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. 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. Learning Package Overview
 Motivation
 Problem Description
 Goal
 Background
 Approach
 Prototype
 Conclusions
© Claudia Di Napoli, Maurizio Giordano

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. 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. Learning Package Overview
 Motivation
 Problem Description
 Goal
 Background
 Approach
 Prototype
 Conclusions
© Claudia Di Napoli, Maurizio Giordano

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. 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. 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. 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. Learning Package Overview
 Motivation
 Problem Description
 Goal
 Background
 Approach
 Prototype
 Conclusions
© Claudia Di Napoli, Maurizio Giordano

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. Learning Package Overview
 Motivation
 Problem Description
 Goal
 Background
 Approach
 Prototype
 Conclusions
© Claudia Di Napoli, Maurizio Giordano

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. 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. 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. 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. Learning Package Overview
 Motivation
 Problem Description
 Goal
 Background
 Approach
 Prototype
 Conclusions
© Claudia Di Napoli, Maurizio Giordano

19. Approach:
The overall picture
We propose to model the Services
Registries
WF service binding process
as a chemical process
where service offers
(passive molecules) coming
from the Internet may enter
the chemical container Services
with QoS
continuously; they react
according to chemical rules
User
(active molecules) allowing request
for the formation of Service
Mappings incrementally.
Chemical rules implement
QoS-driven selection criteria
that act as local constraint
solvers
chemical
reaction
Workflow
Execution
Instantiation Chemical
Container System
© Claudia Di Napoli, Maurizio Giordano

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. 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. 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. 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. 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 >  AND
replace PSM PSM by if cik  ...  chj > 
sm sq
sk
PSM
sj sj
© Claudia Di Napoli, Maurizio Giordano

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. 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. Learning Package Overview
 Motivation
 Problem Description
 Goal
 Background
 Approach
 Prototype
 Conclusions
© Claudia Di Napoli, Maurizio Giordano

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. 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. Learning Package Overview
 Motivation
 Problem Description
 Goal
 Background
 Approach
 Prototype
 Conclusions
© Claudia Di Napoli, Maurizio Giordano

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. 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. 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. 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. 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