Academic Course: 08 Pattern-based design of autonomic systems

Pattern-based design of autonomic systems

Giacomo Cabri - giacomo.cabri@unimore.it

Giovanna Di Marzo Serugendo–giovanna.dimarzo@unige.ch

Jose Luis Fernandez-Marquez –joseluis.fernandez@unige.ch

Outline

• Design patterns

– Reuse

– Composition

• Bio-inspired patterns for autonomic computing

• A classification of patterns for autonomic computing

• References

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Design patterns

• While designing systems, some problems are frequent

• Having solutions that can be reused allows to save time and to rely on tested experiences

• A design pattern is acommon solution to different yet very similar problems

Giovanna.DiMarzo@unige.ch

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Design for the reuse

• The design patterns describe generic solutions that can be applied to specific recurrentproblems

• Do not reinvent the wheel!

• Usually, patterns are collected in a catalogue

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Pattern description

• For each pattern, the following pieces of information are usually reported:– Name

– Aliases

– Problem

– Solution

– Applicability

– Implementation

– … other useful information

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Composition of patterns

• Patterns can be composed, in order to solve different problems (sometimes at different levels)

• The catalogue can provide some suggestions

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From components to ensembles

• Single components can be autonomous

– Or at least exhibit a certain degree of autonomy

• When they are put together in ensembles, there are different architectural choices to make the ensembles autonomic

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Patterns for autonomic systems

• Also in autonomic systems there are some frequent problems that are likely to be already faced and solved

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BIO-INSPIRED PATTERNS

G.DiMarzoSerugendo – J.L. Fernandez-Marquez

Computational Model

• Software agents– Active autonomous entities (services, information, etc.)

• Host– Computing device hosting agent execution

• E.g. sensors, PDAs, computers, …

• Environmental agents– Autonomous entity working on behalf of infrastructure

• E.g. evaporating pheromone, updating gradients

• Environment– Anything else on which agents have no control

• E.g. user switching device off, temperature, humidity, etc.

Bio-inspired Design Patterns

• Bio-inspired design patterns’ Catalogue

– Self-organising mechanisms recurrent and frequently involved in more complex mechanisms

– Identification of:• Inter-relations between mechanisms

• Boundaries of each mechanism

– Classification into three levels

[FMDMSM+12]

Self-Organising Design Patterns

• Description– Abstract Transition Rule

– Sequence Diagram, Implementation details, Explanations

Spreading

http://ergodd.zoo.ox.ac.uk/eurasia/Eurasian%20Street%20Web/Studies/animations/Sim_30_608_393_smoothed.gif

Spreading

• Problem– in systems, where agents perform only local interactions, agents’ reasoning suffers

from the lack of knowledge about the global system.

• Solution– a copy of the information (received or held by an agent) is sent to neighbours and

propagated over the network from one node to another. Information spreads progressively over the system and reduces the lack of knowledge of the agents while keeping the constraint of the local interaction.

• Entities – Dynamics – Environment

• Usage– Information dissemination

Spreading

• Implementation

Aggregation

• Problem– Excess of information produces overloads. Information must be distributively

processed in order to reduce the amount of information and to obtain meaningful information.

• Solution– Aggregation consists in locally applying a fusion operator to synthesise macro

information (filtering, merging, aggregating, or transforming)

• Usage– Aggregation of pheromones, fusion of information, context-awareness

Aggregation

• Implementation

InfrastucturalAgent or

Agentdata_req()

send_data()

Apply Aggregation

send_data_aggr()

StoreAggregated

Evaporation

• Problem– Outdated information cannot be detected and it needs to be removed, or its detection

involves a cost that needs to be avoided. Agent decisions rely on the freshness of the

information presented in the system, enabling correct responses to dynamic environments.

• Solution– Evaporation is a mechanism that periodically reduces the relevance of information. Thus,

recent information becomes more relevant than older information.

• Usage– Digital pheromone, context-awareness (ageing information)

Evaporation

• Implementation

Rel(Inf) > 0 ?

Apply Evap.Stop

Repulsion

• Problem– Agents’ movements have to be coordinated in a decentralised manner in order to achieve a

uniform distribution and to avoid collisions among them

• Solution– The Repulsion Pattern creates

a repulsion vector that guides agents to move from regions with high concentrations of agents to regions with lower concentrations. Thus, after few iterations agents reach a more uniform distribution in the environment

• Usage– Shape formation

Repulsion

• Implementation

Calculate

Repulsion()

Received

Positions?

SendPositionReque

stToNeighbours()

CalculateDesired

Position()

MoveToHostClosest

ToNewPosition()

HostAgent NeighbouringHosts

SendPositionRequest()

SendPosition()SendPosition()

CalculateRepulsionVector()

CalculateDesiredPosition()

MoveToHostClosestToNewPosition() MoveToHostClosest

ToNewPosition()

Gossip

• Problem– in large-scale systems, agents need to reach an agreement, shared among all agents,

with only local perception and in a decentralised way

• Solution– information spreads to neighbours, where it is aggregated with local information.

Aggregates are spread further and their value progressively reaches the agreement

• Usage– Computation of sums, averages

Gossip

• Implementation

Broadcast aggregated Information

input events?

Same value broadcasted

before?

Apply aggregation

operator

apply aggr. operator

HostAgent or

Infras. AgentNeighbour

send(inf)send(inf)

send(aggr)

Gradient

• Problem– agents belonging to large systems suffer from lack of global knowledge to estimate the consequences of

their actions or the actions performed by other agents beyond their communication range

• Solution– information spreads from the location it is initially deposited and aggregates when it meets other

information. During spreading, additional information about the sender’s distance and direction is provided: either through a distance value (incremented or decremented); or by modifying the information to represent its concentration (lower concentration when information is further away).

• Usage– Routing in ad-hoc networks

Gradient

• ImplementationStart

Broadcastinf. incrementing

counter

input events?

Distance is lower than local one?

Aggregated inf. (The one with lower distance stays stored)

HostAgent

check inf andAggregate.

NeighbourHosts

send(inf,0)

HostAgent or

Infras. AgentNeighbour

send(inf,d)send(inf,d)

send(inf, d+1)

Digital Pheromone

• Problem– coordination of agents in large scale environments using indirect communication

• Solution– digital pheromone provides a way to coordinate agent’s behaviour using indirect

communication in high dynamic environments. Digital pheromones create gradients that spread over the environment, carrying information about their distance and direction. Thus, agents can perceive pheromones from the distance and increase the knowledge about the system. Moreover, as time goes by digital pheromones evaporate, providing adaptation to environmental changes

• Usage– Autonomous coordination of swarms (UAV)

Digital Pheromone

Ant Foraging

• Problem– large scale optimisation problems that can be transformed into the

problem of finding the shortest path on a weighted graph.

• Solution– the Ant Foraging Pattern provides rules to explore the environment

in a decentralised manner and to exploit resources

• Usage– Ant Colony Optimisation

Ant Foraging

• Implementation

Morphogenesis

• Problem– in large-scale decentralised systems, agents decide on their roles or plan their

activities based on their spatial position

• Solution– specific agents spread morphogenetic gradients. Agents assess their positions in

the system by computing their relative distance to the morphogenetic gradients sources

• Usage– Meta-morphic robots, shape formation

Morphogenesis

• Implementation

Gradients Received?

Estimate relative position based on received gradients

Change role according to the relative position

HostAgentNeighbour

GradInf()

Estimate relative position

ChangeAgent's role

Chemotaxis

• Problem– decentralised motion coordination aiming at detecting sources or boundaries of

events

• Solution– agents locally sense gradient information and follow the gradient in a specified

direction (i.e. follow higher gradient values, lower gradient values, or equipotential lines of gradients).

• Usage– Routing messages

Chemotaxis

• Implementation

No Inf. Received?

Move to host with highest gradient

Send concetration

request to n

neighbouring hosts

HostAgentNeighbour

GradRequest()

Send_grad()

Choose hostwith highest

concentration

MoveToHost(h)MoveToHost(h)

Quorum Sensing

• Problem– collective decisions in large-scale decentralised systems, requiring a threshold number

of agents or estimation of the density of agents in a system, using only local interactions.

• Solution– the Quorum Sensing Pattern allows to take collective decisions through an estimation

by individual agents of the agents’ density (assessing the number of other agents they interact with) and by determination of a threshold number of agents necessary to take the decision

• Entities – Dynamics – Environment– Gradient, Morphogenesis

• Usage– Power saving in wireless sensor networks, swarms decisions

Quorum Sensing

• Implementation

Gradients

Receved?

Trigger collaborative

Gradient's

concentration higher

than threshold?

HostAgentNeighbour

GradInf()

if Grad > threstrigger task

Flocking

• Problem– Dynamic motion coordination and pattern formation of swarms

• Solution– The Flocking Pattern provides a set of rules for moving groups of agents over the

environment while keeping the formation and interconnections between them

• Entities – Dynamics – Environment– Cohesion (group)

– Separation

– Alignment (velocity, direction)

– Group objective (target)

• Usage– Movies, advertisement, games, modelling crowds

CLASSIFICATION OF PATTERNS

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Why a classification?

• Classification of the existing patterns

• Suggestions to developers

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The considered components

• Service component (SC)

– It is the basic component of ensembles

– It provides some services

– It can be reactive or goal-driven

• Autonomic manager (AM)

– It is the component that adds autonomicity to the ensemble

– It is goal-driven

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The considered components

Service Component

AutonomicManager

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Classification mechanisms

• Two orthogonal directions

– Component level

– Ensemble level

• Component level

– Relates to the addition of autonomic manager(s) to a component

• Ensemble level

– Relates to the aggregation of more service components in an ensemble

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Component level

• One or more AM can be added

• When more AMs are present, there can be two relations:

– P2P, in which AMs work in parallel on different aspects

– Hierarchical, in which one AM manages another AM

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Ensemble level

• An ensemble can be made of different SCs

• They can

– Interact directly

– Interact by means of the environemnt

– Interact by means of the AM

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Component and ensemble levels

• Putting together the two directions, we can have a “table”, where each cell represents a step related to components along with a step related to ensembles

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The complete taxonomy

ENVIRONMENT

Reactive

ENVIRONMENT

Proactive

ENVIRONMENT

ENVIRONMENTENVIRONMENT ENVIRONMENT

ENVIRONMENT ENVIRONMENT

active

ENVIRONMENT

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Examples of classified patterns

• Reactive Stigmergy Service Components Ensemble

• Cognitive Stigmergy Service Components Ensemble

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Reactive Stigmergy Service Components Ensemble

• Pattern Name: Reactive Stigmergy Service Components Ensemble.• Classification: Pattern for Service Components Ensemble.• Intent: There are a large amount of components that are not able

to directly interact one to each other. The components simply react to the environment and sense the environment changes.

• Context: This patterns has to be adopted when:– there are a large amount of components acting together;– the components need to be simple component, without having a lot of

knowledge;– the environment is frequently changing;– the components are not able to directly communicate one with the

other.

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Reactive Stigmergy Service Components Ensemble (2)

• Behaviour: This pattern has not a direct feedback loop. Each single component acts like a bioinspired component (e.g. an ant). To satisfy its simple goal, the SC acts in the environment that senses with its “sensors” and reacts to the changes in it with its “effectors”. The different components are not able to communicate one with the other, but are able to propagate information (their actions) in the environment. Than they are able to sense the environment changes (other components reactions) and adapt their behaviour due to these changes.

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Reactive Stigmergy Service Components Ensemble (3)

Service Component

SENSOR

EFFECTOR

INPUT (service request)

OUTPUT(serviceresponse)

ENVIRONMENT

Service Component

SENSOR

EFFECTOR

Service Component

SENSOR

EFFECTOR

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Cognitive Stigmergy Service Components Ensemble

• Pattern Name: Cognitive Stigmergy Service Components Ensemble.• Classification: Pattern for Service Components Ensemble.• Intent: In this pattern each component needs an external feedback

loop because it is not able to fully adapt alone. The component (and its AM) is not able to direct communicate with the others components. It reacts to the environment changes and uses its AM to choose how to act.

• Context: This patterns has to be adopted when:– there are a large amount of components acting together;– the components are simple and necessitate an external AM to manage

adaptation;– the components and the AMs are not be able to directly communicate one

to the other;– the environment is frequently changing.

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Cognitive Stigmergy Service Components Ensemble (2)

• Behaviour: Each single components acts like an autonomic component: the SC has an explicit feedback loop managed by an external AM. The different components and the AMs are not able to directly communicate one with the other, but are able to propagate information in the environment via “effectors”. Than, using “sensors”, they are able to sense the environment changes (other components reactions) and adapt their behaviour due to these changes.

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Cognitive Stigmergy Service Components Ensemble (3)

AutonomicManager

ServiceComponent

EMITTERCONTROL

SENSOREFFECTOR

EFFECTOR SENSOR

AutonomicManager

ServiceComponent

EMITTERCONTROL

SENSOREFFECTOR

EFFECTOR SENSOR

AutonomicManager

ServiceComponent

EMITTERCONTROL

SENSOREFFECTOR

EFFECTOR SENSOR

ENVIRONMENTGiacomoCabri

Classifying patterns

• The pattern «Ant foraging» can be classified as Reactive Stigmergy Service Components Ensemble

• If an AM is added to the Ant foraging, this can be classified as Cognitive Stigmergy Service Components Ensemble

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Going further

• Multi-level architecture

– Autonomic managers

– Service components

– Environemt AM layer

SC layer

ENVIRONMENT

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References

• E. Gamma, R. Helm, R. Johnson, J. Vlissides, “Design Patterns”, Pearson • Fernandez-Marquez, Jose Luis and Di MarzoSerugendo, Giovanna and

Montagna, Sara and Viroli, Mirko and Arcos, JosepLluis, “Description and composition of bio-inspired design patterns: a complete overview”, Natural Computing, 2012, Springer Verlag

• G. Cabri, M. Puviani, F. Zambonelli , “Towards a Taxonomy of Adaptive Agent-based Collaboration Patterns for Autonomic Service Ensembles”, The 2011 International Workshop on Adaptive Collaboration at the International Conference on Collaboration Technologies and Systems (AC/CTS 2011), Philadelphia, Pennsylvania, USA, May 2011

• M. Puviani, G. Cabri, L. Leonardi , “Adaptive Patterns for Intelligent Distributed Systems: a Swarm robotics Case Study”, The 6th International Symposium on Intelligent Distributed Computing - IDC 2012, Calabria, Italy, September 2012, Springer Verlag

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