The emerging cloud computing and socially-enhanced services computing concepts o ffer several service models, including software-based and human-based services, for programming elastic composite applications utilizing human and software capabilities. However, the current view of elasticity still is centered on the elasticity of software resources. In this talk, we will systematically conceptualize principles for supporting multi-dimensional elasticity, covering resources (software and humans), quality (e.g., quality of service and quality of data), and costs and benefits (e.g., price, rewards and incentives). We then discuss enabling techniques for supporting multi-dimensional elasticity in data-, compute- and human-intensive processes. Finally, we will discuss some initial results and approaches on modeling multi-dimensional elasticity, programming software and human services, and elasticity reasoning.

1. Elastic Processes on Clouds of Hybrid
Services: Principles, Enabling Techniques
and Approaches
Hong-Linh Truong
Distributed Systems Group,
Vienna University of Technology
truong@infosys.tuwien.ac.at
http://www.infosys.tuwien.ac.at/staff/truong
SummerSOC 12, 3 Jul 2012 1

2. Acknowledgement: some results from joint
works with:
Kamal Bhattacharya, Schahram Dustdar, Muhammad Zuhri Catur
Candra, Aditya Ghose, Lam-Son Le, Frank Leymann, Vitaliy
Liptchinsky, Tran-Vu Pham, Michael Reiter, Mirela Riveni, Ognjen
Scekic, Quang-Hieu Vu, and Rostyslav Zaboloznyi
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3. An example of elastic processes
Recap: Schahram‘s presentation yesterday
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4. Hybrid services in the cloud
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5. How to realize elasticity in hybrid
systems?
Attention: ongoing work  not everything is
well tested !
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6. Outline
 Vienna Elastic Computing Model
 Incorporating humans into programming
paradigms
 Programming hybrid services in the cloud
 Modeling elasticity constraints
 Reasoning elasticity constraints
 Conclusions and future work
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7. VIENNA ELASTIC COMPUTING
MODEL
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8. Multi-dimensional Elasticity
Units can be
networks, middleware, Sa
aS, PaaS
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9. Vienna Elastic Computing Model
multi-dimensional elasticity, service computing models
and cloud provisioning models
Schahram Dustdar, Hong-Linh Truong, "Virtualizing Software and Humans for Elastic Processes in Multiple Clouds - a Service
Management Perspective" International Journal of Next-Generation Computing, To appear, 2012
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10. INCORPORATING HUMANS
INTO PROGRAMMING
PARADIGM
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11. Incorporate humans into a
programming paradigm
• Abstracting human compute units as program elements
Programming • Extending programming languages to support human
languages compute units
• Data/control flows via extensible APIs
• Shared memory (e.g., human –software –
Multiple human), message passing (human-to-human), artifact-
programming centric, etc., via APIs working atop the compute unit
models abstraction layer
• Computing capability /profile management: human
computing power, reputation and incentive models
Execution • Monitoring: monitoring and enforcing
environment incentives/rewards, quality of results, availability
• Communication: between human-middleware, among
ICU/SCU , within SCU for exchanging artifacts and
comprehensing social tasks
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12. Incorporate humans into a
programming paradigm program human actions
and dependencies
program incentive condition
Program
elements
and rewarding action
program result evaluation
method
Program languages and programming models
Provisioning/Negotiation/Execution API
IC IC SC
U U U ICU SCU
Abstraction of Human Compute Units
Communication Capability/Profile Software Human-to-middleware
Monitoring
Management Compute interfaces:
Service-based Middleware Units •visualization of collective tasks
•embedding of common forms
•mobile app
Volunteers Professionals
Individual Team 12
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13. Existing technologies are not
adequate
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14. PROGRAMMING HYBRID
SERVICES IN THE CLOUD
Hong-Linh Truong, Schahram Dustdar, Kamal Bhattacharya, Programming Hybrid Services in the Cloud,
Working paper,2012
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15. HBS Communication Interface
 Based on well known technologies for
integrating human requests/responses into SBS
 Emails, web interfaces, Web services, etc.
 Some special characteristics
 The same protocol can be used for multiple layers
(middleware, application, OS) -- unlike in SBS
 Only asyschronous communication and unknown
upper-bound delay
 Can support message passing and shared
memory models
 hbsi  hbsj or hbsi  sbs  hbsj
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16. Human Power Unit (HPU)
 Humans are determined via skills/skill levels
 A particular HBS cloud can define its own sets of
skills (CS) and skill levels (SK)
 Similar to Amazon defines its own EC unit
 Different ways to make sure skill and skill levels
declared in a cloud consistent (e.g., via testing and
monitoring).
HPU Definition: HPU is a value describing the computing
power of an HBS measured in an abstract unit. A cloud of
HBS has a pre-defined basic power
unit, hpuθ, corresponding to the baseline skill bsθ of the
cloud.”
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17. Human Power Unit (HPU) – our
starting point
HPU for a particular (skill,
skill level)
HPU for a set of (skill, skill
level), no weighted factors
Decomposition/compositi
on of HPU for a shared
HBS
Given an HBS, its theoretical HPU can be larger
or smaller than its real HPU – unlike SBS
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18. HBS Instances Provisioning
Types of • Individual Compute Unit (ICU)
services • Social Compute Unit (SCU
• iICU(CS, HPU, price, utilization,loc
Instances ation, APIs)
Description • iSCU(CS,HPU, price, utilization,co
s nnectedness, location, APIs)
• Other (traditional) NFPs
• utilization
Pricing • offering communication APIs
factors • connectedness
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19. Cloud APIs for Provisioning Hybrid
Services
APIs hide low-level platforms and utilize low level HBS
communication interfaces
APIs for HBS information and APIs for HBS execution and
management communication
• listSkills();listSkillLevels();
• listICU();listSCU() • runRequestOnHBS ()
• negotiateHBS() • receiveResultFromHBS()
• startHBS() • sendMessageToHBS()
• suspendHBS () • receiveMessageFromHBS()
• resumeHBS ()
• stopHBS()
• reduceHBS()
• expandHBS() 2012
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20. Prototype (simulated environment)
Combined with Jcloud/boto for real SBS
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21. Framework for Utilizing Hybrid
Services
IT Management
Smart City Management
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22. Modeling HPU-aware Task
Dependency Graphs
Link management skills and compliance constraints to tasks
required HBS
Can be specified in TOSCA!
Modeled in dependency graphs
22
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23. Programming a combination of
HBS and SBS
e.g., preparing/managing inputs/outputs for HBS using SBS
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24. Forming iSCUs
 Done by consumers or cloud providers
Business As
Usual
Corrective
Action
HBS
Constraints
SCU Formation
Algorithms
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25. Configuring iSCU
 Establish „connectedness“ based on compliance
constraints and network topology
 Addional cost might occur!
 Program SBS and HBS for the iSCU to have a
complete working environment.
 Different connectedness
 E.g., Ring-based, star-based, and master-slave
topologies
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26. Selecting HBS: Some algorithms
 Several algorithms can be built based on existing team
formation algorithms which do not consider dependency
graphs
 Different weighted factors can be considered
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27. Forming iSCU by minimizing cost
and considering no direction
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28. Example of star-based iSCU using
Dropbox as a communication hub
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29. Change Model for Task Graph’s
Human Power Unit
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30. MODELING ELASTICITY
CONSTRAINT
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31. Elasticitiy modeling
• Beyond traditional QoS metrics and for only
services
• Modeling elasticity constraints
• Attaching elasticity constraints to processes at
the design time for composition and runtime
elasticity
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32. Beyond QoS metrics (1)
And many more metrics!
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33. Beyond QoS metrics (2)
Elastic dynamic properties associated with:
Data marketplaces and DaaS
Service APIs, data APIs and data assets
Quang Hieu Vu, Tran Vu Pham, Hong-Linh Truong, Schahram Dustdar, Rasool
Asal, DEMODS: A Description Model for Data-as-a-Service, The 26th IEEE
International Conference on Advanced Information Networking and Applications
(AINA-2012)
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34. Elastic Profile Using Rules
process
process
fragment
activity
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35. Declarative constraints based first-
order logic and set theory
Lam-Son Le, Hong-Linh Truong, Adytia Ghose, Schahram
Dustdar, On Elasticity and Constrainedness of Business
Services Provisioning, 9th International Conference on
Services Computing (SCC 2012)
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36. REASON THE ELASTICITY
CONSTRAINTS
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37. Runtime quality, cost and benefits
evaluation and reasoning
principles
Runtime:
Elastic
processes
Part A Part B Part C
Quality, cost model i model j model k
and benefits
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38. Composable cost evaluation
Tran Vu Pham, Hong-Linh Truong, Schahram Dustdar "Elastic High Performance
Applications - A Composition Framework", The 2011 Asia-Pacific Services
Computing Conference (IEEE APSCC 2011), (c) IEEE Computer
Society, December 12 - 15, 2011, Jeju, Korea
Hong Linh Truong, Schahram Dustdar: Composable cost estimation and monitoring for computational applications in cloud
computing environments. Procedia CS 1(1): 2175-2184 (2010)
38
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39. Runtime evaluation of quality of
data in workflows
Michael Reiter, Uwe Breitenbuecher, Schahram Dustdar, Dimka Karastoyanova, Frank Leymann, Hong-Linh Truong, A Novel Framework for Monitoring
and Analyzing Quality of Data in Simulation Workflows, (c)IEEE Computer Society, The 7th IEEE International Conference on e-Science, 5-8 December,
2011, Stockholm, Sweden 3 Jul 2012
SummerSOC 12, 39
Michael Reiter, Hong-Linh Truong, Schahram Dustdar, Dimka Karastoyanova, Robert Krause, Frank Leymann, Dieter Pahr, On Analyzing Quality of
Data Infuences on Performance of Finite Elements driven Computational Simulations, EuroPar 2012

40. Incentive Monitoring
Ognjen Scekic, Hong-Linh Truong, Schahram Dustdar, "Modeling Rewards and Incentive Mechanisms for Social BPM",, Short paper, 10th International
Conference on Business Process Management (BPM2012), (c)Springer-Verlag, September 3-6, 2012, Tallinn, Estonia
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41. Put things together -- IDE for
elastic processes
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42. Put things together – from high-level
languages to programmable units
 Statelet: a declarative coordination language for
collaboration processes
 Statelet statements can be mapped to APIs for
programmable units
Vitaliy Liptchinsky, Roman Khazankin, Hong Linh Truong, Schahram
Dustdar: Statelets: Coordination of Social Collaboration Processes.
COORDINATION 2012
Provisioning/Negotiation/Execution API
ICU ICU SCU ICU SCU
42
Abstraction of Human Compute Units
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43. Conclusions and Future Work
 Novel models and APIs are needed for integrating
humans into program paradigms
 We need to address the whole stack
 Elasticity modeling, monitoring and reasoning
 Mapping high level languages to APIs of hybrid services
 Future works
 Several issues mentioned here are ongoing work
 Declarative languages for specifying elasticity
 Methods for evaluation and experiments
 Some open questions will be addressed in FP7
CELAR
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44. Thanks for
your attention
Hong-Linh Truong
Distributed Systems Group
Vienna University of Technology
truong@infosys.tuwien.ac.at
http://www.infosys.tuwien.ac.at/staff/truong
SummerSOC 12, 3 Jul 2012 44