Cloud Analytics for Capacity Planning and Instant VM Provisioning

(1)

Cloud Analytics for Capacity Planning

and Instant VM Provisioning

Yexi Jiang

Florida International University

Advisor: Dr. Tao Li

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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

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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

(4)

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

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Aggregating the Raw Data

weekly

daily

hourly

Cannot reflect

real capacity

Just right

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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

weekly

daily

hourly

Cannot reflect

real capacity

Just right

Too irregular

(7)

•

Background

•

–

•

–

Experimental evaluation results

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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)

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Motivation

• Reduce power cost via capacity prediction

Cos

t of the

Cl

oud

P

ro

vi

de

r

Prepared Resource

Real Requirement

(10)

Motivation

• Reduce power cost via capacity prediction

Cos

t of the

Cl

oud

P

ro

vi

de

r

Prepared Resource

Predicted Resource

Real Requirement

(11)

Candidate Time Series

•

Capacity time series

–

Non-stationary.

–

Difficult to model directly

•

Provisioning /de-provisioning

time series

–

Obvious temporal pattern

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Basic Idea

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

Existing VM in

cloud

-+

Predicted

Provisioning

Predicted

De-provisioning

Predicted Capacity

(13)

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

w

_p(t)

weight of predictor p

v

_p

predicted value of individual

predictor p

c

_p(t)

cost of predictor p at time t

(14)

Cloud Prediction Cost

•

Over-prediction: cost of resource waste.

•

R

function:

•

Under-prediction: cost of SLA penalty.

•

T

function:

•

Property: Non-negative, Monotonic.

))

(

~

),

(

))

(

~

),

(

v

t

v

t

T

v

t

v

t

R

C

=

+

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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.

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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.

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•

Background

•

–

Experimental evaluation results

•

–

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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

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Core Idea

Model and

predict

demands

Predict

Results

Pre-provision

at 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% requests

2) Inflection point divides the VM types into popular group and rare group

3) Requests for rare image types appear randomly.

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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

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Thank you

•