Storm Prediction in a Cloud. Ian Davis, Hadi Hemmati, Ric Holt, Mike Godfrey Douglas Neuse, Serge Mankovskii

Transcription

1 Storm Prediction in a Cloud Ian Davis, Hadi Hemmati, Ric Holt, Mike Godfrey Douglas Neuse, Serge Mankovskii

2 Load Balancing in Clouds The goal / balancing act: Want to maximise delivery of cloud services While satisfying service level agreements (SLA s) At minimal cost to service provider This requires: Fine grain performance monitoring Accurate short and long term prediction models Intelligent Virtual Machine (VM) placement policies Proactive trend analysis and purchasing policies PESOS May 2013 Storm Prediction in a Cloud 2

3 The challenges Performance monitoring: What information should be monitored How to make sense of Big Data How to handle missing data Prediction models Faced with highly skewed resource consumption Need short and longer term storm warnings Want to accurately predict peak behaviour How best to predict overall behaviour? PESOS May 2013 Storm Prediction in a Cloud 3

4 The case study 1,572 virtual machines 495 physical machines 56 VMware hosts At least 423 virtual desktops Virtual utilization monitored every five minutes Giving average virtual utilization seen every hour Giving maximum virtual utilization seen per hour Six month time frame ( ) PESOS May 2013 Storm Prediction in a Cloud 4

5 Exponential resource usage PESOS May 2013 Storm Prediction in a Cloud 5

6 Low utilization issues Cloud systems are being over provisioned to ensure storms do not violate SLA s Predicting low future utilization produces least total error because only very rarely are future utilizations high When utilizations are high, predicted utilization tend to be very wrong Accurately predicting storms permits higher utilization thus reducing infrastructure costs PESOS May 2013 Storm Prediction in a Cloud 6

7 Possible Prediction algorithms Baseline: Assume utilization unchanged PESOS May 2013 Storm Prediction in a Cloud 7

8 Multivariate Linear Regression (MVLR) Lags 1, 2 hour, 1, 2 day, 1-4 week, 1, 2 months (Good data correlation observed at these lags) 5 Week window Optionally Scale predicted variance to observed variance Internally weight utilization frequency u by (1+u) c Externally work with powers u c and c prediction PESOS May 2013 Storm Prediction in a Cloud 8

9 MVLR Average( Error ) Very small average absolute error when u small Large average absolute error ±0.3 when u large Not predicting peak utilization at all well PESOS May 2013 Storm Prediction in a Cloud 9

10 Weighted MVLR Average( Error ) Increasing c increases overall average error Reduces average error when utilization high Can balance average error across utilizations PESOS May 2013 Storm Prediction in a Cloud 10

11 Power.v. Weighted MVLR Weighted slightly better (works for arbitrary values) Power can be used with standard MVLR Weighted does not require 0 u 1 PESOS May 2013 Storm Prediction in a Cloud 11

12 Predicting Seasonality Using FFT Fit training data with sum of 10 max amplitude waves Peaks not well modelled by this approximation PESOS May 2013 Storm Prediction in a Cloud 12

13 Scaling Seasonality Linearly scale average of top 100 predictions to align with average top 100 observed utilizations PESOS May 2013 Storm Prediction in a Cloud 13

14 Fit to Testing Data Good first approximation to future data PESOS May 2013 Storm Prediction in a Cloud 14

15 Remaining residue Seasonality is a good predictor of future behaviour PESOS May 2013 Storm Prediction in a Cloud 15

16 After applying MVLR to residue Even better fit to the observed values Very occasionally results can be much worse PESOS May 2013 Storm Prediction in a Cloud 16

17 Current Work Adaptive Algorithms Predict with both FFT and weighted algorithm Solve FFT(t)*β + Weighted(t)*(1-β) = utilization(t) If β>1 then β=1 else if β<0 then β=0 Next adaptive prediction Adaptive = FFT(t+1)*β + Weighted(t+1)*(1-β) This uses which ever prediction was last closer, and weights the prediction if last utilization lay between last two predictions PESOS May 2013 Storm Prediction in a Cloud 17

18 Accuracy of FFT+MVLR Doesn t predict large utilizations very well PESOS May 2013 Storm Prediction in a Cloud 18

19 (1+u)^14 Weighted MVLR Doesn t predict small utilizations at all well PESOS May 2013 Storm Prediction in a Cloud 19

20 The Adaptive Algorithm A good compromise between the two extremes PESOS May 2013 Storm Prediction in a Cloud 20

21 Future Work Improve the adaptive algorithm Perhaps use moving window (size > 1) Perhaps use MVLR with dummy variables Use more predictive methods within algorithm Use multiple input sources to predict Disk I/O, Network I/O, Memory etc. Validate against other cloud environments Explore case study with clear trends PESOS May 2013 Storm Prediction in a Cloud 21

22 Thank You PESOS May 2013 Storm Prediction in a Cloud 22