adaptation in distributed collective systems

Distributed Adap.ve Systems (DAS) Unit

Adaptation in Distributed Collective Systems

Annapaola Marconi and Antonio Bucchiarone

Fondazione Bruno Kessler, Trento – Italy [email protected], [email protected]

12 March 2015, Milan

hGp://das.Ck.eu

@DAS_Unit

March, 12 2015 Adaptation in Distributed Collective Systems 2

Outline

§  Introduction

§  A Framework for Distributed Collective Systems o  Application Model

o  Adaptation mechanisms and strategies

o  Incrementality, reduction, reuse

o  Adaptation as AI planning problem

o  Collective adaptation

§  Demonstrator and Evaluation

§  Conclusions


§  Consist of diverse distributed heterogeneous entities o  e.g., IT systems, devices, sensors, robots, people

§  Entities are autonomous but need to collaborate to accomplish collective tasks o  each entity has its own objectives and requirements o  expose functionalities to the outside world to enable collaboration

§  Environment in which they operate is highly dynamic: o  changes of the context in which entities live; o  entities dynamically change their behavior (including offered

functionalities) and join/leave the system; o  changes in system requirements and preferences.

Distributed Collective Systems

DCS need to be ADAPTABLE BY DESIGN DCS need to be ADAPTED ON-THE-FLY


Bremen Harbor §  4 km2, 120.000 storage places

§  2 Million vehicles per year §  18 vehicle brands §  12 technical treatment stations

Aim: Develop a Car Logistic System supporting the management and operation of the port: §  covering the whole car delivery process, from

delivery to disposition; §  supporting the co-operation of the numerous

actors (i.e., cars, ships, trucks, storages, treatment areas, etc.) respecting their own procedures and business policies.

Car Logistic Scenario: Bremen Harbor


Bremen Harbor §  4 km2, 120.000 storage places

§  6 Million vehicles per year §  18 vehicle brands §  12 technical treatment stations

Aim: Develop a Car Logistic System supporting the management and operation of the port: §  covering the whole car delivery process, from

delivery to disposition; §  supporting the co-operation of the numerous

actors (i.e., cars, ships, trucks, treatment areas, etc.) respecting their own procedures and business policies.

Car Logistic Scenario: Bremen Harbor

Challenges: •  Customizable procedures for each car (e.g., brand,

model, order) •  Heterogeneity of actors/facilities involved (e.g., terminals,

storage areas, treatment stations, consignment areas, ships, trucks)

•  System Dynamicity (e.g., actors/facilities join/leave the system, changes in procedures of system facilities, changes in regulations and norms)

•  Context Dynamicity (e.g., unavailability or malfunctioning of the different port facilities, accidental damages of cars and trucks, human errors)

Fully exploit the benefits of the service-oriented paradigm and advance AI planning techniques to develop a context-aware distributed adaptive system.


A Framework for Adaptive Context-aware SBS

En.ty

Provided Fragments

Business Process

En.ty

Provided Fragments

Business Process

En.ty

Provided Fragments

Business Process

En.ty

Provided Fragments

Business Process

Business processes: •  Partial process specifications that allow

dynamic refinement and adaptation according to available system functionalities

•  Modeled via Adaptive Pervasive Flow Language (APFL) an extension of traditional workflow language (BPEL) with abstract activities + preconditions/effects

Process Fragments: •  Offered functionalities that can be

dynamically discovered/used by other entities

•  Modeled as business processes Context Model: •  Important characteristics of the

environment and of the entities that operate in it

•  Used to define context preconditions/effects on process activities and goals on abstract activities


Context Model

§  Shared representation of the system operation environment used to express goals, preconditions and effects on fragments

§  Defined through a set of context properties, each capturing a particular aspect of the system and its evolution (modeled as state transition systems) o  Context properties evolve as an effect of fragment activity execution (normal

behavior) or as a result of exogenous changes (improbable behavior)

§  Context configuration: snapshot of the context at a specific time


Business Processes and Fragments

§  Adaptable Pervasive Flow Language (APFL) o  Extension of traditional workflow language o  Standard constructs: sync/async WS communication, data manipulation,

complex control flow constructs o  + Preconditions on activities

•  Constrain the activity execution to specific context configurations •  Used to catch violations and trigger adaptation

o  + Effects on activities •  Model the expected impact of activity execution on the context •  Used to perform automatic reasoning on activities execution

o  + Compensation goals on activities •  Model the context configuration that has to be reached in case a (successfully

executed) activity needs to be compensated •  Used to (partially/completely) roll-back a process instance

o  + Abstract activities annotated with goals •  Defined at design-time in terms of the goal (context configurations) it needs to achieve •  Refined at run-time into an executable process wrt the available fragments, the current

context configuration and the goal to be achieved



Land Request

Check Gate Avail +

Gate1 Booked

Prepare Gate1

Gate2 Booked

Prepare Gate2

Departure Request Departure Departure

Reply

Business Process

Process Fragments

LANDING MANAGER

Land Request *

Gate1 Booked

Gate2 Booked

Departure Request

Departure Reply

P: ship_loc = harbor & ship_status = registered

P

P, E

P: gate1_status = ready E: gate1_status = assigned

SHIP

Land

Leave

GATE1

Prepare Land S

Prepare Land L

Land Guidance

Prepare Dep S

Precondition/Effects used for -  detecting run-time violations -  automatic reasoning for adaptation

Landing Departure



Land Request

Check Gate Avail +

Gate1 Booked

Prepare Gate1

Gate2 Booked

Prepare Gate2

Departure Request Departure Departure

Reply

Business Process

Provided Services

LANDING MANAGER

GATE1

Prepare Land S

Prepare Land L

Land Guidance

Prepare Dep S

Land Request *

Gate1 Booked

Gate2 Booked

Departure Request

Departure Reply

SHIP

Land

Leave

P

P, E

G

Goals used for -  run-time context-aware service

composition

G: gate1_status = ready

Precondition/Effects used for -  detecting run-time violations -  automatic reasoning for adaptation


Adaptation Mechanisms and Strategies

§  Adaptation needs o  Need for refining an abstract activity within a process instance o  Violation of a precondition of an activity that is going to be executed

§  Adaptation mechanisms o  Refinement: dynamic refinement of abstract activity by context-aware

composition of available fragments o  Local adaptation: identify a fragment composition that allows to re-start a

faulted process from a specific activity o  Compensation: dynamically compute a compensation process for a specific

activity


Adaptation Mechanisms and Strategies G3: CarProgressStoring = yes


Adaptation Mechanisms and Strategies G3: CarProgressStoring = yes

P1: CarStatus = ok and CarRegistra.on = no

A1: CarStatus = nok


Adaptation Mechanisms and Strategies

§  Adaptation needs o  Need for refining an abstract activity within a process instance o  Violation of a precondition of an activity that is going to be executed

§  Adaptation mechanisms o  Refinement: dynamic refinement of abstract activity by context-aware

composition of available fragments o  Local adaptation: identify a fragment composition that allows to re-start a

faulted process from a specific activity o  Compensation: dynamically compute a compensation process for a specific

activity

§  Adaptation strategies o  Combine adaptation mechanisms to solve complex adaptation problems

•  E.g., Re-refinement, Backward adaptation

o  Search for alternative solutions •  E.g., Local on current activity -> Backward on current refinement -> Re-refinement -> …

o  One-shot vs incremental adaptation


§  Is it feasible to resolve at run time a large amount of complex adaptation problems? o  No, in general it is not.

§  Solution: o  interleaving of adaptation and execution o  reduction of the complexity of adaptation problems o  reuse of already solved adaptation solutions.

Incrementality, reduction, reuse


ADAPTATION SOLUTION Process Fragment

ADAPTATION SOLUTION Process Fragment

PROBLEM REDUCTION

Relevant Process

Fragments

Relevant Context

ProperLes

ADAPTATION as AI PLANNING

ASTRO CAptEvo

Context-‐aware fragment composiLon

ADAPTED PROCESS Process

ADAPTATION NEED System ConfiguraLon,

Adapt. Trigger EnLty Instance

EXECUTION

Running System ConfiguraLon

? Process

Context

EnLty Instance

REDUCED ADAPTATION PROBLEM (Reduced) System Config.,

Goal, EnLty instance

INCREMENTAL ADAPTATION

m1 m2 m3 ; m1 m4 ; m3

AdaptaLon Strategy Adapted Process

SOLUTION REUSE

AdaptaLon knowledge base …

…

Problem SoluLon

<S3 , G3 , δ3 >

<S2 , G2 , δ2 >

<S1 , G1 , δ1 >

REDUCED ADAPTATION PROBLEM (Reduced) System Config.,

Goal, EnLty instance

ADAPTATION PROBLEM System ConfiguraLon, Goal, EnLty instance

Incrementality, reduction, reuse

§  INCREMENTALITY: incrementally addressing complex adaptation problems by interleaving problem solving and solution execution

§  REDUCTION: reduce complexity of each adaptation problem minimizing the search space according to the specific execution context

§  REUSE: reuse adaptation solutions by learning from past executions


Adaptation as AI Planning Problem

Adaptation Goal

PLAN

NER

Adaptation Process

G

Σ ||

Madapt

STS2AP

FL

Σ C

Planning domain

Synthesized plan

Process Fragments

. . .

P1

Pn APFL2STS Σ P1

Σ Pn

. . .

State Transition Systems

GOAL

BUILDE

R

Planning Goal

G Σ

Context Configuration

. . .

C1

Cm CM2STS Σ C1

Σ Cm

. . . Madapt composition of fragments that, if executed from the current configuration

and in the absence of exogenous events, ensures that the resulting context configuration satisfies G.



PLAN

NER

Adaptation Process Σ ||

Madapt

STS2AP

FL

Σ C

Planning domain

Synthesized plan

APFL2STS, CM2STS Transformation of fragments and context configuration in STSs and removal of improbable events

GOAL BUILDER Translation of the adaptation goal in EAGLE planning goal

Adaptation Goal

G

GOAL

BUILDE

R

Planning Goal

G Σ

Process Fragments

. . .

P1

Pn APFL2STS Σ P1

Σ Pn

. . .

State Transition Systems


. . .

C1

Cm CM2STS Σ C1

Σ Cm

. . .



PLAN

NER

Adaptation Process

Madapt

STS2AP

FL

Σ C Synthesized

plan

Product of fragment and context STSs synchronized on preconditions and effects

Adaptation Goal

G

GOAL

BUILDE

R

Planning Goal

G Σ

Process Fragments

. . .

P1

Pn APFL2STS Σ P1

Σ Pn State Transition

Systems


. . .

C1

Cm CM2STS Σ

Σ

. . .

C1

Cm

. . . Σ ||

Planning domain

Σ ||



Adaptation Process

Madapt

STS2AP

FL

PLANNER sophisticated AI planning techniques for WS composition developed within the ASTRO project (non-determinism, extended goals) (2002 – Today)

Adaptation Goal

G

GOAL

BUILDE

R

Planning Goal

G Σ

Process Fragments

. . .

P1

Pn APFL2STS Σ P1


Systems


. . .

C1

Cm CM2STS Σ

Σ

. . .

C1

Cm

. . . PL

AN

NER

Σ C

Planning domain

Σ ||

Synthesized plan



STS2APFL Translation of the synthesized plan into an APFL executable adaptation process

Adaptation Goal

G

GOAL

BUILDE

R

Planning Goal

G Σ

Process Fragments

. . .

P1

Pn APFL2STS Σ P1


Systems


. . .

C1

Cm CM2STS Σ

Σ

. . .

C1

Cm

. . . PL

AN

NER

Σ C

Planning domain

Σ ||

Synthesized plan

Adaptation Process

Madapt

STS2AP

FL


Implementation and Evaluation

§  We implemented the framework and evaluated on the CLS scenario

§  Demonstrator ASTRO-CAptEvo: o  simulate the CLS scenario, view the

adaptation/composition mechanisms in action, inspect the internal system operation

Demo: hGp://www.astroproject.org/downloads/captevoDemo

Video: hGp://www.astroproject.org/downloads/captevoVideo.zip

IEEE ICWS/Services 2012: Best Paper Award & ServicesCup winner


§  MODELING EFFORT AND COMPLEXITY §  29 entity types and process models, 69 fragment models, 40 context properties §  Refinement:

§  Modularization and re-use of different specification elements (Context Properties and Fragments)

§  Each fragment can be adjusted by its provider separately from other fragments of the application

§  Entities/fragments can join/leave the system without affecting the implementation

§  Adaptation: §  N. different adaptation cases to define considering only car damages and

stores unavailability (>170) §  Adaptation processes correct by construction




96% of problems solved in less than 30 seconds

96% of problems solved in less than 1 seconds

REDUCTION

REDUCTION + REUSE



New adaptaLon problems versus total number of adaptaLon needs (8 simulaLons)


§  So far we have considered SELFISH ADAPTATION: “an entity adapts considering only its own objectives”

§  BUT o  an adaptation might be “good” for one entity but not

for other entities in the same system o  an adaptation in an entity might trigger other

adaptations in different parts of the system (chains of adaptations)

Selfish vs Collective adaptation


Collective Adaptive Systems - Concepts

EnLty §  Represents a physical or virtual

organiza.onal unit. §  Comprises of a set of goals [1] and cells

[2]. §  Associated with preferences, u.lity

func.ons and context. Cell §  Described as a process consis.ng of

abstract and concrete ac.vi.es. §  Specialized by replacing abstract

ac.vi.es with abstract or concrete ac.vi.es.

Cell A B C

Goal 1

Cell

D B G

Goal 2 En.ty

I J H

E

F

OR

Specializa.on of abstract ac.vity G

[1] Antonio Bucchiarone, Claudio Antares Mezzina, Heorhi Raik. A Goal Model for Collec>ve Adap>ve Systems. In FoCAS@SASO 2014, London, UK. [2] V. Andrikopoulos, A. Bucchiarone, S. Saez, D. Karastoyanova C. Mezzina: Towards Modeling and Execu>on of Collec>ve Adap>ve Systems. ICSOC Workshops 2013: 69-‐81


Collective Adaptive Systems - Concepts

Ensemble §  Associated with a goal. §  Composi.on of specialized cells

w.r.t. goal and context. §  Cells collaborate to achieve

ensemble goal. Cell

D E

Cell

D B

C

Cell

E F G

Ensemble

Goal

“Emergent aggrega>on of autonomous and self-‐adap>ve process-‐based elements”


From Selfishness to Collective Goodness

§  Each CAS par.cipant cannot adapt its behavior in complete isola.on

§  Each adapta.on may nega.vely affect the goals of other par.cipants (i.e., conflicLng goals)

§  Each adapta.on can compromise the whole collabora.on

1.   Every adapta>on must be resolved at the scale of the whole Ensemble.

2.   Adapta>ons may need to be simultaneously applied to the behavior of mul>ple par>cipants

3.   Adapta>ons must respect the specific condi>ons that par>cipa>on to the Ensemble imposes.


Mul.-‐Modal Urban Mobility

Passenger

Des.na.ons Reach des.na.on

City council Minimize CO2/ Avoid traffic jams

FlexiBus FlexiBus Company

Manage buses & routes Follow assigned route

Taxi Transport passenger(s)

to des.na.on Car


FlexiBus


FlexiBus

ACCEPT DECLINE

Splite Fare with John and Mary?


FlexiBus

ACCEPT DECLINE

REPLAN YOUR ROUTE?


FlexiBus

Which part of the an Ensemble should be engaged to solve an adaptaLon need?

When an adaptaLon need will trigger collecLve AdaptaLon?

How we guarantee that an adaptaLon does not compromise the whole collaboraLon?

Distributed AdaptaLon approach for CAS


Issue Type and Solver

FlexiBus

,


Issue Resolution init

depend_found

cancelled

find solu.on

sol_found sol_not_found

returned

commiGed

no_dependency

targeted

sent

received

report 0 fitness

report consolidated fitness

communica.on

FlexiBus

An issue resolu.on is triggered when: •  a problem is detected internally to a role; •  a solver of a role received a request;

RM


Issue Communication

init

sent

received

cancelled

commiGed

send issue to the target

receive reply from the target: all targets are received

commit solu.on to the target

cancel solu.on for this target

RM

PassDelay4

Bus

PassDelay

delay1

com1

FlexiBus


Distributed Adaptation Tree

AND ………..

… … …

role instance boundaries

role instance boundaries

OR Internal Resolu.on

Consolida.on of all values from all communica.ons and calcula.on of the

overall resolu.on value.

RM FlexiBus

Decision among values received by two resolu.ons (Cell U.lity (i.e., Passenger))

Values: how well the corresponding target can

resolve the issue


Multiple Ensembles Involvement

res1

res2 res3

com1

com2 com3 com4

res4 res5 res6 res7 res8

OR

AND

OR OR

Internal ResoluLon

Traffic Jam

Route1 Ensemble

Route2 Ensemble

Route3 Ensemble

FlexiBus

RM 1

RM 2 RM 3


§  A framework for adaptation of distributed collective systems: o  Dynamic

•  Entities can leave/join the system at run-time •  Fragment/process models can change at run-time

o  Adaptive •  Allows for partial specification of processes (abstract activities) and to leave the

handling of improbable/extraordinary situations to run-time (activity preconditions) •  Provides different adaptation mechanisms properly coordinated through strategies

o  Context-aware •  Shared context model describing the system operational environment •  Can be used to model exogenous events which may affect the system operation

o  Collective (on-going) •  Multiple entities must adapt simultaneously to properly addresses a critical runtime

condition and to preserve their collaboration and benefits. •  Decentralized adaptation with distributed

o  User-centric (on-going) •  Smart Mobility scenario within the STREETLIFE project

Conclusions


Conclusions

CAR LOGISTIC INTEGRATED MOBILITY

CHILDREN MOBILITY DRONES FLEET


A. Bucchiarone, C. Mezzina, M. Pistore, H. Raik, G. ValeGo: CollecLve AdaptaLon in Process-‐Based Systems. SASO 2014: 151-‐156. A. Bucchiarone, C. Antares Mezzina, H. Raik. A Goal Model for CollecLve AdapLve Systems. In FoCAS@SASO 2014, London, UK.

V. Andrikopoulos, A. Bucchiarone, S. Saez, D. Karastoyanova C. Mezzina: Towards Modeling and ExecuLon of CollecLve AdapLve Systems. ICSOC Workshops 2013: 69-‐81 A. Bucchiarone, A. Marconi, C. Antares Mezzina, M. Pistore, H. Raik. On-‐the-‐Fly AdaptaLon of Dynamic Service-‐Based Systems: Incrementality, ReducLon and Reuse. ICSOC 2013, Berlin, Germany, December 2-‐5, 2013:146-‐161. H. Raik, A. Bucchiarone, N. Khurshid, A. Marconi, and M. Pistore. Astro-‐captevo: Dynamic context-‐aware adapta>on for service-‐based systems. In SERVICES, pages 385–392, 2012. A. Bucchiarone, A. Marconi, M. Pistore, and H. Raik. Dynamic adapta>on of fragment-‐based and context-‐aware business processes. In ICWS, pages 33–41, 2012. Antonio Bucchiarone, Alberto Lluch-‐Lafuente, Annapaola Marconi, Marco Pistore: A Formalisa>on of Adaptable Pervasive Flows. WS-‐FM 2009: 61-‐75 Marco Pistore, Paolo Traverso: Planning as Model Checking for Extended Goals in Non-‐determinis>c Domains. IJCAI 2001: 479-‐486

References


PhD Symposium @ SASO 2015

hgp://www.saso-‐conference.org/


hgp://www.agentgroup.unimore.it/DAS2015/

Distributed Adap.ve Systems (DAS) Unit

Adaptation in Distributed Collective Systems

Annapaola Marconi and Antonio Bucchiarone

Fondazione Bruno Kessler, Trento – Italy [email protected], [email protected]

12 March 2015, Milan

hGp://das.Ck.eu

@DAS_Unit

adaptation in distributed collective systems

Software

system o changes

o entities

collective tasks o

collective systems dcs

reuse o adaptation

car logistic system

systems das unit adaptation

system requirements