![The -calculus
a declarative, functional formalism
inherently concurrent model of computation
basic data structure: multiset (chemical solution)
– passive molecules: booleans, integers, tuples, naming molecules
– active molecules: -abstraction
reaction: active molecules capture other molecules
– x.M, N → M[x:=N]
execution: perform reactions until a stable (inert) chemical
solution is resulted](https://image.slidesharecdn.com/jra-2-3sztakichemicalworkflow-120103083022-phpapp02/85/S-CUBE-LP-Chemical-Modeling-Workflow-Enactment-based-on-the-Chemical-Metaphor-31-320.jpg)
![Acknowledgements
The research leading to these results has
received funding from the European
Community’s Seventh Framework
Programme [FP7/2007-2013] under grant
agreement 215483 (S-Cube).](https://image.slidesharecdn.com/jra-2-3sztakichemicalworkflow-120103083022-phpapp02/85/S-CUBE-LP-Chemical-Modeling-Workflow-Enactment-based-on-the-Chemical-Metaphor-52-320.jpg)
S-CUBE LP: Chemical Modeling: Workflow Enactment based on the Chemical Metaphor
- 1. S-Cube Learning Package Chemical Modeling: Workflow Enactment based on the Chemical Metaphor INRIA, CNR, SZTAKI Zsolt Németh, SZTAKI www.s-cube-network.eu
- 2. Learning Package Categorization S-Cube Service Infrastructure Multi-level and self-adaptation Supporting adaptation of service-based applications
- 3. Learning Package Overview Workflow, workflow management Problem and requirement analysis – enactment in large-scale heterogeneous environments A chemical metaphor for workflow enactment A coordination model for workflow enactment formalized in the -calculus Further challenges
- 4. This talk is about workflow but… Workflow is just an example – It is a common programming model for grids – Features many coordination problems Focus: coordination – Large-scale distributed, dynamic, error prone environment and applications – Some degree of autonomy, adaptability, self-* must be provided
- 5. Workflow “The computerized facilitation or automation of a business process in whole or in part” – The Workflow Management Coalition a collection of activities that are processed in some order and where both data-flow and control-flow relationships may be present
- 6. The workflow Management Reference Model (Workflow Management Coalition) Process Design Process Analysis, Modeling & Definition Tools & Definition Process Definition Build time Run time Process Instantiation Workflow Enactment Service & Control Interaction with User (human) Applications & Users & Application Tools Tools
- 7. Scope of enactment Abstract workflow Problem standalone application components the order in which they are executed names, references, etc. are logical Abstract workflow Represented by a graph typically: DAG Concrete workflow Physical environment
- 8. Scope of enactment Concrete workflow Problem standalone application components the order in which they are executed selected resources, services Abstract workflow additional activities (e.g. file transfer, staging, etc.) all names, references, etc. are physical Concrete workflow Physical environment
- 9. Learning Package Overview Workflow, workflow management Problem and requirement analysis – enactment in large-scale heterogeneous environments A chemical metaphor for workflow enactment A coordination model for workflow enactment formalized in the -calculus Further challenges
- 10. A common scenario (e.g. grid workflow) Abstract workflow Workflow engine Resources
- 11. A common scenario (e.g. grid workflow) Abstract workflow Information system Workflow engine Resources
- 12. A common scenario (e.g. grid workflow) Abstract workflow Information system Workflow engine Resources
- 13. A common scenario (e.g. grid workflow) Abstract workflow Information system Workflow engine Concrete workflow Resources
- 14. A common scenario (e.g. grid workflow) Abstract workflow Information system Workflow engine Concrete workflow Resources
- 15. Problem analysis (current approaches) The abstract workflow is static (Typically) no advanced control structures A priori mapping/ partly a priori mapping Mapping based on stored information A priori simulation/ a priori test/ a priori optimisation Centralised engine Human interaction Limited autonomy, limited ability for adaptation Overall lack of any high level model
- 16. Requirement analysis Workflow enactment in large-scale heterogeneous environments – should provide a higher level of autonomy – should be able to adapt to changing conditions – should be distributed – should be able to make decisions on partial and actual information – should support arbitrarily complex control structures Often a complex problem can be solved in a more simple way using nature inspired models
- 17. Learning Package Overview Workflow, workflow management Problem and requirement analysis – enactment in large-scale heterogeneous environments A chemical metaphor for workflow enactment A coordination model for workflow enactment formalized in the -calculus Further challenges
- 18. This talk is about a chemical metaphor but… It is just an example – Chemical reactions are reasonably similar to workflow enactment – It has a well established formalism Generally: nature inspiration for solving complex problems – Simple, primitive parts behave “intelligently” as a whole – Ants, termites, cells, molecules, etc.
- 19. A chemical metaphor Reactions are – autonomous – distributed – concurrent – depending on local conditions – depending on actual conditions – not following any a priori pattern – evolving in time
- 20. A chemical metaphor Reactions are Workflow enactment should autonomous provide a higher level of distributed autonomy concurrent be able to adapt to changing conditions depending on local conditions be distributed depending on actual be able to make decisions on conditions partial and actual information not following any a priori support arbitrarily complex pattern control structures evolving in time
- 21. A vision of chemical enactment Resources
- 22. A vision of chemical enactment Resources Activities
- 23. A vision of chemical enactment Resources Activities Control
- 24. A vision of chemical enactment React
- 25. Learning Package Overview Workflow, workflow management Problem and requirement analysis – enactment in large-scale heterogeneous environments A chemical metaphor for workflow enactment A coordination model for workflow enactment formalized in the -calculus Further challenges
- 26. From a vision to a model Materialize information: resource quantums Define an abstract chemical coordination model – independent from actual technical realizations – provide a high-level abstract framework for refinement – formalism: -calculus Advance, refine: find chemical examples for – Complex control – Fault tolerance – Resource management – Optimization – etc
- 27. Resource quantums The usual solutions P4 P6 P1P2 P5 P3 • stored information may be time sensitive
- 28. Resource quantums The usual solutions P4 P2 P1 P6 P3 P5
- 29. Resource quantums (« materialization of information ») resources are represented as tickets P4 P6 P1P2 P5 P3 • tickets represent a guaranteed service
- 30. Resource quantums (« materialization of information ») P2 P6 P5 P4 P1 P3
- 31. The -calculus a declarative, functional formalism inherently concurrent model of computation basic data structure: multiset (chemical solution) – passive molecules: booleans, integers, tuples, naming molecules – active molecules: -abstraction reaction: active molecules capture other molecules – x.M, N → M[x:=N] execution: perform reactions until a stable (inert) chemical solution is resulted
- 32. The -calculus: -abstraction Active molecules: -abstraction: PC.M – P is a pattern that selects elements for the reaction – C is a condition; the reaction takes place if C is true – M is the action Example: i:x, j:y i≤j, x>y. (j+1):x, j:y Semantics: capture i:x and j:y and replace them by (j+1):x, j:y if i≤j, x>y -abstraction is one-shot Universal matching symbol:
- 33. The -calculus -terms are – Commutative: M1,M2≡M2,M1 – Associative: (M1,M2),M3≡M1,(M2,M3) – Realize Brownian motion Reactions – Locality: if M1→M2, then M,M1→M,M2 – Solution: if M1→M2, then <M1>→<M2> Conditional reactions – xC.M Atomic capture – x1,x2,…xn.M
- 34. Resources a resource quantum is modeled as a sub-solution – recall: chemical solutions can be nested elements in the solution are inert attribute:value pairs there are mandatory attributes otherwise, the format is felxible <id:R1, type:comp, proc:16, OS:Linux, …> <id:N1, type:net, bandwidth:23, …> <id:R3, type:comp, proc:1, installed:equsolver, network:N1 …>
- 35. Elementary activities the chemical model does not execute an activity, just enacts it – execution is external exit from the chemical world: – execute activity on resource using parameter – a symbolic notation that can be realised in many ways return to the chemical world – the execution produces some result or error put back in form of a solution (control molecule) – <ActivityID:result> – <ActivityID:result, error:errorcode, …> – <ActivityID:result, executed:R1, …> execute A on R→ <A:result…>,<id:R,…>
- 36. Resource dependency activity A needs a resource <id:r, type:comp, proc:1, >. execute A on r < id:R2, type:comp, proc:1,…> < id:R1, type:comp, proc:16, OS:Linux, …> < id:R3, type:comp, proc:1, OS:SunOS, …> <id:r, type:comp, proc:1, >. execute A on r < id:R4, type:comp, < id:R5, type:comp, proc:1, memory:23, …> proc:1, disk:12, …>
- 37. Resource dependency <id:r, type:comp, proc:1, >. execute A on r Captured a matching resource < id:R2, type:comp, proc:1,…> < id:R1, type:comp, proc:16, OS:Linux, …> execute A on R3 < id:R4, type:comp, < id:R5, type:comp, proc:1, memory:23, …> proc:1, disk:12, …>
- 38. Resource dependency Both the resource and the activity are replaced by a control molecule < id:R2, type:comp, proc:1,…> < id:R1, type:comp, proc:16, OS:Linux, …> < id:R3, type:comp, proc:1, OS:SunOS, …> <A:12, error:0, executed:R3> < id:R4, type:comp, < id:R5, type:comp, proc:1, memory:23, …> proc:1, disk:12, …>
- 39. Data and control dependency activity A waits a result from activity B <B:x, >. execute A using x <C:0, error:5> <B:18> <B:x, >. execute A using x <D:42> <H:apple>
- 40. Data and control dependency <B:x, >. execute A using x Captured a corresponding control molecule <C:0, error:5> execute A using 18 <D:42> <H:apple>
- 41. Data and control dependency Replaced by the result <C:0, error:5> <A:2, error:0,...> <D:42> <H:apple>
- 42. Complex dependencies An activity needs both input from another activity and resources – <id:r, 1>, <B:x, 2>. execute A on r using x Dependencies can be arbitrarily combined – find a resource r1 for activity A and a resource r2 for B so that r1 and r2 have the same operating system – <id:r1, OS:x, 1>, <id:r2, OS:x, 2> . execute A on r1, execute B on r2 Conditions can be added as well – <id:r, disk:x, memory:y, > x>30, y>12. execute A on r
- 43. Workflow patterns Sequence Conditional Split Synchronizing merge P-split – p-out-of-n activities follow A P-merge – p-out-of-n results trigger activity A Loop
- 44. Learning Package Overview Workflow, workflow management Problem and requirement analysis – enactment in large-scale heterogeneous environments A chemical metaphor for workflow enactment A coordination model for workflow enactment formalized in the -calculus Further challenges
- 45. Challenges What we have so far is a framework – Principles of a chemical coordination model – It is just the beginning! Let’s explore and advance – Solve further aspects of workflow enactment: fault tolerance, optimization, resource control, complex workflow structures, constraints, co-allocation, compensation, etc. – Find further nature analogon within the chemical model: temperature, weight, magnetic properties, size, gravity, catalysts, membranes, etc.
- 46. Challenges (examples) Resource control by chemical agents – withdraw, modify, block, accept, reject, group, etc. Assert a neutralization agent: <id:r1, >. Replace a molecule: <id:r1, proc:1, OS:x, >.<id:r1, proc:1, OS:Linux, > Combine two molecules: (<id:r1, proc:1, >, <id:r1, proc:1, >). <id:r1, proc:2, >
- 47. Challenges (examples) Fault-tolerance by chemical agents – error molecules, retry, rollback, redundancy, compensation, etc. Assert an error handling molecule that reactivates in case of error (<Ai:x, >, <Aj:y, >, <id:r, R, >). (execute A on r using x y, (<A:errork, >,<id:r’, R, >).execute A on r’ using x y))
- 48. Challenges (examples) Complex resource allocation and co-llocation scenarios by molecules – e.g. reserve r1 and r2 so that there is a network link between them – (<id:r1, proc:1, network:n, >, <id:r2, proc:4, network:n, >, <id:n, type:net, >). (execute A on r1, (<A:x, >.execute B on r2 using x))
- 49. Conclusion The chemical metaphor: autonomous, adapting, distributed, actual The nature inspired coordination model – activities, resources and control are modeled using the same formalism – -calculus is not just description but defines execution semantics as well – workflow structure, resources and control can be added/withdrawn/modified in a fully dynamic and adaptive way
- 50. Connections to other teaching units Foundations – The Chemical Computing model and HOCL Programming Applications – Dynamic Adaptation with the Chemical Model © S-Cube – 50/<Max>
- 51. References Zsolt Németh, Christian Pérez, Thierry Priol: Distributed workflow coordination: molecules and reactions. IPDPS 2006 Zsolt Németh, Christian Pérez, Thierry Priol: Workflow Enactment Based on a Chemical Metaphor. SEFM 2005: 127-136 Manuel Caeiro, Zsolt Németh, Thierry Priol: A Chemical Model for Dynamic Workflow Coordination. PDP 2011: 215-222
- 52. Acknowledgements The research leading to these results has received funding from the European Community’s Seventh Framework Programme [FP7/2007-2013] under grant agreement 215483 (S-Cube).