ADVISE: a Framework for Evaluating
Cloud Service Elasticity Behavior
Georgiana Copil1, Demetris Trihinas2, Hong−Linh Truong1, Daniel Moldovan1,
George Pallis2, Schahram Dustdar1, Marios Dikaiakos2
1
Distributed Systems Group, Vienna University of Technology
2
Computer Science Department, University of Cyprus
12th International Conference on Service Oriented Computing

Overview
 Motivation
 Evaluating Cloud Service Behavior
– Learning process
– Determining expected elasticity behavior
 Experiments
 Conclusions and Future Work
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Motivation – Cloud service runtime evolution
Complex
Cloud
Service
Elastic
Cloud
Service
Deployment (running)
process
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Elasticity
control
process
Elasticity Control
Processes
What would be
the elasticity
behavior?
Elasticity
requirements
Elasticity controller

Motivation – Cloud service runtime evolution
Elasticity control
process enforced
Which will be the behavior?
now
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Motivation – Cloud service runtime evolution
Which will be the behavior?
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Possible requirements
violations
Elasticity control
process enforced
Expected impact
Expected cool-off
period
now
Which elasticity control process is most appropriate?
How a control process will affect metrics, e.g., throughput, of the
overall service and individually on each part of the cloud service?

Motivation – Cloud service behavior
 Cloud service behavior is complex and can depend on:
– The structure of the cloud service
– The runtime resources used
– The workload of the cloud service
– The control processes enforced, e.g., by the controller
 Capturing & using these types of information for
evaluating elasticity behavior
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Service
Topology 1
Unit 1
Unit 2
Topology 2
Unit 3
Unit 4
푉푀푥1 푉푀 푉푀푥2 푥3 푉푀푥푛

Approach
 Input:
– Cloud service structure
– Monitoring information of different service parts (e.g., service
units, service topologies)
– Elasticity control process 퐸퐶푃푖
 Expected output:
– Metrics evolution, in time, for different service parts and 퐸퐶푃푠
 Main mechanism:
– Creating behavior clusters
– Computing closest behavior centroids
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Gathering information
Relevant timeseries
퐸퐶푃푖 enforcement
Metric measurement
Select relevant timeseries where 퐸퐶푃푖 was enforced before
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Clustering elasticity behaviors
 Transform relevant timeseries to multi-dimensional
points
푡1 푡2 … 푡푛 Time
Metric
푚푥
퐶푙푢푠푡푒푟푐 푚푥 퐶1푚푥
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푀푒푡푟푖푐푉푎푙 (푡1)
푀푒푡푟푖푐푉푎푙 (푡2)
푀푒푡푟푖푐푉푎푙 (푡푛)
푀푒푡푟푖푐푉푎푙 (푡3)
…
푀푒푡푟푖푐푉푎푙 (푡4)
Behavior Point
BP
퐶푙푢푠푡푒푟1푚 K-means 푥
퐶푙푢푠푡푒푟2 푚푥
퐶2푚푥
퐶푐 푚푥

Computing expected behavior
퐵푃푚푥
Current values
퐶푙푢푠푡푒푟1 푚푥
퐶1푚푥
퐵푃푚푦
퐶푙푢푠푡푒푟2 푚푥
퐶2푚푥
퐶푙푢푠푡푒푟1 푚푦
퐶1푚푦
퐶푙푢푠푡푒푟푝 푚푦
퐶푝 푚푦
퐶푙푢푠푡푒푟1푚푥 퐶푙푢푠푡푒푟2 푚푥
퐶푙푢푠푡푒푟푟 푚푥
퐶푙푢푠푡푒푟1 푚푦
a b -
퐶푙푢푠푡푒푟푝 푚푦
c - d
Co-occurrence matrix
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Compute centroids
closest to the 퐵푃푖
퐶1푚푥
퐶푝 푚푦
Transform
to timeseries
푚 푚푥 푦
퐶푙푢푠푡푒푟푟 푚푥
퐶푟 푚푥

Experiment Settings [1/3]
 Setting:
– M2M service
– Video Service
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Experiment Settings [2/3]
 Setting:
– Running on public Flexiant cloud FCO
– MELA & JCatascopia for monitoring cloud services
– Randomly apply ECPs of random type for collecting behavioral
information
– “Interesting” metrics
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Experiment Settings [3/3]
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Experiments – Video Service
Video Service – effect of 퐸퐶푃1 on Application Server
퐸퐶푃1 - scale in application server tier – select instance to remove,
stop the video streaming service, remove instance from load
balancer, stop JCatascopia monitoring agent, delete instance
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Experiments – M2M Service [1/2]
M2M Service – effect of 퐸퐶푃7 on the entire cloud service
퐸퐶푃7 - scale in data node service unit – copy data from the instance
to be removed, remove recursively virtual machine
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Experiments – M2M Service [2/2]
M2M Service – effect on Data End Controller of enforcing 퐸퐶푃8
퐸퐶푃8 - scale out data node service unit – create new network
interface, create new instance, assign token to node, set cluster
controller
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The more random the workload, of the service,
the more difficult to estimate the behavior Lower abstraction layer
Experiments –
Quality of Results
푉푎푟푖푎푛푐푒푚
=
=> better estimations
푛푏퐸푠푡푖푚푎푡푖표푛푠 푒푠푡푖푚푎푡푖표푛푆푖푧푒(푒푠푡푖푚푎푡푒푑푀푒푡푟푖푐푚 − 표푏푠푒푟푣푒푑푀푒푡푟푖푐푚)2
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푛푏퐸푠푡푖푚푎푡푖표푛푠 − 1
Complex,
unpredictable
metrics => very low
degree of accuracy

Conclusions and Future Work
 Conclusions
– When controlling a complex cloud service, we need to consider
the impact elasticity control processes have on different service
parts
– ADVISE is indeed able to "advise" elasticity controllers about
cloud service behavior
 Future work
– Integrating with rSYBL (https://github.com/tuwiendsg/rSYBL)
– Adapting the control mechanisms of rSYBL to use such
information
 ADVISE
– More experiments available at http://tuwiendsg.github.io/ADVISE
– Prototype https://github.com/tuwiendsg/ADVISE
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Thank you!
Georgiana Copil
e.copil@dsg.tuwien.ac.at
http://dsg.tuwien.ac.at/staff/ecopil/
Distributed Systems Group
Vienna University of Technology
Austria
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