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S-Cube Learning Package             Dynamic Adaptation:Dynamic Adaptation with the Chemical Model             CNR, SZTAKI,...
Learning Package Categorization                        S-Cube              Self-* Service Infrastructure             and S...
Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions            ...
Motivation Service Based Applications (SBAs) are realized composing  third-party services accessible through Internet and...
Motivation Both user requirements and service quality attributes may  change in time due to the dynamic nature of service...
Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions            ...
Problem Description Workflow: a set of activities to be executed  according to data/control-flow constraints             ...
Problem Description Workflow: a set of activities to be executed  according to data/control-flow constraints WF service ...
Problem Description Workflow: a set of activities to be executed  according to data/control-flow constraints WF service ...
Problem Description To find a Service Mapping (SM), if any, starting from a  Workflow, user requirements, and an initial ...
Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions            ...
Goal To model the workflow service binding process as an evolving  and autonomic system so that:   – the process is distr...
Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions            ...
Background:Nature Inspired Computing Nature Inspired Computing is emerging as a way to  reproduce such an autonomous (lif...
Background:Nature Inspired Computing Chemical Computing is a nature inspired (unconventional)  programming model where th...
Background:Chemical Computing Gamma-calculus is a declarative, functional formalism giving  the formal definition of the ...
Background:Chemical Computing: an example passive        multiset                     let max =molecules  (data)          ...
Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions            ...
Approach:The overall pictureWe propose to model the                                                     Services          ...
Approach:Problem chemical formalization Workflow of activities as a DAG                     <id:1, in:0, out:2> Graph no...
Approach:Problem chemical formalization Each WF graph node may have zero or more  associated offers represented by the  c...
Approach:Problem chemical formalization      A Partial Service Mapping (PSM) is defined as the       building block for t...
Approach:Chemical service selection The chainrule deals with sequences and it builds PSMs from  other (elementary) PSMs  ...
Approach:Chemical service selection The splitrule deals with split and merge nodes and it builds a  new PSM:   – two PSMs...
Approach:Chemical communication The rcvoffer rule receives service offers from the network  (via UDP protocol)   – Applie...
Approach:   Chemical middleware for service binding                                                      Services       Th...
Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions            ...
Prototype:The -Scheme interpreter To realize the chemical-based middleware for service binding, we  developed a HOCL int...
Prototype:The -Scheme interpreter An interaction session with the -Scheme interpreter:  The greatest prime number in a ...
Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions            ...
Conclusions Autonomous behaviour   – WF service binding is modeled as an autonomous and always     running chemical syste...
References This presentation is based on [1], [2], [3]. Related works on using a chemical approach for service-  based a...
References  – Di Napoli C., Giordano M., Pazat J-L., and Wang C.    A Chemical Based Middleware for Workflow Instantiation...
References  4. Viroli M., Casadei M.     Chemical-inspired selfcomposition of competing services,”     Proc. of the 2010 A...
Acknowledgements      The research leading to these results has      received funding from the European      Community’s S...
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S-CUBE LP: Dynamic Adaptation: Dynamic Adaptation with the Chemical Model

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Transcript of "S-CUBE LP: Dynamic Adaptation: Dynamic Adaptation with the Chemical Model"

  1. 1. S-Cube Learning Package Dynamic Adaptation:Dynamic Adaptation with the Chemical Model CNR, SZTAKI, INRIA Claudia Di Napoli, CNR Maurizio Giordano, CNR www.s-cube-network.eu
  2. 2. Learning Package Categorization S-Cube Self-* Service Infrastructure and Service Discovery Support Infrastructure Mechanisms for the Run-Time Adaptation of Services Dynamic Adaptation with the Chemical Model © Claudia Di Napoli, Maurizio Giordano
  3. 3. Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions © Claudia Di Napoli, Maurizio Giordano
  4. 4. Motivation Service Based Applications (SBAs) are realized composing third-party services accessible through Internet and as such they are managed and operated in a completely decentralized way Users will require SBAs with end-to-end quality requirements and third-party service providers will provide services with quality attributes Service Based Applications are very likely to be provided according to market-oriented approaches that regulate the demand and supply of services © Claudia Di Napoli, Maurizio Giordano
  5. 5. Motivation Both user requirements and service quality attributes may change in time due to the dynamic nature of service-based environments: – users may decide to change their requirements according to some marketing strategies, – service providers may decide to change the values of the quality attributes they provide services with according to market trends, – more service providers providing the same service but with different quality attributes may be available Therefore mechanisms to support this dynamicity (at the infrastructure level of the S-Cube Conceptual Research Framework) are necessary when selecting services composing an SBA upon a user request © Claudia Di Napoli, Maurizio Giordano
  6. 6. Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions © Claudia Di Napoli, Maurizio Giordano
  7. 7. Problem Description Workflow: a set of activities to be executed according to data/control-flow constraints 1 2 3 4 5 6 7 8 9 10 11 © Claudia Di Napoli, Maurizio Giordano
  8. 8. Problem Description Workflow: a set of activities to be executed according to data/control-flow constraints WF service binding: a mapping 1 between WF activities and service implementations with non-fun 2 3 values that fulfill some user requirements – More Service Mappings (SMs) may be found: 4 5 a service can be provided by different sources and with different non-fun values 6 7 8 9 Service offers 10 11 © Claudia Di Napoli, Maurizio Giordano
  9. 9. Problem Description Workflow: a set of activities to be executed according to data/control-flow constraints WF service binding: a mapping 1 between WF activities and service implementations with non-fun 2 3 values that fulfill some user requirements – More service mappings (SM) may be found: 4 5 a service can be provided by different sources and with different non-fun values 6 7 8 WF execution: a path from the start to the end node of a specific SM 9 10 11 © Claudia Di Napoli, Maurizio Giordano
  10. 10. Problem Description To find a Service Mapping (SM), if any, starting from a Workflow, user requirements, and an initial set of service offers 1 A service “offer” is a service specification composed of: 2 3 – an endpoint to a software service – a Quality Attribute, i.e. a value specifying a 4 5 generic non-fun parameter of the service (price, delivery time ...) related to the user requirements 6 7 8 Currently supported workflows include: 9 – sequences – split and merge (if-then-else, fork-join) 10 – no loops 11 © Claudia Di Napoli, Maurizio Giordano
  11. 11. Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions © Claudia Di Napoli, Maurizio Giordano
  12. 12. Goal To model the workflow service binding process as an evolving and autonomic system so that: – the process is distributed - service compositions are local and not serial – the process is incremental - SMs are built by aggregating smaller mappings – dynamicity is allowed - changes in service availability is taken into account when the service binding takes place – adaptability is allowed - new SMs may be found starting from previously computed partial results © Claudia Di Napoli, Maurizio Giordano
  13. 13. Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions © Claudia Di Napoli, Maurizio Giordano
  14. 14. Background:Nature Inspired Computing Nature Inspired Computing is emerging as a way to reproduce such an autonomous (lifelike-based) behaviour in solving computing problems Its characterizing features are: – autonomy, entities are individuals that act independently – distributed, entities with localized decision-making capabilities are distributed in a heterogeneous environment, and they interact to exchange information on each other or to affect the states of others – emergent, entities collectively exhibit a complex behaviour not present or predefined in the behaviour of each single entity; – adaptability, entities change behaviour in response to changes in the environment in which they operate; – self-configuration, local interactions among entities determine the evolution of the system toward desired states according to self- aggregation mechanisms © Claudia Di Napoli, Maurizio Giordano
  15. 15. Background:Nature Inspired Computing Chemical Computing is a nature inspired (unconventional) programming model where the computation is represented by abstract molecules reacting in an abstract chemical solution according to local interactions (chemical reactions) The result of a program is represented by the molecules present in the chemical solution when it reaches an inert state, i.e. when no element in the solution can trigger any reactions – Data are (passive) molecules – Operations are chemical reactions (active molecules) - reactions are unpredictable, concurrent, distributed, governed by local conditions and well-known general laws © Claudia Di Napoli, Maurizio Giordano
  16. 16. Background:Chemical Computing Gamma-calculus is a declarative, functional formalism giving the formal definition of the chemical paradigm introduced in 1986 by Banâtre & Le Métayer (INRIA) The Higher Order Chemical Language (HOCL) is an implementation of the Gamma-calculus extended with expressions, types, pairs, empty solutions and names. For further details consult learning package: The Chemical Computing Model and HOCL Programming © Claudia Di Napoli, Maurizio Giordano
  17. 17. Background:Chemical Computing: an example passive multiset let max =molecules (data) replace x,y 1 by x 4 if x>y 8 2 active 6 molecules 12 (operations) © Claudia Di Napoli, Maurizio Giordano
  18. 18. Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions © Claudia Di Napoli, Maurizio Giordano
  19. 19. Approach:The overall pictureWe propose to model the Services RegistriesWF service binding processas a chemical processwhere service offers(passive molecules) comingfrom the Internet may enterthe chemical container Services with QoScontinuously; they reactaccording to chemical rules User(active molecules) allowing requestfor the formation of ServiceMappings incrementally.Chemical rules implementQoS-driven selection criteriathat act as local constraintsolvers chemical reaction Workflow Execution Instantiation Chemical Container System © Claudia Di Napoli, Maurizio Giordano
  20. 20. Approach:Problem chemical formalization Workflow of activities as a DAG <id:1, in:0, out:2> Graph nodes and edges are chemical molecules: 1 <from:1, to:3> Nodei = <id:si,,in:ni,out:mi,... > 2 3 Edgel = <from:si,,to:sj,type:edgetype,...> <id:4, in:1, out:2> 4 5 – They are catalysts involved in reactions but remain intact afterwards 6 7 8 <from:7, to:10> – Graph nodes are ordered according to 9 the edge directions 10 <id:11, in:2, out:0> 11 © Claudia Di Napoli, Maurizio Giordano
  21. 21. Approach:Problem chemical formalization Each WF graph node may have zero or more associated offers represented by the chemical elementary molecules: 1 Offerik = <eik:si, qos: cik, rlev:pik> <e41:4, qos:7, rlvl:0.2> 2 3 <e42:4, qos:9 , rlvl:0.3 > – ei is an endpoint to a software service 4 5 with a service description (interface) si offered with a Quality Attribute ci and a 6 7 8 reactivity level pi 9 – They are removed from <e92:9, qos:32 , rlvl:0.1 > the system after they <e91:9, qos:40 , rlvl:0.2 > 10 react 11 © Claudia Di Napoli, Maurizio Giordano
  22. 22. Approach:Problem chemical formalization A Partial Service Mapping (PSM) is defined as the building block for the composition as: – either a single node with an 1 associated offer (elementary PSM), – or a mapping of a workflow connected 2 3 subgraph (fragment) with the following conditions*: 4 5 1. The first node has no edges outgoing to nodes outside the subgraph 2. The last node has no edges incoming 6 7 8 from nodes outside the subgraph 3. All other nodes of the subgraph have no 9 edges incoming from or outgoing to nodes outside the subgraph 10 (*) the PSM definition correspond s to the basic-block defintion in control flow analysis theory 11 © Claudia Di Napoli, Maurizio Giordano
  23. 23. Approach:Chemical service selection The chainrule deals with sequences and it builds PSMs from other (elementary) PSMs – the two PSMs are concatenated to form a new PSM containing all nodes of the component PSMs – the trigger part of the rule (the if part) implements local selection criteria to be defined according to the specific considered application and QoS types specified by the user selection strategy sl sl specification according PSM to inputs from JRA-2.2, 1.3, 1.2 si replace by PSM if pli  pjk >  AND cli  cjk >  sj PSM sk sk © Claudia Di Napoli, Maurizio Giordano
  24. 24. Approach:Chemical service selection The splitrule deals with split and merge nodes and it builds a new PSM: – two PSMs molecules are the branches of a split/merge, and the other two PSMs are molecules containing split/merge nodes as last/first nodes – the trigger part of the rule (the if part) implements local selection criteria to be defined according to the specific considered application and QoS types specified by the user si sl selection strategy PSM PSM specification according to inputs from sk JRA-2.2,1.3,1.2 sl sp pik  ...  phj >  ANDreplace PSM PSM by if cik  ...  chj >  sm sq sk PSM sj sj © Claudia Di Napoli, Maurizio Giordano
  25. 25. Approach:Chemical communication The rcvoffer rule receives service offers from the network (via UDP protocol) – Applies always  communication container is never inert – Evolution: as soon as new offers arrive the selection is triggered again and again UDP replace offer: by offer: if The sndwf rule sends the instantiation results (if any) to the caller software module (or through the network) – Applies only when results are available UDP replace result: by result: if WF enactment © Claudia Di Napoli, Maurizio Giordano
  26. 26. Approach: Chemical middleware for service binding Services The container multiset is a with QoS communication layer. It is an interface towards both service providers located on the network, and the sndwf workflow enactment module. The instantiator multiset rcvoffer receives a workflow splitrule description specifying both the functionality of each{“RESULT” } {“OFFER” } component of the required wfextract prerule SBA, and the dependence chairule constraints occurring among Instantiated the components, together Workflof with a user QoS Instantiator multiset requirements for the entire application. It selects a service for each workflow Container multiset activity according to the available offers and the user requirements. If an instantiated workflow is obtained, it is sent back to the main thread. © Claudia Di Napoli, Maurizio Giordano
  27. 27. Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions © Claudia Di Napoli, Maurizio Giordano
  28. 28. Prototype:The -Scheme interpreter To realize the chemical-based middleware for service binding, we developed a HOCL interpreter, named -Scheme, on top of (and integrated in) a Scheme programming environment (Racket) with: – Interpreter mode - fast chemical middleware prototyping and testing – Modularization - Chemical code embedded in scheme functions with local scope binding – Fast & on-fly compilation support - byte-code JIT generation during interpreter execution – Distribution and Concurrency - Multithread, multiprocessing. – Communication support - Standard network protocol support (UDP, HTTP; ….) – Powerful programming control mechanism - Continuation handling © Claudia Di Napoli, Maurizio Giordano
  29. 29. Prototype:The -Scheme interpreter An interaction session with the -Scheme interpreter: The greatest prime number in a multiset – For -Scheme documentation and tutorial visit: ...... (shortly available!) © Claudia Di Napoli, Maurizio Giordano
  30. 30. Learning Package Overview Motivation Problem Description Goal Background Approach Prototype Conclusions © Claudia Di Napoli, Maurizio Giordano
  31. 31. Conclusions Autonomous behaviour – WF service binding is modeled as an autonomous and always running chemical system decoupled from the execution stage Distributed approach – Service binding to service specifications (i.e. mapping service description to “offers”) is concurrent and may be distributed (no serial instantiation) Adaptation – the binding process computes new service compositions as soon as new service offers become available – active rules could be modified to express different constraints © Claudia Di Napoli, Maurizio Giordano
  32. 32. References This presentation is based on [1], [2], [3]. Related works on using a chemical approach for service- based applications can be found in [4], [5], [6], [7] Further readings on Gamma-calculus and HOCL can be found in the learning package The Chemical Computing Model and HOCL Programming For a better understanding of quality attribute models can be found in the learning package Quality of Service models for Service Oriented Architectures © Claudia Di Napoli, Maurizio Giordano
  33. 33. References – Di Napoli C., Giordano M., Pazat J-L., and Wang C. A Chemical Based Middleware for Workflow Instantiation and Execution LNCS 6481 (2010) “Towards a Service-Based Internet”, pp. 100- 111, Springer – Di Napoli C., Giordano M., Németh Z., and Tonellotto N. Adaptive instantiation of service workflows using a chemical approach Proc. of CoreGrid Workshop (in conjunction with EuroPar 2010) – Di Napoli C., Giordano M., Németh Z., and Tonellotto N. Using Chemical Reactions to Model Service Composition Proc. of 2nd Int. Workshop on Self-Organizing Architectures (in conjunction with ICAC 2010), pp. 43-50 © Claudia Di Napoli, Maurizio Giordano
  34. 34. References 4. Viroli M., Casadei M. Chemical-inspired selfcomposition of competing services,” Proc. of the 2010 ACM Symposium on Applied Computing. NY, USA, pp. 2029–2036 5. Viroli M., and Zambonelli F. A biochemical approach to adaptive service ecosystems Information Science, 2009 6. Nemeth Z., Perez C., Priol T. Workflow enactment based on a chemical metaphor Proceedings of the Third IEEE Int.Conf. on Software Engineering and Formal Methods, pp. 127–136 7. Caeiro M., Nemeth Z., and Priol T. A chemical model for dynamic workflow coordination Proc. of the 2011 19th International Euromicro Conference on Parallel, Distributed and Network-Based Processing, pp. 215–222 © Claudia Di Napoli, Maurizio Giordano
  35. 35. 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). © Claudia Di Napoli, Maurizio Giordano
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