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ADVISE: a Framework for Evaluating
Cloud Service Elasticity Behavior
Georgiana Copil1, Demetris Trihinas
2, HongβLinh Truong1, Daniel Moldovan
1,
George Pallis2, Schahram Dustdar
1, Marios Dikaiakos
2
1Distributed Systems Group, Vienna University of Technology
2Computer Science Department, University of Cyprus
12th International Conference on Service Oriented Computing
ICSOC 2014, 5 November, Paris 2
Overview
Motivation
Evaluating Cloud Service Behavior
β Learning process
β Determining expected elasticity behavior
Experiments
Conclusions and Future Work
ICSOC 2014, 5 November, Paris 3
Motivation β Cloud service runtime evolution
Complex
Cloud
Service
Elastic
Cloud
Service
(running)Deployment
process
Elasticity
control
process
Elasticity Control
Processes
What would be
the elasticity
behavior?
Elasticity
requirements
Elasticity controller
ICSOC 2014, 5 November, Paris 4
Motivation β Cloud service runtime evolution
Elasticity control
process enforced
now
Which will be the behavior?
ICSOC 2014, 5 November, Paris 5
Motivation β Cloud service runtime evolution
Possible requirements
violations
Elasticity control
process enforcedExpected impact
Expected cool-off
period
now
Which will be the behavior?
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?
ICSOC 2014, 5 November, Paris 6
Motivation β Cloud service behavior
Cloud service behavior is complex and can depend on:
β The structure of the cloud service
β The runtime resources used
β The workload of the cloud service
β The control processes enforced, e.g., by the controller
Capturing & using these types of information for
evaluating elasticity behavior
Service
Topology 1
Unit 1
Unit 2
Topology 2
Unit 3
Unit 4
πππ₯1 πππ₯2πππ₯3 πππ₯π
ICSOC 2014, 5 November, Paris 7
Approach
Input:
β Cloud service structure
β Monitoring information of different service parts (e.g., service
units, service topologies)
β Elasticity control process πΈπΆππ
Expected output:
β Metrics evolution, in time, for different service parts and πΈπΆππ
Main mechanism:
β Creating behavior clusters
β Computing closest behavior centroids
ICSOC 2014, 5 November, Paris 8
Gathering information
Select relevant timeseries where πΈπΆππ was enforced before
πΈπΆππ enforcement
Metric measurement
Relevant timeseries
ICSOC 2014, 5 November, Paris 9
Clustering elasticity behaviors
Transform relevant timeseries to multi-dimensional
points
Timeπ‘1 π‘2 π‘πβ¦
Metric
ππ₯
πππ‘ππππππ (π‘1)
πππ‘ππππππ (π‘2)
πππ‘ππππππ (π‘3)
πππ‘ππππππ (π‘4)
β¦
πππ‘ππππππ (π‘π)
Behavior Point
BP
K-meansπΆππ’π π‘ππ1ππ₯
πΆππ’π π‘ππ2ππ₯
πΆππ’π π‘πππ ππ₯πΆ1ππ₯
πΆ2ππ₯
πΆπ ππ₯
ICSOC 2014, 5 November, Paris 10
Computing expected behavior
πΆππ’π π‘ππ1ππ₯
πΆ1ππ₯
πΆππ’π π‘ππ2ππ₯
πΆ2ππ₯
πΆππ’π π‘ππ1ππ¦
πΆ1ππ¦
πΆππ’π π‘πππ ππ¦
πΆπππ¦
πΆππ’π π‘ππ1ππ₯ πΆππ’π π‘ππ2ππ₯πΆππ’π π‘πππ ππ₯
πΆππ’π π‘ππ1ππ¦a b -
πΆππ’π π‘πππ ππ¦c - d
Co-occurrence matrix
Current values
π΅πππ¦π΅πππ₯
Compute centroids
closest to the π΅ππ
πΆπππ¦
πΆ1ππ₯
Transform
to timeseries
ππ₯ππ¦
πΆππ’π π‘πππ ππ₯
πΆπ ππ₯
ICSOC 2014, 5 November, Paris 11
Experiment Settings [1/3]
Setting:
β M2M service
β Video Service
ICSOC 2014, 5 November, Paris 12
Experiment Settings [2/3]
Setting:
β Running on public Flexiant cloud FCO
β MELA & JCatascopia for monitoring cloud services
β Randomly apply ECPs of random type for collecting behavioral
information
β βInterestingβ metrics
ICSOC 2014, 5 November, Paris 13
Experiment Settings [3/3]
ICSOC 2014, 5 November, Paris 14
Experiments β Video Service
Video Service β effect of πΈπΆπ1 on Application Server
πΈπΆπ1 - scale in application server tier β select instance to remove,
stop the video streaming service, remove instance from load
balancer, stop JCatascopia monitoring agent, delete instance
ICSOC 2014, 5 November, Paris 15
Experiments β M2M Service [1/2]
M2M Service β effect of πΈπΆπ7 on the entire cloud service
πΈπΆπ7 - scale in data node service unit β copy data from the instance
to be removed, remove recursively virtual machine
ICSOC 2014, 5 November, Paris 16
Experiments β M2M Service [2/2]
M2M Service β effect on Data End Controller of enforcing πΈπΆπ8πΈπΆπ8 - scale out data node service unit β create new network
interface, create new instance, assign token to node, set cluster
controller
ICSOC 2014, 5 November, Paris 17
Experiments β
Quality of Results
πππππππππ
= πππΈπ π‘ππππ‘ππππ ππ π‘ππππ‘ππππππ§π(ππ π‘ππππ‘πππππ‘ππππ β πππ πππ£πππππ‘ππππ)
2
πππΈπ π‘ππππ‘ππππ β 1
The more random the workload, of the service,
the more difficult to estimate the behavior Lower abstraction layer
=> better estimations
Complex,
unpredictable
metrics => very low
degree of accuracy
ICSOC 2014, 5 November, Paris 18
Conclusions and Future Work
Conclusions
β When controlling a complex cloud service, we need to consider
the impact elasticity control processes have on different service
parts
β ADVISE is indeed able to "advise" elasticity controllers about
cloud service behavior
Future work
β Integrating with rSYBL (https://github.com/tuwiendsg/rSYBL)
β Adapting the control mechanisms of rSYBL to use such
information
ADVISE
β More experiments available at http://tuwiendsg.github.io/ADVISE
β Prototype https://github.com/tuwiendsg/ADVISE
ICSOC 2014, 5 November, Paris 19
Thank you!
Georgiana Copil
[email protected]://dsg.tuwien.ac.at/staff/ecopil/
Distributed Systems GroupVienna University of Technology
Austria