1. A software engineering approach to software runtime self-reconfiguration Fabiano Dalpiaz

2. Talk outline Autonomic Computing (AC) ▫Motivation ▫Self-* properties ▫MAPE cycle Software engineering and AC ▫The need of a software engineering approach ▫Goal models to drive self-reconfiguration ▫Key challenges Towards goal-based self-reconfiguration ▫Required subsystems ▫BDI and Jason ▫Towards self-reconfiguration in Jason F. Dalpiaz 2

3. Autonomic Computing F. Dalpiaz 3

4. Why Autonomic Computing? Current and future software exhibits ▫Inherent complexity ▫Computational distribution ▫A set of different strategies/behaviors (variability) ▫Interaction among different “components”  Software  Hardware (in general, sensors and effectors)  Humans ▫Run-time adaptivity to unpredicted circumstances Autonomic Computing F. Dalpiaz 4

5. Autonomic Computing and self-* properties Autonomic Computing: computing systems that can manage themselves given high-level objectives from administrators [Hor01] Self-* properties ▫Self-(re)configuration ▫Self-healing ▫Self-optimization ▫Self-protection Self-reconfiguration and self-* ▫Is self-reconfiguration the mechanism to enact the other self-* properties? F. Dalpiaz 5

6. The Monitor-Analyze-Plan-Execute cycle Autonomic Computing systems are made up of several autonomic elements [Kep03] MAPE cycle ▫Monitor  What changed/happened? ▫Analyze  How to face the change ▫Plan  Define the actions to perform ▫Execute  Perform actions in response to the change Self-reconfiguration is tightly linked to MAPE F. Dalpiaz 6

7. Software Engineering and Autonomic Computing F. Dalpiaz 7

8. The need of a Software Engineering approach Many current approaches are poorly engineered ▫Ad-hoc  Focused on a single self-* property  Related to a particular case study (resource allocation, very often) ▫Abstract  Architectures makes sense if applied to real code  Lack of a “standard” infrastructure Some engineering approaches exist ▫Look at EASe and SEAMS@ICSE workshops Why not using a goal-driven approach? F. Dalpiaz 8

9. i*/Tropos to drive self-reconfiguration Basic ingredients ▫Goals are functional requirements to be achieved ▫Plans are sequences of actions; plans are performed to achieve goals ▫Soft-goals are qualities that can be evaluated to select the best behavior ▫Variability constructs (OR-decomposition is the main one) enable different behaviors ▫Dependencies enable social interaction between different agents/actors F. Dalpiaz 9

10. Autonomic Computing and goal models: related work Existing work on goal models and AC ▫Requirements-driven design of autonomic application software [Lap06] ▫Exploring and evaluating alternatives in socio- technical systems [Bry06] ▫Design of high-variability software agents [Pen07] ▫Reverse Engineering Goal Models from Legacy Code [Yu05] The focus is mainly on design-time or offline ▫A run-time and online solution is missing F. Dalpiaz 10

11. Key ingredients to engineer autonomic software Architecture ▫(Web)-Services or Agents to support distribution ▫Adapt established infrastructures ▫Guarantee reuse, extensibility, and scalability Algorithms and reconfiguration mechanisms ▫The following questions should be answered:  When reconfiguring?  Why reconfiguring?  Which behavior should be selected?  How to enact compensation if something fails? Integration with a SW development methodology F. Dalpiaz 11

12. Towards goal-based self- reconfiguration F. Dalpiaz 12

13. Supporting self-reconfiguration: subsystems Monitor ▫Goals/Plans – check what went wrong ▫Environment – react to changes Communication ▫Agents (or autonomic elements) need an infrastructure enabling interaction Analyze/Reasoning ▫Decide if events should be handled ▫Choose the best strategy when variability exists Planning ▫Define the course of actions to be carried out Execution ▫Support both simulated and real environments F. Dalpiaz 13

14. BDI basics Belief-Desire-Intention architecture ▫An agent-based architecture ▫Suitable to drive self-reconfiguring software ▫Beliefs represent the informational state of the agent ▫Desires (or goals) represent the motivational state of the agent ▫Intentions represent the deliberative state of the agent ▫Plans are sequences of actions that an agent can perform to achieve one or more of its intentions F. Dalpiaz 14

15. Jason: an AgentSpeak interpreter Jason [Bor07] is interpreter for an extended version of AgentSpeak ▫AgentSpeak [Rao96] is a logic-based BDI language ▫The non-logical coding of agents exploits Java ▫Jason can be run centralized or decentralized (using JADE [Bel99] or SACI [Hub03]) ▫The framework is highly extensible and customizable ▫Goals are events, which are handled by plans ▫A shared environment exists, affected by actions F. Dalpiaz 15

16. The MAPE cycle in Jason F. Dalpiaz 16 Jason Reasoning Cycle – an extension of the BDI loop – carried out by each agent Monitor 1.Perceive the environment  Detect what changed in the environment 2.Update the belief base  Decide which changes become beliefs  By default, all changes are recorded as beliefs 3.Receive communication from other agents  Agents check the mailbox, one message for each cycle

17. The MAPE cycle in Jason F. Dalpiaz 17 Analyze 4.Select “socially acceptable” messages  Enables the filtering of unwanted messages 5.Select an event  Each cycle requires the selection of only one event 6.Retrieve all relevant plans  The plan library is searched for plans to handle the event 7.Determine all applicable plans  Verify whether the precondition holds  This step filters the relevant plans

18. The MAPE cycle in Jason F. Dalpiaz 18 Plan 8.Select one applicable plan  Meta-reasoning can be applied to select the plan 9.Select an Intention  Among the several concurrent intentions, choose one for the current reasoning cycle Execute 10.Execute one step of the intention  One action is performed

19. Customization in Jason F. Dalpiaz 19 Monitor: Perceive the environment (perceive) Belief Update Function (BUF) Check incoming messages (checkMail) Analyze: Determine socially acceptable messages (SocAcc) Belief Revision Function (BRF) Select event (S E ) Select plan among the applicable ones (S O ) Plan: Select intention (S I ) Execute: Perform an action (act) Send a message (sendMsg)

20. On the notion of goal: Jason vs. i*/Tropos Goals in Jason ▫Goals are not declarative by their own nature  A declarative goal ensures that the expected state of the world is reached, after a plan is carried out  A plan executed to handle the achievement goal !drink(Beer) doesn’t assure the agent will believe drink(Beer) to be true  Goals can be made declarative using test goals +!drink(Beer) : drink(Beer) <- true. +!drink(Beer) : handles(Beer) <- open(Beer); empty(Beer); ?drink(Beer). +drink(Beer) : true <-.succeed_goal(drink(Beer)). F. Dalpiaz 20 Plan Precondition Test goal Achievement goal Plan body

21. On the notion of goal: Jason vs. i*/Tropos Goals in i*/Tropos ▫OR/AND goal Decomposition  Difficult to elegantly implement in Jason ▫What’s the purpose of internal goals?  internal = not leaf & not root  Are they part of the strategy (hence, plans) or of the requirements (hence, goals)?  They can be seen a way of organizing plans  Plans are code (have a body)  Goals enable variability and plans composition F. Dalpiaz 21

22. Adding self-reconfiguration to Jason: on the way Take Jason as starting point ▫Extend Jason using its customization points ▫Integrate goal-model reasoning (i*/Tropos) Two-levels reconfiguration ▫In the small (locally)  Every agent chooses the best option ▫In the large (globally)  Optimize the social structure (dependencies)  Use metrics to define what is optimal  Contracting vs. Enforcing dependencies  Organizational hierarchy to drive dependencies F. Dalpiaz 22

23. Adding self-reconfiguration to Jason: on the way Goal reasoning vs. plan reasoning ▫Plans treated as atomic entities in the goal model ▫Plans “are” the leaf-level nodes of goal models ▫Plans can be refined in another layer F. Dalpiaz 23

24. An algorithm to select the best alternative Selecting the best alternative (locally) ▫Plans pre-conditions checked against the environment  If false, the plan cannot be applied ▫Quantitative soft-goal contribution  Post-order visit (leafs first)  Bottom-up propagation: from plans to the root level goal  Soft-goals have a weight (their sum is 1.0)  Xor-decomposition  select the best option  And-decomposition  average F. Dalpiaz 24

25. Selecting the best alternative (1) Plan node F. Dalpiaz 25

26. Selecting the best alternative (2) Xor-decomposition F. Dalpiaz 26

27. Selecting the best alternative (3) And-decomposition F. Dalpiaz 27

28. Next steps Evaluate and select the best approach for ▫Monitoring – guarantee scalability ▫Environment representation  Use of an ontology?  In-progress work for location-based software [Ali08] ▫Global reconfiguration ▫Compensation of plan failure Case study identification ▫An industrial case study would be very effective Design tool-support ▫Integrate with existing AOSE methodology ▫Tropos is being extended in this direction [Mor08] F. Dalpiaz 28

29. References (1) [Hor01] Horn, P.: Autonomic Computing: IBM’s Perspective on the State of Information Technology. Keynote address at National Academy of Engineers at Harvard University. March 2001. [Kep03] Kephart, J., Chess, D.: The Vision of Autonomic Computing. Computer 36(1) (Jan 2003) 41–50. [Lap06] Lapouchnian, A., Yu, Y., Liaskos, S., Mylopoulos, J.: Requirements-driven design of autonomic application software. Proceedings of CASCON 2006 (2006). [Bry06] Bryl, V., Giorgini, P., Mylopoulos, J.: Designing cooperative is: Exploring and evaluating alternatives. CoopIS 6 (2006) 533–550. [Pen07] Penserini, L., Perini, A., Mylopoulos, J.: High Variability Design for Software Agents: Extending Tropos. ACM TAAS Journal 2(4) (2006). F. Dalpiaz 29

30. References (2) [Yu05] Yu, Y., Wang, Y., Mylopoulos, J., Liaskos, S., Lapouchnian, A., do Prado Leite, J.C.S.: Reverse engineering goal models from legacy code. In Proceedings of International Conference on Requirements Engineering, pages 363–372, 2005. [Bor07] Bordini, R.H., Wooldridge, M., Hubner, J.F.: Programming Multi-Agent Systems in AgentSpeak using Jason (Wiley Series in Agent Technology). John Wiley & Sons (2007). [Rao96] Rao, A.S.: AgentSpeak (L): BDI Agents Speak Out in a Logical Computable Language. In Proceedings of MAAMAW’96, 1996. [Bel99] Bellifemine, F., Poggi, A., Rimassa, G.: JADE – A FIPA- compliant agent framework. In Proceedings of PAAM, 1999. [Hub03] Hubner, J.F., Sichman, J.S.: SACI Programming Guide. F. Dalpiaz 30

31. References (3) [Ali08] Ali, R., Dalpiaz, F., Giorgini, P.: Location-based Variability for Mobile Information Systems. To appear in CAiSE’08. [Mor08] Morandini, M., Penserini, L., Perini, A.: Towards Goal- Oriented Development of Self-Adaptive Systems. To appear in SEAMS’08 workshop @ICSE. F. Dalpiaz 31