Transcript Document

Host Load Prediction in a Google Compute
Cloud with a Bayesian Model
Sheng Di1, Derrick Kondo1, Walfredo Cirne2
1INRIA
2Google
Outline
 Motivation of Load Prediction
 Google Load Measurements & Characterization
 Pattern Prediction Formulation
 Exponential Segmented Pattern (ESP) Prediction
 Transformation of Pattern Prediction
 Mean Load Prediction based on Bayes Model
 Bayes Classifier
 Features of Load Fluctuation
 Evaluation of Prediction Effect
 Conclusion
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Motivation (Who needs Load Prediction)
 From the perspective of on-demand allocation
 User’s resources/QoS are sensitive to host load.
 From the perspective of system performance
 Stable load vs. Unstable load:
System is best to run in a load balancing state,
where the load burst can be released asap.
 From the perspective of Green computing
 Resource Consolidation:
Shutting down idle machines can save electricity cost
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Google Load Measurements & Characterization
 Overview of Google trace
 Google released one-month trace in Nov. of
2011 (40G disk space).
 10,000+ machines @ Google
 670,000 jobs, 25 million tasks in total
 Task: the basic resource consumption unit
 Job: a logic computation object that
contains one or more tasks
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Google Load Measurements & Characterization
 Load Comparison between Google and Grid (GWA)
 Google host load fluctuates with higher noises
min noise / mean noise / max noise
 Google:
0.00024, 0.028, 0.081
 AuverGrid: 0.00008, 0.0011, 0.0026
> 20 times
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Pattern Prediction Formulation
 Exponentially Segmented Pattern (ESP)
 The hostload fluctuation over a period is split into a set of
consecutive segments, whose lengths increase exponentially.
 We predict the mean load over each time segment: l1, l2,
…..
(Evidence window)
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Pattern Prediction Formulation (Cont’d)
 Reduction of ESP Prediction Problem
 Idea: Get Segmented Levels (li) always from the
mean load (denoted as ηi ) during [t0, ti ]
 We can get li , based on t0, (ti-1,ηi-1), (ti,ηi)
t0
t1
t2
t3
t4
Time Series
Current time point
 Two key steps in the Pattern Prediction Algorithm
 Predict mean values with b2k lengths from current point
 Transform the set of mean load prediction to ESP
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Traditional Approaches to Mean Load Prediction
 Can Feedback Control Model work? NO
 Example: Kalman Filter
 Reason: one-step look-ahead prediction doesn’t fit our
16 hours
16 hours
long-interval prediction goal.
 Can we use
short-term prediction
error to instruct
long-term prediction
feed-back? NO
 Can the traditional methods like
Linear Model fit Google host load prediction?
 Such as Simple Moving Average, Auto-Regression (AR), etc.
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Mean Load Prediction based on Bayes Model
 Principle of Bayes Model (Why Bayes?)
 We strongly believe the correctness of probability
 Posterior Probability rather than Prior Probability
 Naïve Bayes Classifier (N-BC)
 Predicted Value:
 Minimized MSE Bayes Classifier (MMSE-BC)
 Predicted Value:
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Why do we use Bayes Model?

Special Advantages of Bayes Model

1.
2.
3.
4.
Bayes Method can
effectively retain important features about load
fluctuation and noises, rather than ignoring them.
dynamically improve prediction accuracy, with
more accurate probability updated based on
increasing samples.
estimate the future with low computation
complexity, due to quick probability calculation.
only take limited disk space since it just needs to
keep/update corresponding probability values
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Mean Load Prediction based on Bayes Model
 Implementation of Bayes Classifier
 Evidence Window: an interval until current moment
 States of Mean Load: for prediction interval

r states (e.g., r = 50 means there are 50 mean load
states to predict: [0,0.02), [0.02,0.04),……, [0.98,1]
)
Key Point: How to extract features in Evidence Window?
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Mean Load Prediction based on Bayes Model

Features of Hostload in Evidence Window
1. Mean Load State (Fml(e))
2. Weighted Mean Load State (Fwml(e))
3. Fairness index (Ffi(e))
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Mean Load Prediction based on Bayes Model
4. Noise-decreased fairness index (Fndfi(e))

Load outliers are kicked out
5. Type state (Fts(e)): for degree of jitter



Representation: (α, β)
α= # of types (or # of state levels)
β= # of state changes
eg. α = 4
eg. β = 8
0.10
0.08
0.06
0.04
0.02
0.00
β=1β=2
β=3 β=4
β=5 β=6 β=7 β=8
Prediction Interval
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Mean Load Prediction based on Bayes Model
6. First-Last Load (Ffll(e))
= {first load level, last load level

}
7. N-segment Pattern (FN-sp(e))



F2-sp(e): {0.01, 0.03}
F3-sp(e): {0.02, 0.04, 0.04}
F4-sp(e): {0.02, 0.02, 0.05, 0.05}
0.10
0.08
0.06
0.04
0.02
0.00
F2-sp
4-sp(e)
3-sp
Prediction Interval
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Mean Load Prediction based on Bayes Model
 Correlation of Features
 Linear Correlation Coefficient
 Rank Correlation Coefficient
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Mean Load Prediction based on Bayes Model
 Compatibility of Features
 Four Groups split:
{Fml, Fwml, F2-sp, F3-sp, F4-sp} , {Ffi, Fndfi} , {Fts} , {Ffll}
 Total Number of Compatible Combinations:
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Evaluation of Prediction Effect (Cont’d)
 List of well-known load prediction methods
 Simple Moving Average
 Mean Value in the Evidence Window (EW)
 Linear Weighted Moving Average
 Linear Weighted Moving Average Value in the EW
 Exponential Moving Average
 Last-State
 use last state in the EW as the prediction value
 Prior Probability
 the value with highest prior probability
 Auto-Regression (AR): Improved Recursive AR
 Hybrid Model [27]: Kalman filter + SG filter + AR
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Evaluation of Prediction Effect (Cont’d)
 Training and Evaluation
 Evaluation Type A: the case with insufficient samples


Training Period: [day 1, day 25]: only 18,000 load samples
Test Period: [day 26, day 29]
 Evaluation Type B: ideal case with sufficient samples


Training Period : [day 1, day 29]: emulation of larger set of samples
Test Period: [day 26, day 29]
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Evaluation of Prediction Effect (Cont’d)
 Evaluation Metrics for Accuracy
 Mean Squared Error (MSE)
where
are true mean values
and
 Success Rate (delta of 10%) in the test period
success rate =
Number of Accurate Predictions
Total Number of Predictions
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Evaluation of Prediction Effect (Cont’d)
1. Exploration of Best Feature Combination
(Success Rate): Evaluation Type A
Representation of Feature Combinations


101000000 denotes the combination of the mean
load feature and fairness index feature
(a) s = 3.2 hour
(b) s = 6.4 hour
(c) s = 12.8 hour
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Evaluation of Prediction Effect (Cont’d)
1. Exploration of Best Feature Combination
(Mean Squared Error)
(a) s = 3.2 hour
(b) s = 6.4 hour
(c) s = 12.8 hour
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Evaluation of Prediction Effect (Cont’d)
2. Comparison of Mean Load Prediction Methods
(Success Rate of CPU load w.r.t. Evaluation Type A)
(a) s = 6.4 hour
(b) s = 12.8 hour
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Evaluation of Prediction Effect (Cont’d)
2. Comparison of Mean Load Prediction Methods
(MSE of CPU load w.r.t. Evaluation Type A)
(a) s = 6.4 hour
(b) s = 12.8 hour
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Evaluation of Prediction Effect (Cont’d)
4. Comparison of Mean-Load Prediction Methods
(CPU load
w.r.t.
Evaluation
Type B)
Best feature Combination
 mean load
 fairness index
 type-state
 first-last
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Evaluation of Prediction Effect (Cont’d)
5. Evaluation of Pattern Prediction Effect
Mean Error & Mean MSE


Mean Error:
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Evaluation of Prediction Effect (Cont’d)
5. Evaluation of Pattern Prediction Effect

Snapshot of Pattern Prediction (Evaluate Type A)
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Conclusion
 Objective: predict ESP of host load fluctuation
 Two-Step Algorithm
 Mean Load Prediction for the exponential interval from the
current moment
 Transformation to ESP
 Bayes Model (for Mean Load Prediction)
 Exploration of best-fit combination of features
 Comparison with 7 other well-known methods
 Use Google Trace in the experiment
 Evaluation type A:
Bayes Model ({Fml}) outperforms others by 5.6-50%
 Evaluation type B: {Fml,Ffi,Fts,Ffll} is the best combination.
 MSE of Pattern Predictions: majority are in [10-8, 10-5]
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Thanks
?
Questions
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