Reconciling self-adaptation and self-organization

Reconciling Self-adaptation and Self-organization

Franco Zambonelli Università di Modena e Reggio Emilia

[email protected]

Non-adaptive System

Adaptive with Human-in-the-Loop

Self-adaptive

Self-organizing

Reconciling Self-A and Self-O…

Outline

•  Self-‐adap)ve systems – Concepts & Experiences

•  Self-‐organizing systems – Concepts & Experiences

•  Reconcilia)on – Roundabouts and more… – Future urban scenarios

•  Conclusions

Self-adaptive Systems: Concepts

•  Key research issues –  From my own very personal and

necessarily limited perspec)ve

•  Engineering the structure of feedback loops –  Individual loops –  Nested loops –  Interac)ng loops

•  And the components within –  E.g., for effectors –  Parameters upda)ng –  Behavioral upda)ng (COP & C) –  Structural upda)ng

The ASCENS Project

•  ASCENS “Autonomic Service Component Ensembles” –  EU FP7 FET IP –  Starting October 1st 2010, lasting

4 years

•  Key Challenges – How to develop complex and

large scale autonomic software systems?

– Models + Engineering Tools –  Experience with swarm robotics

and car sharing of e-vehicles

The ASCENS SOTA Model (IEEE ECBS 2012, IEEE WETICE 2012)

•  Kind of conceptual framework •  SOTA :: “State of the Affairs”

–  A Mul)dimensional space –  Everything in the world in which the system lives and executes,

that may affect its behaviour

•  Adap)ve systems can (should?) be expressed in terms of “goals” = “states of the affairs” to be achieved –  Without making assump)on on the actual design –  It is a requirements engineering ac)vity

SOTA: Components of the Model

•  “State of the Affairs” :: S (t) at )me t, of a specific en)ty e is a tuple of n si values, each represen)ng a specific aspect of the current situa)on

•  Dynamics :: evolu)on of Se as a movement in a virtual n-‐dimensional space Se:

•  Transi5ons :: θ(t, t + 1) expresses a movement of e in S à endogenous or exogenous

•  Goal :: achievement of a given state of the affairs, represented as a confined area in space

•  U5lity :: (constraints on the trajectory to follow in the phase space Se) expressed as a subspace in Se:

Using SOTA in Analysis •  Model checking func)onal

and non func)onal requirements –  Opera5onaliza5on of SOTA

goals and U)li)es –  Transforma5on into

asynchronous FLTL –  Verifica5on with LTSA tool

•  Elici)ng “awareness” requirements – Iden5fica5on: which knowledge (dimensions of SOTA space) with

components – Virtualiza5on: which (virtual) sensors available with components – Metrifica5on: which granularity/accuracy is needed for sensors

Using SOTA in Design •  Express adapta)on pa]erns

in terms of G and U –  G and U express the

adapta)on needs –  And can thus drive the

iden)fica)on of With which so_ware architecture structure of feedback loops)

•  At the level of both individual and ensembles –  Two levels are strictly inter-‐twined –  And possibly self-‐expressing the structure of feedback loops

•  Pa]ern-‐based approach –  What general structures for feedback loops? –  Macro taxonomy of pa]erns

SOTA Patterns

G = ∅,

U = U1,U2,... Un

G = G1,G2,...,Gm, U = U1,U2,...,Un

GASC = GCSC ∩ GACM UASC = UCSC ∩ UACM

Self-org vs Self-adaptive Patterns •  There are cases in which top-

down self-adaptive patterns are more effective –  A small group of robots/vehicles

with a leader perceiving and directing/negotiating the the group

–  “Loci” of feedback control

•  There are cases in which bottom up self-org patterns are better –  A large group of robots/vehicles

works well with peer organization –  Self-organizing activities and

coordinated movements –  Distributed implicit control loops

Key Question #1

•  How to integrate bo]om-‐up self-‐organiza)on pa]erns into large-‐scale self-‐adap)ve systems? – What interface/API? – For what classes of self-‐org behavior?

– What mechanisms? – Can we define general rules/approaches?

The SAPERE Project •  SAPERE “Self-aware Pervasive

Service Ecosystems” –  EU FP7 FET –  Starting October 1st 2010, lasting

3 years

•  Key Challenges –  To define and implement a general

framework for self-organizing service ecosystems

– Models + Middleware –  Experience with pervasive urban

services and pervasive displays

The SAPERE Approach •  Nature-inspired (Biochemical) –  Simply metaphor for combining/aggregating services

in a spontaneous way – Whether human or

ICT ones •  Spatially-situated – To match the

nature of urban scenarios

–  Inherently adaptive –  Spontaneous

reconfiguration of activities and interactions

The SAPERE Architecture •  Humans & ICT Devices

–  Interact by injecting/consuming service/data components

•  Service Components –  Execute in a sort virtual “Spatial substrate”

–  Distributed reactive tuple space

–  Moving, acting, composing, as from eco-laws

•  Eco Laws –  Rule local activities and

interactions –  Apply based on local state –  Self-organization of collective

behavior

Using SAPERE •  Inject “live semantic

annotations” (LSAs) –  messages+service

descriptions •  Eco-laws apply to LSAs

–  Bonding (subsuming discovery and composition) between LSAs

–  Propagation (pheromones and fields) of LSAs

–  Decay (evaporation) •  Observe resulting LSA

–  Their content –  Their distributed structure

Example: Steering Mobility

•  Mobile entities ingject LSA expressing their presence –  Propagation of LSA

•  Observe other LSAs –  And if affected by their presence in chosing directions

Simulation of Steering Mobility

Integrating Self-organization and Self-Adaptation in SAPERE •  Some LSAs that bonds with each other but are insensitive to

fields and pheromones –  Autonomic manager can be easily integrated in the loop

•  Other LSAs as fields and pheromones –  For self-org patterns

•  All in the same environment/space and with same mechanisms

SAPERE Ecosystem of Displays

Key Question #2

•  How to control by design the behavior of self-‐organizing (sub)systems? – Predictable non-‐determinism

– Direct engineering of self-‐organizing behaviors

– E.g., in SAPERE, how can we sure that the macro behavior of steered will not diverge from what expected

The Roundabout Lesson: Engineering the environment •  The shape of the environment can affect the behavior of self-‐organizing components – Without undermining their autonomy

– Without losing the advantages of self-‐organiza)on

–  Yet promo)ng more predictability

•  And enabling top-‐down engineering – The shape you give is the behavior you get

Engineering the Environment in SAPERE

•  What does it means to “shape” the environment –  Shaping its perception by components –  Equivalent to the distort the way LSAs are perceived and propagate

•  Very easy to implement but… –  Still to be verified

its effectiveness and the ease of engineering top-down behaviors in this way

Engineering the Environment in SAPERE

•  What does it means to “shape” the environment –  Shaping its perception by components –  Equivalent to the distort the way LSAs are perceived and propagate

•  Very easy to implement but… –  Still to be verified

its effectiveness and the ease of engineering top-down behaviors in this way

Key Question #3

•  Are there different approaches to reconciliate? –  I have no answers…. – However…

The Jazz Perspective •  A few “engineered” rules

–  How and how not to interact –  Rythms and rules of interactions

•  Freedom of self-organization for anything else –  With who and when to interact –  According to which internal goals/attitudes –  Dynamic instantiation of feedback loops

•  Worth investigating? I have no idea but it is fascinating –  cfr “Ad-opera” approach

Key Question #4

•  Where will reconcilia)on approaches be firstly applied? –  In most large-‐scale so_ware systems

– And primarily in future urban socio-‐technical superorganisms

Smart Cities: From Senseable…

•  Sensing what’s happening – Via ICT devices – And social

networks

•  To better understand (via data analysis) – City and social

dynamics – At a global level

Sense

Understand (compute)

…To Actuable

•  We can “shape” other than understand – Actuating ICT

device – Steering human

actions

•  Closing loops that enables finalized urban behaviors possible

Sense


Act (Steer)

…To Actuable

•  We can “shape” other than understand – Actuating ICT

device – Steering human

actions

•  Closing the loop that enables finalized urban behaviors possible

Sense


Act (Steer)

Adaptation in Urban Superorganisms •  The ICT and Human/Social level

blurred to the point of invisibility –  Their capabilities well complement

each other à high value co-creation –  High-levels of collective “urban”

intelligence –  Necessarily situated and adaptive

•  Many levels of top-down and bottom-up adaptation –  Centralized control (municipalities) –  Bottom up control (citizen

proactiveness) –  Hybrid (crowdsourcing)

•  Will have to be orchestrated

Example: Mobility in Urban Superorganisms •  Mobility per se :: steer for car, bike, ride sharing •  City maintainance :: please go there and do that •  Exhibitions ::steer to avoid crowd or suggest paths •  All of these require

–  Sensing, computing (data interpretation) actuation (steering) –  Adaptive self-organized mobility strategies –  Top up engineering and control of behaviors

•  Exxacerbating all previous engineering challenges

Conclusions •  Need to reconcile self-org and self-adapt approaches •  Integration of localized self-org sub-systems ! •  Controlling self-organizing behaviors ! •  Jazz ?

•  Will be of fundamental importance in future urban socio-technical superorganisms •  Yet there are still a lot of engineering challenges •  There included social issues à humans are back in the loop!

Technology

Reconciling self-adaptation and self-organization