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
GiacomoCabri

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
GiacomoCabri

Design for the reuse
• The design patterns describe generic solutions
that can be applied to specific recurrent
problems
• 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

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

Self-Organising Design Patterns

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

Aggregation
• Implementation
Host
Infrastuctural
Agent or
Agent
data_req()
send_data()
Apply
Aggregation
send_data_aggr()
Store
Aggregated
Data

Evaporation

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 ?
Start
Apply Evap.Stop
YesNo
rev?
yesNo

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

Repulsion
• Implementation
Start
Calculate
Repulsion()
Received
Positions?
yes
No
SendPositionReque
stToNeighbours()
CalculateDesired
Position()
MoveToHostClosest
ToNewPosition()
HostAgent Neighbouring
Hosts
SendPositionRequest()
SendPositionRequest()
SendPosition()
SendPosition()
CalculateRepulsion
Vector()
CalculateDesired
Position()
MoveToHostClosest
ToNewPosition() MoveToHostClosest
ToNewPosition()

Gossip

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
Start
Broadcast
aggregated
Information
Stop
input
events?
yes
no
Same value
broadcasted
before?
yes
no
Apply
aggregation
operator
apply aggr.
operator
Host
Agent or
Infras. Agent
Neighbour
Hosts
send(inf)
send(inf)
send(aggr)
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

Gradient
• Implementation
Start
Broadcast
inf. incrementing
counter
Stop
input
events?
yes
No
Distance is
lower than
local one?
no
yes
Aggregated inf.
(The one with
lower distance
stays stored)
HostAgent
check inf and
Aggregate.
Neighbour
Hosts
send(inf,0)
send(inf,0)
Host
Agent or
Infras. Agent
Neighbour
Hosts
send(inf,d)
send(inf,d)
send(inf, d+1)
send(inf, d+1)

Digital Pheromone

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

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

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
Start
No
Gradients
Received?
yes
Estimate relative
position based on
received gradients
Change role
according to the
relative position
HostAgent
Neighbour
Hosts
GradInf()
GradInf()
Estimate
relative position
Change
Agent's role

Chemotaxis

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
Start
yes
No Inf. Received?
yes
Move to host with
highest gradient
Send concetration
request to n
neighbouring hosts
HostAgent
Neighbour
Hosts
GradRequest()
GradRequest()
Send_grad()
Send_grad()
Choose host
with 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
– Gradient, Morphogenesis
• Usage
– Power saving in wireless sensor networks, swarms decisions
Quorum Sensing

Quorum Sensing
• Implementation
Start
No
Gradients
Receved?
yes
Trigger collaborative
task
Gradient's
concentration higher
than threshold?
No
yes
HostAgent
Neighbour
Hosts
GradInf()
GradInf()
if Grad > thres
trigger task

Flocking

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
– Cohesion (group)
– Separation
– Alignment (velocity, direction)
– Group objective (target)
• Usage
– Movies, advertisement, games, modelling crowds

CLASSIFICATION OF PATTERNS
GiacomoCabri

Why a classification?
• Classification of the existing patterns
• Suggestions to developers
GiacomoCabri

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
Autonomic
Manager
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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
SC
ENVIRONMENT
SC SC
S
C
A
M
S
C
S
C
SC
AM
A
M
S
C
A
M
S
C
A
M
S
C
ENVIRONMEN
T
A
M
S
C
A
M
S
C
A
M
S
C
A
M
AM
SC
AM
A
M
S
C
A
M
A
M
S
C
A
M
A
M
S
C
A
M
E
N
V
I
R
O
N
M
E
N
T
A
M
S
C
A
M
A
M
S
C
A
M
S
C
A
M
S
C
ENVIRONMENT
A
M
A
M
A
M
A
M
S
C
A
M
S
C
A
M
S
C
A
M
A
M
A
M
A
M
S
C
A
M
S
C
A
M
S
C
A
M
A
M
A
M
S
C
A
M
S
C
A
M
S
C
A
M
A
M
A
M
A
M
Reacti
ve
ENVIRONMENT
Proac
tive
ENVIRONMENT
ENVIRONMENT
ENVIRONMENT ENVIRONMENT
ENVIRONMENT
A
M
S
C
A
M
S
C
A
M
S
C
Pr
o
act
ive
Pr
o
act
ive
Pr
o
act
ive
ENVIRONMENT
GiacomoCabri

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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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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Service Component
SENSO
R
EFFECTOR
INPUT
(service
request)
OUTPUT
(service
response)
ENVIRONMENT
Service Component
SENSO
R
EFFECTOR
INPUT
(service
request)
OUTPUT
(service
response)
Service Component
SENSO
R
EFFECTOR
INPUT
(service
request)
OUTPUT
(service
response)
GiacomoCabri

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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• 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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Autonomic
Manager
Service
Component
EMITTERCONTROL
SENSOREFFECTOR
EFFECTOR SENSO
R
Autonomic
Manager
Service
Component
EMITTERCONTROL
SENSOREFFECTOR
EFFECTOR SENSO
R
Autonomic
Manager
Service
Component
EMITTERCONTROL
SENSOREFFECTOR
EFFECTOR SENSO
R
ENVIRONMENT
GiacomoCabri

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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Academic Course: 08 Pattern-based design of autonomic systems

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