A software engineering approach to software

runtime self-reconfiguration

Fabiano Dalpiaz

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

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Autonomic Computing

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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

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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?

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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

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Software Engineering and Autonomic

Computing

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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?

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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

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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

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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

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Towards goal-based self-reconfiguration

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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

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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

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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

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The MAPE cycle in Jason

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• Jason Reasoning Cycle – an extension of the BDI loop – carried out by each agent

•Monitor1. Perceive the environment

Detect what changed in the environment2. Update the belief base

Decide which changes become beliefs By default, all changes are recorded as

beliefs3. Receive communication from other agents

Agents check the mailbox, one message for each cycle

The MAPE cycle in Jason

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•Analyze4. Select “socially acceptable” messages

Enables the filtering of unwanted messages5. 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 event7. Determine all applicable plans

Verify whether the precondition holds This step filters the relevant plans

The MAPE cycle in Jason

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•Plan8. 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

•Execute10.Execute one step of the intention

One action is performed

Customization in Jason

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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 (SE)Select plan among the applicable ones (SO)

Plan:Select intention (SI)

Execute:Perform an action (act)Send a message (sendMsg)

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)).

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Plan

Precondition

Test goal

Achievementgoal Plan body

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

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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

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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 anotherlayer

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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

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Selecting the best alternative (1)Plan node

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Selecting the best alternative (2)Xor-decomposition

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Selecting the best alternative (3)And-decomposition

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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]

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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).

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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.

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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.

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