This document discusses principles for engineering elastic Internet of Things (IoT) cloud systems. It outlines the key concepts of elasticity for IoT elements and cloud platform services. It then presents several engineering principles for IoT cloud systems, including enabling virtualization and composition of IoT components, dynamic provisioning of resources, and providing coherence across all levels from IoT elements to cloud services. The document also describes models and techniques for programming elasticity, such as software-defined machines for IoT and frameworks for controlling elastic objects. Finally, it overviews several tools developed by the authors for monitoring, analyzing and controlling elasticity in IoT cloud systems.

1. Principles for Engineering Elastic IoT
Cloud Systems
Hong-Linh Truong
Joint work with Georgiana Copil, Schahram Dustdar, Duc-Hung Le, Daniel
Moldovan, Stefan Nastic
Distributed Systems Group
TU Wien
truong@dsg.tuwien.ac.at
dsg.tuwien.ac.at/research/viecom
SummerSOC 2015, Hersonissos, 2nd July, 2015 1

2. Outline
 IoT cloud systems and engineering principles
 Models and techniques
 Tooling
 Demo
 Conclusions and Future Work
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3. Elastic IoT Cloud systems and
engineering princinples
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4. Scenario
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Offers services for
handling IoT Data
Offers services for
handling IoT Data
Offers services for big,
data analytics
Offers services for big,
data analytics
Offers services for
complex problem solving
using human experts
Offers services for
complex problem solving
using human experts
IoT Cloud Platform
Data Analytics
Platform
Expert Provisioning
Platform
Sensors
<<send data>>
<<analyze data>> <<notify possible
problem>>
<<control/configure
sensors>>
Predictive maintenance companyPredictive maintenance company
<<monitor>>
Chillers
<<predict and solve
problems>>
<<control
services>>
<<control
algorithms>>

5. Elasticity analytics – observations
 Elasticity of IoT elements
 Activate/change sensor deployment/configurations for
required data; changing communication protocols; deploying
new sensors
 Elasticity of cloud platform services
 Deploy/reconfigure cloud services handling changing data
 Elasticity of data analytics
 Switch and combine different types of data analytics
processes and engines due to the severity of problems and
quality of results
 Elasticity of teams of human experts
 Forming and changing different configurations of teams
during specific problems and problem severity
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6. IoT Cloud SystemIoT Cloud System
Our view on IoT Cloud Systems
 IoT cloud systems: IoT elements and cloud services
 A coherent view atop IoT elements and cloud services!
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Application
Hong Linh Truong, Schahram Dustdar: Programming Elasticity in the Cloud. IEEE Computer 48(3): 87-90 (2015)Hong Linh Truong, Schahram Dustdar: Programming Elasticity in the Cloud. IEEE Computer 48(3): 87-90 (2015)

7. Engineering perspectives
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End-to-end
Engineering and
Optimization
Development
and
Production
Symbiosis
Elasticity
Coherence
Hong Linh Truong, Schahram Dustdar: Principles for Engineering IoT Cloud Systems. IEEE Cloud Computing 2(2): 68-76 (2015)Hong Linh Truong, Schahram Dustdar: Principles for Engineering IoT Cloud Systems. IEEE Cloud Computing 2(2): 68-76 (2015)

8. Principles (1-2)
1. Enable virtualization and composition of IoT
components as unit
Selection, composition, pay-per-use
2. Enable emulated/simulated IoT parts working
with production cloud services
Symbiotic development and operation
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9. Principles (3-5)
3. Enable dynamic provisioning of IoT and cloud
service units through uniform marketplaces and
repositories for multiple stakeholders
4. Provide multi-level software stack deployment
and configuration
5. Provide software-defined elasticity and
governance primitive functions for all IoT units
and cloud service units
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10. Principles (6-7)
6. Provide monitoring and analysis for an end-to-
end view on elasticity and dependability
properties
7. Coordinate elasticity to enable a coherent
elastic execution through the whole IoT cloud
systems
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11. Models & Techniques
11SummerSOC 2015

12. Programming frameworks
and languages for software-
defined elastic services
Programming frameworks
and languages for software-
defined elastic services
Deploying and configuring for
elastic object
Deploying and configuring for
elastic object
Controlling Elastic ObjectsControlling Elastic Objects
Monitoring and Analyzing
Elasticity
Monitoring and Analyzing
Elasticity
Programming Elasticity in IoT
Cloud Systems
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 Conceptualizing elastic objects for IoT elements and
cloud services
 Programming „the world of elastic objects“
 Developing elastic cloud software
Hong Linh Truong, Schahram Dustdar: Programming Elasticity in the Cloud. IEEE Computer 48(3): 87-90 (2015)Hong Linh Truong, Schahram Dustdar: Programming Elasticity in the Cloud. IEEE Computer 48(3): 87-90 (2015)
Testing ElasticityTesting Elasticity

13. Software-defined Elastic Service
13
How to represent IoT elements and cloud services under
the same view?
SummerSOC 2015

14. Software-Defined IoT Units
 Virtualizing IoTs resources under “service
units” with software-defined API for
accessing, configuring and controlling units
 Composing and creating gateways and
virtual topologies (of multiple gateways)
 Provisioning (atomic and composite) units
dynamically and on-demand in cloud and
edge computing environments
Software-defined
IoT Unit
FunctionalAPI
Utility
cost-function
IoT resource and functionality binding
Late-bound
policies
Infrastructure capabilities
GovernanceAPI
Dependency
units
Provisioning API
Runtime
mechanisms
Runtime
controllers
(e.g, elasticity)
Non-functionalaspects
Runtime composition
Functionalaspects
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Stefan Nastic, Sanjin Sehic, Le-Duc Hung, Hong-Linh Truong, and Schahram Dustdar (2014). Provisioning Software-defined IoT
Cloud Systems. The 2nd International Conference on Future Internet of Things and Cloud (FiCloud-2014), August27-29, 2014,
Barcelona, Spain.
Stefan Nastic, Sanjin Sehic, Le-Duc Hung, Hong-Linh Truong, and Schahram Dustdar (2014). Provisioning Software-defined IoT
Cloud Systems. The 2nd International Conference on Future Internet of Things and Cloud (FiCloud-2014), August27-29, 2014,
Barcelona, Spain.

15. Software-defined machines (SDMs)
for IoT
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Hong Linh Truong, Schahram Dustdar: Principles for Engineering IoT Cloud Systems. IEEE Cloud Computing 2(2): 68-76 (2015)Hong Linh Truong, Schahram Dustdar: Principles for Engineering IoT Cloud Systems. IEEE Cloud Computing 2(2): 68-76 (2015)

16. Information for elastic configuration
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Duc-Hung Le, Hong-Linh Truong and Schahram Dustdar, Managing Information for Dynamic Configuration of Elastic IoT Cloud Systems, June 2015. On
submission
Duc-Hung Le, Hong-Linh Truong and Schahram Dustdar, Managing Information for Dynamic Configuration of Elastic IoT Cloud Systems, June 2015. On
submission
We must be able to capture different types of information
Types of information
Information model

17. Elasticity primitive operations
17
For the cloud services
For IoT elements
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Primitive operations: actions can be performed on elastic
objects to change their elasticity states
Change communication protocols; change sensor
frequency; activating/deactivating sensors,
gateways configuration, etc.
Change communication protocols; change sensor
frequency; activating/deactivating sensors,
gateways configuration, etc.

18. Elasticity Model for Cloud Services
Moldovan D., G. Copil,Truong H.-L., Dustdar S. (2013). MELA:
Monitoring and Analyzing Elasticity of Cloud Service. CloudCom
2013
Moldovan D., G. Copil,Truong H.-L., Dustdar S. (2013). MELA:
Monitoring and Analyzing Elasticity of Cloud Service. CloudCom
2013
Elasticity space functions: to determine if a
service unit/service is in the “elasticity behavior”
Elasticity space functions: to determine if a
service unit/service is in the “elasticity behavior”
Elasticity Pathway functions: to characterize the
elasticity behavior from a general/particular view
Elasticity Pathway functions: to characterize the
elasticity behavior from a general/particular view
Elasticity Space
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19. Specifying and controling elasticity
Basic constructs
Schahram Dustdar, Yike Guo, Rui Han,
Benjamin Satzger, Hong Linh Truong:
Programming Directives for Elastic Computing.
IEEE Internet Computing 16(6): 72-77 (2012)
Schahram Dustdar, Yike Guo, Rui Han,
Benjamin Satzger, Hong Linh Truong:
Programming Directives for Elastic Computing.
IEEE Internet Computing 16(6): 72-77 (2012)
SYBL (Simple Yet Beautiful Language) for
specifying elasticity requirements
SYBL-supported requirement levels
Cloud Service Level
Service Topology Level
Service Unit Level
Relationship Level
Programming/Code Level
Current SYBL implementation
in Java using Java annotations
@SYBLAnnotation(monitoring=„“,constraints=„“,strategies=„
“)
in XML
<ProgrammingDirective><Constraints><Constraint
name=c1>...</Constraint></Constraints>...</Programm
ingDirective>
as TOSCA Policies
<tosca:ServiceTemplate name="PilotCloudService">
<tosca:Policy name="St1"
policyType="SYBLStrategy"> St1:STRATEGY
minimize(Cost) WHEN high(overallQuality)
</tosca:Policy>...
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Runtime needs elasticity
primitive opertations!

20. TOOLS
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21. Monitoring, Controlling and Testing
IoT Cloud Systems
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Check: http://tuwiendsg.github.io/iCOMOT/demo.html

22. Elasticity Information as a Service
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https://github.com/tuwiendsg/ELISEhttps://github.com/tuwiendsg/ELISE
Collecting configuration information from
different phases
Scalable and extensible runtime
system
Duc-Hung Le, Hong-Linh Truong and Schahram Dustdar, Managing
Information for Dynamic Configuration of Elastic IoT Cloud Systems,
June 2015. On submission
Duc-Hung Le, Hong-Linh Truong and Schahram Dustdar, Managing
Information for Dynamic Configuration of Elastic IoT Cloud Systems,
June 2015. On submission

23. SALSA- Multi-cloud, multi-stack,
complex topologies configuration
23
 Well-defined APIs for manipulating and provisioning objects
 Support different types of objects, e.g., VMs, OS containers,
services, service containers, IoT sensors, and gateways
Data center services Sensors
https://github.com/tuwiendsg/SALSAhttps://github.com/tuwiendsg/SALSA
SummerSOC 2015

24. High level elasticity control
#SYBL.CloudServiceLevel
Cons1: CONSTRAINT responseTime < 5 ms
Cons2: CONSTRAINT responseTime < 10 ms
WHEN nbOfUsers > 10000
Str1: STRATEGY CASE fulfilled(Cons1) OR
fulfilled(Cons2): minimize(cost)
#SYBL.ServiceUnitLevel
Str2: STRATEGY CASE ioCost < 3 Euro :
maximize( dataFreshness )
#SYBL.CodeRegionLevel
Cons4: CONSTRAINT dataAccuracy>90%
AND cost<4 Euro
#SYBL.CloudServiceLevel
Cons1: CONSTRAINT responseTime < 5 ms
Cons2: CONSTRAINT responseTime < 10 ms
WHEN nbOfUsers > 10000
Str1: STRATEGY CASE fulfilled(Cons1) OR
fulfilled(Cons2): minimize(cost)
#SYBL.ServiceUnitLevel
Str2: STRATEGY CASE ioCost < 3 Euro :
maximize( dataFreshness )
#SYBL.CodeRegionLevel
Cons4: CONSTRAINT dataAccuracy>90%
AND cost<4 Euro
Georgiana Copil, Daniel Moldovan, Hong-Linh Truong, Schahram Dustdar, "SYBL: an Extensible Language for Controlling
Elasticity in Cloud Applications", 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid),
May 14-16, 2013, Delft, Netherlands
Georgiana Copil, Daniel Moldovan, Hong-Linh Truong, Schahram Dustdar, "SYBL: an Extensible Language for Controlling
Elasticity in Cloud Applications", 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid),
May 14-16, 2013, Delft, Netherlands
https://github.com/tuwiendsg/rSYBLhttps://github.com/tuwiendsg/rSYBL
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25. Elasticity space and pathway analytics
25
Daniel Moldovan, Georgiana Copil, Hong-Linh Truong, Schahram Dustdar, "MELA: Elasticity Analytics for Cloud Services", International Journal of Big
Data Intelligence, 2015, Vol. 2, No. 1
Daniel Moldovan, Georgiana Copil, Hong-Linh Truong, Schahram Dustdar, "MELA: Elasticity Analytics for Cloud Services", International Journal of Big
Data Intelligence, 2015, Vol. 2, No. 1
https://github.com/tuwiendsg/MELAhttps://github.com/tuwiendsg/MELA
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26. rtGovOps – Governance
capabilities
 Governance capabilities:
 Any function that „manipulates“ an IoT cloud resource
 Building blocks of operational governance (GovOps)
processes
 Executed „inside“ software-defined machines (SDMs)
 Governance processes/strategies
 Functional configuration
 Performance
 Uncertainty study
 Risk study
26
https://github.com/tuwiendsg/GovOps/
SummerSOC 2015
Stefan Nastic, Michael Vögler, Christian Inzinger, Hong-Linh Truong, Schahram Dustdar, "rtGovOps: A Runtime Framework for Governance in Large-
scale Software-defined IoT Cloud Systems", The 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, 2015
Stefan Nastic, Michael Vögler, Christian Inzinger, Hong-Linh Truong, Schahram Dustdar, "rtGovOps: A Runtime Framework for Governance in Large-
scale Software-defined IoT Cloud Systems", The 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, 2015

27. iCOMOT -- Toolsets and actions for
IoT Cloud Systems
27SummerSOC 2015
http://tuwiendsg.github.io/iCOMOT/
Hong-Linh Truong, Georgiana Copil, Schahram Dustdar, Duc-Hung Le, Daniel Moldovan, Stefan Nastic, "iCOMOT – a Toolset for Managing IoT Cloud
Systems", 16th IEEE International Conference on Mobile Data Management, 15-18 June, 2015, Pittsburg, USA. (Demo)
Hong-Linh Truong, Georgiana Copil, Schahram Dustdar, Duc-Hung Le, Daniel Moldovan, Stefan Nastic, "iCOMOT – a Toolset for Managing IoT Cloud
Systems", 16th IEEE International Conference on Mobile Data Management, 15-18 June, 2015, Pittsburg, USA. (Demo)

28. DEMO
http://tuwiendsg.github.io/iCOMOT/
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29. Conclusions and Outlook
 Engineering IoT cloud systems
 Deal with complex IoT elements and cloud services
 Coordinating elasticity across IoT platforms and
cloud platforms is needed
 Engineering an end-to-end elasticity for IoT cloud
systems needs a complex set of tools
 Ongoing work
 Coordinated elasticity control for people and data
elasticity in IoT cloud systems (ICSOC submissions)
 Using iCOMOT to support testing, privacy/risk and
uncertainty studies for IoT cloud systems
 Data elasticity management in IoT cloud systems
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30. Thanks for your
attention!
Questions?
Hong-Linh Truong
Distributed Systems Group
TU Wien
dsg.tuwien.ac.at/research/viecom
SummerSOC 2015 30