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Elastic cognitive systems 18 6-2015-dustdar

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Schahram Dustdar from Vienna University of Technology presented on the Cognitive Systems Institute Group Speaker Series call on June 18,2015.

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Elastic Cognitive Systems Cognitive Systems Institute Group, 18 June 2015 Schahram Dustdar Distributed Systems Group TU Vienna dsg.tuwien.ac.at
eHealth & Smart Health networksGame Machine Telephone PC DVD Audio TV STBDVC Smart Homes Smart eGovernments & eAdministrationsSmart Energy Networks Smart Evolution – People, Services,Things Elastic Systems Smart Transport Networks
Marine Ecosystem: http://www.xbordercurrents.co.uk/wildlife/marine-ecosystem-2 Think Ecosystems: People, Services, Things Diverse users with complex networked dependencies and intrinsic adaptive behavior – has: 1. Robustness mechanisms: achieving stability in the presence of disruption 2. Measures of health: diversity, population trends, other key indicators
Approach Elastic Computing People ThingsSoftware
Elastic Computing for the Internet of Things
Smart City Dubai Pacific Controls Command Control Center
Connecting machines and people Event Analyzer on PaaS Peak Operation Other stakeholders ... events stream Normal Operation Human Analysts Peak OperationNormal Operation Machine/Human Event Analyzers Critical situation 1 Experts SCU (Big) Data analytics Wf. A Wf. B Critical situation 2 Cloud DaaS Data analytics M2M PaaS Cloud IaaS Operation problem Maintenance process Core principles:  Human computation capabilities under elastic service units  Augmenting human-based units together with software-based units
Elasticity ≠ Scaleability Resource elasticity Software / human-based computing elements, multiple clouds Quality elasticity Non-functional parameters e.g., performance, quality of data, service availability, human trust Costs & Benefit elasticity rewards, incentives Elasticity
Specifying and controling elasticity Basic primitives 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>...
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 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 Space
Elastic Computing for People
Human-based service elasticity  Which types of human-based service instances can we provision?  How to provision these instances?  How to utilize these instances for different types of tasks?  Can we program these human-based services together with software-based services  How to program incentive strategies for human services?
Computing Models Machine-based Computing Human-based Computing Things-based computing Grid Processing Unit ArchitectureComm. SMP S. Dustdar, H. Truong, “Virtualizing Software and Humans for Elastic Processes in Multiple Clouds – a Service Management Perspective”, in International Journal of Next Generation Computing, 2012 Ad hoc networks Web of things
Specifying and controling elasticity of human-based services What if we need to invoke a human? #predictive maintanance analyzing chiller measurement #SYBL.ServiceUnitLevel Mon1 MONITORING accuracy = Quality.Accuracy Cons1 CONSTRAINT accuracy < 0.7 Str1 STRATEGY CASE Violated(Cons1): Notify(Incident.DEFAULT, ServiceUnitType.HBS)
Evolution of Human-Based Computing • Tim Berners-Lee’s Social Machines: “a computational entity that blends computational and social processes” • Our view: • People AND Computational Units • Complex Workflows • Respond to ad-hoc situations • Leverage human creativity • Embrace uncertainty • No over-regulation • Human-driven adaptation
 Complex collaborative patterns/workflows  On-demand (machine-driven)  Open-call (human-driven)  Crowd satisfaction and non-monetary motivation  Incentives and rewards  Reputation, accountability  Career ladders, reputation transfer, virtual careers, hierarchy Developments in Human-Based Comp. ① ② ③
On-demand Collaborative Use-case  Hybrid ad-hoc collectives  Provisioned on-demand by the platform (e.g., SmartSociety, SCU) ①
Elastic SCU provisioning Elastic profile SCU (pre-)runtime/static formation Cloud APIs Muhammad Z.C. Candra, Hong-Linh Truong, and Schahram Dustdar, Provisioning Quality-aware Social Compute Units in the Cloud, ICSOC 2013. Algorithms  Ant Colony Optimization variants  FCFS  Greedy SCU extension/reduction  Task reassignment based on trust, cost, availability Mirela Riveni, Hong-Linh Truong, and Schahram Dustdar, On the Elasticity of Social Compute Units, CAISE 2014 ①
Motivating Scenario #2: Human-driven: Collaborative Ride-Sharing ②
Open-call Collaborative Use-case  The platform composes the possible/optimal execution plans based on subtask offers submitted by crowd members.  Plans are recommended/offered to interested crowd members  Crowd members are able to negotiate for participating in execution of multiple plans concurrently, effectively making only a subset of them happen.  Negotiation orchestrated by the platform Composition Recommendation NegotiationExecution Feedback ② request
SmartSociety Platform M. Rovatsos et al., Agent protocols for social computation Quality-aware Social Compute Units, in Metaheuristics for Smart Cities, 2015. P. Zeppezauer et al., Virtualizing communication for hybrid and diversity-aware collective adaptive systems, WESOA@ICSOC, 2014. http://www.smart-society-project.eu/publications/deliverables/D_6_1/ users crowd of human and machine peers ②
Crowd satisfaction and non-monetary motivation [2] Kittur, A., et al.: The future of crowd work. Proc. of CSCW ’13, New York, USA. • How to make virtual labor market competitive and attractive for skilled workers? [2]  Complex collaborative patterns/workflows  Hierarchy/structure  Worker satisfaction and non-monetary motivation  Reputation, accountability  Career ladders, reputation transfer, virtual careers Incentive Management ③
Operational Context ③
Automated Incentive Management abstraction interlayer ③
Research Questions abstraction interlayer • Identify common incentivizing patterns in existing systems • Express the patterns as compositions of fundamental, platform-agnostic incentive elements. ③
Modeling Incentives  Examined incentive strategies in over 200 existing social computing platforms  Examined incentive mechanisms in economics, management science, sociology, psychology  Identified fundamental incentive mechanisms in use today and their constituent elements  New mechanisms can be built by composing and customizing well-known incentive elements [3] Scekic, O., Truong, H.-L., Dustdar, S.: Incentives and rewarding in social computing. Communications of the ACM, 56(6), 72 (2013). ③
Research Questions abstraction interlayer • Virtualize system-specific worker team representations into a system- agnostic model amenable to the application of incentives. • Develop primitives for executing (applying) incentive actions. ③
Abstraction Interlayer  PRINC (PRogrammable INcentives) framework.  Allows modeling of human worker teams – storing and altering worker metrics – storing and altering worker structure – storing behavioral history and scheduling of incentive actions  Event-based communication with underlying socio-technical system [4] Scekic, O., Truong, H.-L., Dustdar, S.: Modeling rewards and incentive mechanisms for social BPM. Proc. BPM’12 (pp. 150–155), Talinn, (2013). [5] Scekic, O., Truong, H.-L., Dustdar, S.: Programming incentives in information systems. In Proc. CAiSE’13 (pp. 688–703), Valencia (2013). ③
Research Questions abstraction interlayer • Design a declarative, human-friendly way of modeling incentives out of fundamental incentive elements. • Translate the modeled incentive strategy into executable actions. ③
A Domain-Specific Language for Incentives [6] Scekic, O., Truong, H.-L., Dustdar, S.: Managing Incentives in Social Computing Systems with PRINGL. WISE’14 (pp. 415—424), Thessaloniki, Greece  PRINGL – PRogrammable INcentive Graphical Language  Visuo-textual language – Graphical elements for modeling and composing incentive elements – Traditional code snippets for additional business logic  System-independent  Human-friendly syntax  Elements can be stored, shared, reused  Translated to code executable on abstraction interlayer ③
Managing Incentives with PRINGL
Conclusions  Elastic Cognitive Computing is the next step in the man & machine „symbiosis“  Novel environments needed for  Complex collaboration patterns-> include Things, Services  Incentive management  Reputation transfer  Virtual careers
Thanks for your attention! Prof. Dr. Schahram Dustdar Distributed Systems Group TU Wien dsg.tuwien.ac.at

More Related Content

Elastic cognitive systems 18 6-2015-dustdar

  • 1. Elastic Cognitive Systems Cognitive Systems Institute Group, 18 June 2015 Schahram Dustdar Distributed Systems Group TU Vienna dsg.tuwien.ac.at
  • 2. eHealth & Smart Health networksGame Machine Telephone PC DVD Audio TV STBDVC Smart Homes Smart eGovernments & eAdministrationsSmart Energy Networks Smart Evolution – People, Services,Things Elastic Systems Smart Transport Networks
  • 3. Marine Ecosystem: http://www.xbordercurrents.co.uk/wildlife/marine-ecosystem-2 Think Ecosystems: People, Services, Things Diverse users with complex networked dependencies and intrinsic adaptive behavior – has: 1. Robustness mechanisms: achieving stability in the presence of disruption 2. Measures of health: diversity, population trends, other key indicators
  • 5. Elastic Computing for the Internet of Things
  • 6. Smart City Dubai Pacific Controls Command Control Center
  • 7. Connecting machines and people Event Analyzer on PaaS Peak Operation Other stakeholders ... events stream Normal Operation Human Analysts Peak OperationNormal Operation Machine/Human Event Analyzers Critical situation 1 Experts SCU (Big) Data analytics Wf. A Wf. B Critical situation 2 Cloud DaaS Data analytics M2M PaaS Cloud IaaS Operation problem Maintenance process Core principles:  Human computation capabilities under elastic service units  Augmenting human-based units together with software-based units
  • 8. Elasticity ≠ Scaleability Resource elasticity Software / human-based computing elements, multiple clouds Quality elasticity Non-functional parameters e.g., performance, quality of data, service availability, human trust Costs & Benefit elasticity rewards, incentives Elasticity
  • 9. Specifying and controling elasticity Basic primitives 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>...
  • 10. 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 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 Space
  • 12. Human-based service elasticity  Which types of human-based service instances can we provision?  How to provision these instances?  How to utilize these instances for different types of tasks?  Can we program these human-based services together with software-based services  How to program incentive strategies for human services?
  • 13. Computing Models Machine-based Computing Human-based Computing Things-based computing Grid Processing Unit ArchitectureComm. SMP S. Dustdar, H. Truong, “Virtualizing Software and Humans for Elastic Processes in Multiple Clouds – a Service Management Perspective”, in International Journal of Next Generation Computing, 2012 Ad hoc networks Web of things
  • 14. Specifying and controling elasticity of human-based services What if we need to invoke a human? #predictive maintanance analyzing chiller measurement #SYBL.ServiceUnitLevel Mon1 MONITORING accuracy = Quality.Accuracy Cons1 CONSTRAINT accuracy < 0.7 Str1 STRATEGY CASE Violated(Cons1): Notify(Incident.DEFAULT, ServiceUnitType.HBS)
  • 15. Evolution of Human-Based Computing • Tim Berners-Lee’s Social Machines: “a computational entity that blends computational and social processes” • Our view: • People AND Computational Units • Complex Workflows • Respond to ad-hoc situations • Leverage human creativity • Embrace uncertainty • No over-regulation • Human-driven adaptation
  • 16.  Complex collaborative patterns/workflows  On-demand (machine-driven)  Open-call (human-driven)  Crowd satisfaction and non-monetary motivation  Incentives and rewards  Reputation, accountability  Career ladders, reputation transfer, virtual careers, hierarchy Developments in Human-Based Comp. ① ② ③
  • 17. On-demand Collaborative Use-case  Hybrid ad-hoc collectives  Provisioned on-demand by the platform (e.g., SmartSociety, SCU) ①
  • 18. Elastic SCU provisioning Elastic profile SCU (pre-)runtime/static formation Cloud APIs Muhammad Z.C. Candra, Hong-Linh Truong, and Schahram Dustdar, Provisioning Quality-aware Social Compute Units in the Cloud, ICSOC 2013. Algorithms  Ant Colony Optimization variants  FCFS  Greedy SCU extension/reduction  Task reassignment based on trust, cost, availability Mirela Riveni, Hong-Linh Truong, and Schahram Dustdar, On the Elasticity of Social Compute Units, CAISE 2014 ①
  • 19. Motivating Scenario #2: Human-driven: Collaborative Ride-Sharing ②
  • 20. Open-call Collaborative Use-case  The platform composes the possible/optimal execution plans based on subtask offers submitted by crowd members.  Plans are recommended/offered to interested crowd members  Crowd members are able to negotiate for participating in execution of multiple plans concurrently, effectively making only a subset of them happen.  Negotiation orchestrated by the platform Composition Recommendation NegotiationExecution Feedback ② request
  • 21. SmartSociety Platform M. Rovatsos et al., Agent protocols for social computation Quality-aware Social Compute Units, in Metaheuristics for Smart Cities, 2015. P. Zeppezauer et al., Virtualizing communication for hybrid and diversity-aware collective adaptive systems, WESOA@ICSOC, 2014. http://www.smart-society-project.eu/publications/deliverables/D_6_1/ users crowd of human and machine peers ②
  • 22. Crowd satisfaction and non-monetary motivation [2] Kittur, A., et al.: The future of crowd work. Proc. of CSCW ’13, New York, USA. • How to make virtual labor market competitive and attractive for skilled workers? [2]  Complex collaborative patterns/workflows  Hierarchy/structure  Worker satisfaction and non-monetary motivation  Reputation, accountability  Career ladders, reputation transfer, virtual careers Incentive Management ③
  • 25. Research Questions abstraction interlayer • Identify common incentivizing patterns in existing systems • Express the patterns as compositions of fundamental, platform-agnostic incentive elements. ③
  • 26. Modeling Incentives  Examined incentive strategies in over 200 existing social computing platforms  Examined incentive mechanisms in economics, management science, sociology, psychology  Identified fundamental incentive mechanisms in use today and their constituent elements  New mechanisms can be built by composing and customizing well-known incentive elements [3] Scekic, O., Truong, H.-L., Dustdar, S.: Incentives and rewarding in social computing. Communications of the ACM, 56(6), 72 (2013). ③
  • 27. Research Questions abstraction interlayer • Virtualize system-specific worker team representations into a system- agnostic model amenable to the application of incentives. • Develop primitives for executing (applying) incentive actions. ③
  • 28. Abstraction Interlayer  PRINC (PRogrammable INcentives) framework.  Allows modeling of human worker teams – storing and altering worker metrics – storing and altering worker structure – storing behavioral history and scheduling of incentive actions  Event-based communication with underlying socio-technical system [4] Scekic, O., Truong, H.-L., Dustdar, S.: Modeling rewards and incentive mechanisms for social BPM. Proc. BPM’12 (pp. 150–155), Talinn, (2013). [5] Scekic, O., Truong, H.-L., Dustdar, S.: Programming incentives in information systems. In Proc. CAiSE’13 (pp. 688–703), Valencia (2013). ③
  • 29. Research Questions abstraction interlayer • Design a declarative, human-friendly way of modeling incentives out of fundamental incentive elements. • Translate the modeled incentive strategy into executable actions. ③
  • 30. A Domain-Specific Language for Incentives [6] Scekic, O., Truong, H.-L., Dustdar, S.: Managing Incentives in Social Computing Systems with PRINGL. WISE’14 (pp. 415—424), Thessaloniki, Greece  PRINGL – PRogrammable INcentive Graphical Language  Visuo-textual language – Graphical elements for modeling and composing incentive elements – Traditional code snippets for additional business logic  System-independent  Human-friendly syntax  Elements can be stored, shared, reused  Translated to code executable on abstraction interlayer ③
  • 32. Conclusions  Elastic Cognitive Computing is the next step in the man & machine „symbiosis“  Novel environments needed for  Complex collaboration patterns-> include Things, Services  Incentive management  Reputation transfer  Virtual careers
  • 33. Thanks for your attention! Prof. Dr. Schahram Dustdar Distributed Systems Group TU Wien dsg.tuwien.ac.at