Increase front servers, when SLO is violated, front server is the bo`leneck and current number of front servers can not sa*sfy SLO 2. Decrease front servers, when SLO is sa9sfied and front server is not the bovleneck SLO Users Database Dispatcher Front server pool 32

33 Experiment setup Different transaction mix: Browsing mix: 95% browsing and 5% ordering Shopping mix: 80% browsing and 20% ordering Ordering mix: 50% browsing and 50% ordering Think time of emulated browsers (EB) are modeled by MAPs (injecting burstiness) [ICAC 09] Mean = 7 seconds, SCV = 20, Index of dispersion = 41 (low) and 1806 (high) Using a MAP for the service processes in front and database servers [Middleware 08] Compare results to systems with a static number of server Power usage Raw machine seconds 33

34 Evaluation results (1) Persistent bottleneck: ordering mix Front servers are always the bottleneck Using minimal power while reaching SLO target Transient behavior of ordering mix EB =

35 Evaluation results (2) Persistent Bottleneck: shopping mix Database is the system bottleneck and dominate the end-toend response time Do not increase front server even in the presence of a burst Bottleneck switch: browsing mix Locate bottleneck before provisioning 35

36 Interesting open problems (to a simple problem ) Prediction of burst length More details about power usage measurement Cost of power up/power down Real (more complicated) problems abound Virtualization Multiple resources Sharing? How to share best? Best match? How to give different priorities? SLOs? Multiclass vs single class? Effective modeled-based allocation [HPDC 12, DSN- PDS 12 (to appear) 36

37 Acknowledgements: Work funded by NSF grant NSF, #CCF , Computing and Communication Foundations, Computing Processes and Artifacts, (CPA-ACR-CSA) Effective Resource Allocation under Temporal Dependence Data center workload characterization has been done during my sabbatical leave at IBM-Research Zurich 37

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