Cloud Analytics for Capacity Planning and Instant VM Provisioning

Yexi Jiang Florida International University

Advisor: Dr. Tao LiCollaborator: Dr. Charles Perng, Dr. Rong Chang

Presentation Outline

• Background• Cloud Capacity Prediction

– Predict provisioning resource demand– Estimate de-provisioning requests– Experimental evaluation results

• Instant Cloud Provisioning– Predict VM provisioning demand– Experimental evaluation results

1 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Background

• What is Cloud Analytics? Rapidly identify cloud resource or application trouble spots so you can solve the problem.

• What is the objective of cloud analytics? • The cloud platform itself.

• What can cloud analytics do?– Workload analysis– System fault diagnostics– …

2 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Smart Cloud Enterprise trace data

• 5 month, 35k+ requests, 120+ image types, 20+ features each record• Important Features: Image Name, Owner, Start Time, End Time, ID

3 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Aggregating the Raw Data

5

weekly

daily

hourly

Cannot reflect real capacity

Just right

4 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Aggregating the Raw Data

Measurement Weekly Daily Hourly

Coefficient of Variance (CV) 0.5606 0.7915 1.2249

Skewness 0.3295 1.5644 5.4464

Kurtosis 1.62 5.8848 52.4103

6

weekly

daily

hourly

Cannot reflect real capacity

Just right

Too irregular

5 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Presentation Outline

• Background• Cloud Capacity Prediction

– Predict provisioning resource demand– Estimate de-provisioning requests– Experimental evaluation results

• Instant Cloud Provisioning– Predict VM provisioning demand– Experimental evaluation results

6 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Cost of Data Centers

• 31% of the cost is related to power.• As hardware price continuously decreases, the proportion would

further increase.• The US EPA estimates the energy usage at data centers is experiencing

successive doubling every five years. (7.4 billion in 2011)

* From James Hamilton's Blog

7 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Motivation

• Reduce power cost via capacity predictionCo

st o

f the

Clo

ud P

rovi

der

Prepared Resource

Real Requirement

8 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Motivation

• Reduce power cost via capacity predictionCo

st o

f the

Clo

ud P

rovi

der

Prepared Resource

Predicted Resource

Real Requirement

9 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Candidate Time Series

• Capacity time series– Non-stationary. – Difficult to model directly

• Provisioning /de-provisioning time series– Obvious temporal pattern– Better candidate

10 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Basic Idea

• Capacity = (# existing VMs) + (# provisioning) - (# de-provisioning)

Existing VM in cloud

-

+

PredictedProvisioning

PredictedDe-

provisioning

Predicted Capacity

11 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Predicting Provisioning Demands

• Ensemble method for time series prediction• Individual prediction techniques used:

– Moving Average. Naïve predictor.– Auto Regression. Linear predictor.– Neural Network. Non-linear predictor.– Gene Expression Programming. Genetic algorithm.– Support Vector Machine. Linear predictor with non-linear kernel.

• Dynamic weighted linear combination

• Weight update

wp(t) weight of predictor p

vp predicted value of individual predictor p

cp(t) cost of predictor p at time t

e(t) error of individual predictor p

12 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Cloud Prediction Cost

• Over-prediction: cost of resource waste. • R function:

• Under-prediction: cost of SLA penalty. • T function:

• Property: Non-negative, Monotonic.

13 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

))(~),(())(~),(( tvtvTtvtvRC +=

Prediction Result

• Ensemble has the best average performance.

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Predicting De-provisioning

• Use the life span CDF F(x) of VMs to estimate number of de-provisioning requests

• Estimation of distribution: step-wise function.

* ni # of VMs with life span t (t1 < t < t2)

15 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

De-provisioning evaluation

Test data: last 60 day. Test methods:1. No preparation at all (None)2. Always prepare the maximum capacity

(Maximum)3. Time series prediction (Time Series)4. Life span distribution despite of image

– 60 days of data (Dist 60)– 90 days of data (Dist 90)

• Global distribution estimation method outperforms the time series prediction method.

16 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Presentation Outline

• Background• Cloud Capacity Prediction

– Predict provisioning resource demand– Estimate de-provisioning requests– Experimental evaluation results

• Instant Cloud Provisioning– Predict VM provisioning demand– Experimental evaluation results

17 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Motivation

• Problem: Existing clouds are not “instant”, not suitable for mid-job scaling and urgent tasks.

• VM preparation is fast, but patching, security assurance, manual process and other processes cost time.

• Known solutions:– Prepare extreme large number of different types of VMs. Waste

resource– Ask customers to provide schedule. Impractical

• Our Idea: Make good use of the customer historical requests to infer the future demand. Reduce the average VM provisioning fulfillment time.

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Core Idea

Model and predict

demands

Predict Results

Pre-provisionat suitable

time

Wait for Requests

Assign VMs to

customers

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Focus on individual types

• No obvious temporal patterns for individual image type. Ensemble is still required.

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Focus on popular VM types

1) About 10% (12) of the 124 VM types consists more than 80% requests2) Inflection point divides the VM types into popular group and rare group 3) Requests for rare image types appear randomly.

21 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Workflow Overview

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Experimental Evaluation

• Ensemble method have the best performance in reducing waiting time and resource waste.

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Conclusion

• Capacity Prediction– The demand of cloud capacity can be estimated by predicting provisioning and de-

provisioning requests– Use time series ensemble method for provisioning prediction– Use VM life span model for de-provisioning prediction

• Instant cloud provisioning– Pre-provision VMs before requests arrive– Predict VM provision requests use time series ensemble method– The average provisioning fulfillment time can be reduced by 85%+

• Future work– Improve prediction with user profile– Fine-grain adjustment with control theory

24 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Thank you!

25 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Thank you

• Related Paper:• Intelligent Cloud Capacity Management. (NOMS 2012)• ASAP: A Self-Adaptive Prediction System for Instant Cloud

Resource Demand Provisioning. (ICDM 2011)

• Patent:• Cloud Provisioning Accelerator, Serial # 13306506, Pending

26 Yexi Jiang http://users.cis.fiu.edu/~yjian004/