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Reconciling           Franco ZambonelliSelf-adaptation and   Università di Modena e                      Reggio EmiliaSelf...
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...
Self-adaptive Systems: Concepts•  Key	  research	  issues	  	       –  From	  my	  own	  very	  personal	  and	          n...
The ASCENS Project•  ASCENS “Autonomic Service   Component Ensembles”  –  EU FP7 FET IP  –  Starting October 1st 2010, las...
The ASCENS SOTA Model(IEEE ECBS 2012, IEEE WETICE 2012)•  Kind	  of	  conceptual	  framework	  •  SOTA	  ::	  “State	  of	...
SOTA: Components of the Model•  “State	  of	  the	  Affairs”	  ::	  S	  (t)	  at	  )me	  t,	  of	  a	     specific	  en)ty	 ...
Using SOTAin Analysis•  Model	  checking	  func)onal	     and	  non	  func)onal	     requirements	      –  Opera5onaliza5o...
Using SOTA in Design•  Express	  adapta)on	  pa]erns	     in	  terms	  of	  G	  and	  U	      –  G	  and	  U	  express	  t...
SOTA Patterns                      	              G	  =	  ∅,	  	  	  	  	  	       U	  =	  U1,U2,...	  Un	                ...
Self-org vs Self-adaptive Patterns•  There are cases in which top-   down self-adaptive patterns are   more effective   – ...
Key Question #1•  How	  to	  integrate	     bo]om-­‐up	  self-­‐   organiza)on	  pa]erns	     into	  large-­‐scale	  self-...
The SAPERE Project•  SAPERE “Self-aware Pervasive   Service Ecosystems”  –  EU FP7 FET  –  Starting October 1st 2010, last...
The SAPERE Approach•  Nature-inspired (Biochemical)   –  Simply metaphor for combining/aggregating services      in a spon...
The SAPERE Architecture•  Humans & ICT Devices   –  Interact by injecting/      consuming service/data      components•  S...
Using SAPERE•  Inject “live semantic   annotations” (LSAs)   –  messages+service      descriptions•  Eco-laws apply to LSA...
Example: Steering Mobility•  Mobile entities ingject LSA expressing their presence   –  Propagation of LSA•  Observe other...
Simulation of Steering Mobility
Integrating Self-organizationand Self-Adaptation in SAPERE•  Some LSAs that bonds with each other but are insensitive to  ...
SAPERE Ecosystem of Displays
Key Question #2•  How	  to	  control	  by	  design	     the	  behavior	  of	  self-­‐   organizing	  (sub)systems?        ...
The Roundabout Lesson:Engineering the environment•  The	  shape	  of	  the	  environment	     can	  affect	  the	  behavior...
Engineering the Environmentin SAPERE•  What does it means to “shape” the environment   –  Shaping its perception by compon...
Engineering the Environmentin SAPERE•  What does it means to “shape” the environment   –  Shaping its perception by compon...
Key Question #3•  Are	  there	  different	     approaches	  to	     reconciliate?	     –  I	  have	  no	  answers….	     – ...
The Jazz Perspective•  A few “engineered” rules   –  How and how not to interact   –  Rythms and rules of interactions•  F...
Key Question #4•  Where	  will	     reconcilia)on	     approaches	  be	  firstly	     applied?	         –  In	  most	  larg...
Smart Cities: From Senseable…•  Sensing what’s   happening  –  Via ICT devices     Sense	    –  And social     networks•  ...
…To Actuable•  We can “shape”   other than   understand           Sense	                            Act                   ...
…To Actuable•  We can “shape”   other than   understand           Sense	                            Act                   ...
Adaptation in UrbanSuperorganisms•  The ICT and Human/Social level   blurred to the point of invisibility   –  Their capab...
Example:Mobility in Urban Superorganisms•    Mobility per se :: steer for car, bike, ride sharing•    City maintainance ::...
Conclusions•  Need to reconcile self-org and self-adapt approaches  •  Integration of localized self-org sub-systems !  • ...
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Reconciling self-adaptation and self-organization

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Franco Zambonelli's presentation at SEAMS 2012

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Transcript of "Reconciling self-adaptation and self-organization"

  1. 1. Reconciling Franco ZambonelliSelf-adaptation and Università di Modena e Reggio EmiliaSelf-organization franco.zambonelli@unimore.it
  2. 2. Non-adaptive System
  3. 3. Adaptive with Human-in-the-Loop
  4. 4. Self-adaptive
  5. 5. Self-organizing
  6. 6. Reconciling Self-A and Self-O…
  7. 7. Outline•  Self-­‐adap)ve  systems   –  Concepts  &  Experiences    •  Self-­‐organizing  systems   –  Concepts  &  Experiences  •  Reconcilia)on   –  Roundabouts  and  more…   –  Future  urban  scenarios  •  Conclusions  
  8. 8. 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  
  9. 9. 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
  10. 10. 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  
  11. 11. 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:  
  12. 12. Using SOTAin 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  
  13. 13. 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  
  14. 14. SOTA Patterns   G  =  ∅,             U  =  U1,U2,...  Un       G  =  G1,G2,...,Gm,                   U  =  U1,U2,...,Un          GASC  =  GCSC  ∩  GACM             UASC  =  UCSC  ∩  UACM        
  15. 15. 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
  16. 16. 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?  
  17. 17. 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
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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
  22. 22. Simulation of Steering Mobility
  23. 23. Integrating Self-organizationand 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
  24. 24. SAPERE Ecosystem of Displays
  25. 25. 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    
  26. 26. 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  
  27. 27. Engineering the Environmentin 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
  28. 28. Engineering the Environmentin 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
  29. 29. Key Question #3•  Are  there  different   approaches  to   reconciliate?   –  I  have  no  answers….   –  However…  
  30. 30. 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
  31. 31. 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    
  32. 32. Smart Cities: From Senseable…•  Sensing what’s happening –  Via ICT devices Sense   –  And social networks•  To better understand (via data analysis) Understand   –  City and social (compute)   dynamics –  At a global level
  33. 33. …To Actuable•  We can “shape” other than understand Sense   Act   (Steer)   –  Actuating ICT device –  Steering human actions•  Closing loops that Understand   enables finalized (compute)   urban behaviors possible
  34. 34. …To Actuable•  We can “shape” other than understand Sense   Act   (Steer)   –  Actuating ICT device –  Steering human actions•  Closing the loop Understand   that enables (compute)   finalized urban behaviors possible
  35. 35. Adaptation in UrbanSuperorganisms•  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
  36. 36. 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
  37. 37. 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!
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