Reconciling self-adaptation and self-organization

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Franco Zambonelli's presentation at SEAMS 2012

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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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