From Engineer to Alchemist, There and Back Again: An Alchemist Tale

SAPERE Self-aware Pervasive Service Ecosystems
From Engineer to Alchemist, There and Back Again: An
Alchemist Tale
Danilo Pianini – danilo.pianini@unibo.it
Alma Mater Studiorum—Universit`a di Bologna
Cesena, May 8th, 2012
Danilo Pianini (UniBo) An Alchemist Tale Cesena, May 8th, 2012 1 / 46

Outline Do or do not, there is no try.
1 Simulation
Deﬁnitions
Models
When it’s useful?
2 Alchemist
SAPERE background
Computational model
Engine and architecture
Case studies
Performance
3 Development
Development model
Contributors
Future activities

Simulation
Simulation

Simulation Definitions
Scientific Method
Traditional science workflow [Parisi, 2001]
Traditional scientific method
identification
direct observation
theories / hypothesis
empirical observation
quantitative analysis
validation / invalidation

Simulation Definitions
Definition of Simulation
A new way for describing scientific theories
[Parisi, 2001]
Simulation is the process with which we can study the dynamic
evolution of a model system, usually through computational tools
[Banks, 1999]
Simulation is the imitation of the operation of a real-world process or
system over time

Simulation Models
Simulation Requires a Model
M. Minsky – Models, Minds, Machines
A model (M) for a system (S), and an experiment (E) is anything to which
E can be applied in order to answer questions about S.
Representation / abstraction
Formalisation
Aggregation, Simpliﬁcation, Omission
Building a model...
How complex should be the model?
Which assumptions should be done?

Simulation Models
From Model to Simulation. . .
Computer simulation
Models are designed runnable processes
Simulation creates a virtual laboratory
controlled conditions
it’s easy to modify the experiment (variables, parameters, etc.)
Simulations imitate the operations of the modelled process
generation of an artiﬁcial evolution of the system

Simulation Models
. . . and Back
Deductions on the real system represented
Evaluation of theories about the model
Model validation [Klugl and Norling, 2006]
if the data is reliable;
if prediction doesn’t match the observed behaviour do not match
⇒ the model must be revised

Simulation When it’s useful?
Why do we Need Simulations?
[Parisi, 2001, Klugl and Norling, 2006]
The real system cannot actually be observed
The time scale of the real system is too small or too large for
observation
The original system doesn’t yet exist (or not anymore)
The system is too complex

Alchemist
Alchemist

Alchemist SAPERE background
The SAPERE Project
http://www.sapere-project.eu/
SAPERE (Self-aware Pervasive Service Ecosystems) is an EU STREP
project under the FP7 FET Proactive Initiative: Self-Awareness in
Autonomic Systems (AWARENESS)
The objective of SAPERE is the development of a highly-innovative
theoretical and practical framework for the decentralized deployment
and execution of self-aware and adaptive services for future and
emerging pervasive network scenarios

SAPERE World
LSA Space
LSA
LSALSA
LSA
eco-law
Engine
Software services
SAPERE Node
eco-law activation
SAPERE
Node
SAPERE
Node
SAPERE
Node
SAPERE
Node
SAPERE
Node
SAPERE
Node
SAPERE
Node
SAPERE
Node
SAPERE
Node
SAPERE
Node

Simulation of a SAPERE environment
The role of simulation
Emergence cannot be fully designed
It’s crazy to deploy a whole ecosystem without any test
Fist class abstractions
Dynamic environment
Diﬀerent, mobile, communicating nodes
Programmability through a set of chemical-like laws
Continuous Time Markov Chain (CTMC) model
Autonomous agents

Two approaches
Classic ABM modelling
High flexibility
Topology as first-class abstraction
Dynamics explicitly modelled
No native support for CTMC model
Chemical inspired modelling
Natively CTMC
Very fast and reliable algorithms exist in literature
Very limited topology: multicompartment at best
Only classic reactions can change the world status: limited flexibility

Alchemist Computational model
Alchemist simulation approach
Base idea
Start from the existing work with stochastic chemical systems
simulation
Extend it as needed to reach desired flexibility
Goals
Full support for Continuous Time Markov Chains (CTMC)
Support for differently distributed events (e.g. Triggers)
Rich environments, with obstacles, mobile nodes, etc.
More expressive reactions
Fast and flexible SSA engine

Enriching the environment description
Environment
Node
Reactions
Molecules
Alchemist world
The Environment contains and links together Nodes
Each Node is programmed with a set of Reactions
Nodes contain Molecules
Each Molecule in a node is described with a Concentration

Extending the concept of reaction
From a set of reactants that combine themselves in a set of products to:
Number of
neighbors<3
Node
contains
something
Any other
condition
about this
environment
Rate equation: how conditions
influence the execution speed
Conditions Probability distribution Actions
Any other
action
on this
environment
Move a node
towards...
Change
concentration
of something
Reaction
In Alchemist, every event is an occurrence of a Reaction

Alchemist Engine and architecture
SSA Algorithms
Several SSA exist, they follow the same base schema [Gillespie, 1977]:
1 Select next reaction using markovian rates
2 Execute it
3 Update the rates which may have changed

Do the math: reaction speed
Consider a chemical reaction in the form:
A + B
µ
−→ C
The probability that this reaction will trigger depends on:
1 Number of molecules A and B: the higher, the higher the probability
those molecule will encounter and react
2 µ, a speed coeﬃcient for the reaction
We deﬁne the propensity of a reaction as its speed in a precise instant of
time as
aµ = µ[A][B]

Do the math: next reaction choice
If we assume every reaction is a Poisson process, the probability for it to
be the next one is:
P(next = µ) =
∞
0
P(µ, τ)dτ =
∞
0
aµe−τ j aj
dτ =
aµ
j aj

Do the math: next reaction time
We can also compute the next time of occurrence:
P(τ)dτ =
j
P(µ, τ)dτ =


j
aj

 e−τ j aj
dτ
j
aj = λ −→ λe−λx
F(x ≤ t) =
t
−∞
λe−λx
dx = −e−λt
t
−∞
= e−λt
Now, given a uniformly distributed random r, it’s possible to compute it’s
equivalent for the desired distribution:
e−λt
= r ⇒ t =
− ln (r)
λ

Existing algorithms
Direct method
1 Compute propensity for each reaction
2 Select next reaction probabilistically
3 Execute it

Existing algorithms
A+B→C
B+C→D E+G→A
D+E→E+F F→D+G
Direct method + Dependency graph
1 Compute the dependencies among reactions
2 Compute propensity for each reaction
3 Select next reaction probabilistically
4 Execute it
5 Update propensities only for potentially involved reactions
6 Goto 3

Existing algorithms
Next Reaction
1 Compute a putative execution time for each reaction
2 Store reactions in a binary heap
3 Pick the next reaction
4 Execute it
5 Compute putative times only for potentially involved reactions
6 Goto 3

Existing algorithms
[Slepoy et al., 2008]
1 Compute propensities
2 Split the reactions in groups by their propensity
3 Throw randoms until a reaction in a group is selected
4 Execute it
5 Update propensities only for potentially involved reactions
6 Goto 3

More ﬂexibility!
What they miss is what we added
Support for instantaneus events (∞ Markovian rate)
Reactions can be added and removed during the simulation
Support for non-exponential time distributed events (e.g. triggers)
Dependencies among reactions are evaluated considering their
“context”, speeding up the update phase

Smart data structures ⇒ bleeding edge performances
2.0
4 4
3.7
1
7.3
2 1
5.5
1 0
2
8.9
1 0
4.2
0 0
9.1
0 0
10.1
0 0
inf
0 0
A+B→C
B+C→D E+G→A
D+E→E+F F→D+G
Next Reaction eﬃcient structures made dynamic
Dynamic Indexed Priority Queue
Allow to access the next reaction to execute in O(1) time
Worst case update in log2 (N) (average case a lot better)
Extended to ensure balancing with insertion and removal
Dynamic Dependency Graph
Allows to smartly update only a subset of all the reaction
Extended with the concept of input and output context

Modular structure
Environment
User Interface
Alchemist language
Application-specific Alchemist Bytecode Compiler
Environment description in application-specific language
Incarnation-specific language
Reporting System
Interactive UI
Reaction Manager
Dependency Graph
Core Engine
Simulation Flow Language Parser
Environment Instantiator
XML Bytecode

Some Features in short
Parallel executor
Approximate Stochastic Model Checker
Parallelised engine
Alchemist2Blender
PVeStA integration

Alchemist Case studies
Crowd evacuation
EXIT 1
EXIT 2

Crowd steering

Adaptive Displays

Simple Morphogenesis proof of concept

Morphogenesis of a Drosophila Melanogaster

Anticipative adaptation
0
5
10
15
20
25
30
35
40
45 0
5
10
15
20
25
30
35
40
45
0
10
20
30
40
50
60
'datald.log' using 2:3:4
0
10
20
30
40
50
60

Realistic pedestrians
see the video...

Alchemist Performance
Testbed
We realised the same crowd-steering application for both Alchemist
and RePast, in order to evaluate the performance gap (if any)
Source code for RePast and standalone Alchemist application
available at
http://www.apice.unibo.it/xwiki/bin/view/Alchemist/JOS
We choose a simpliﬁed use case which allows simulation in RePast
without any change in its engine
This actually cripples out the most important Alchemist optimization
for complex environment (the dependency graph)

Alchemist Performance
Results
0
50
100
150
200
250
300
350
400
450
500
50 100 150 200 250 300 350 400 450 500
Executiontime[s]
Number of agents
Performance comparison with Repast
Repast Alchemist

Development
Development

Development Development model
Distributed development
Alchemist is a training ground for some good team development practices
Linux kernel-like development model
Java
XText
Mercurial DCVS
Bitbucket web-based code hosting
Maven
JUnit
Source is released under GPL

Development Contributors
People involved
Michele Bombardi (Done)
Chiara Casalboni (Done)
Francesca Cioﬃ (Done)
Further experiments with Alchemist-SAPERE
Davide Ensini (Done)
Approximate Stochastic Model Checker improvement
Enrico Galassi (Done)
Alchemist-SAPERE high level language
Enrico Gualandi (Ongoing)
Alchemist-SAPERE languages review

Development Contributors
People involved
Luca Mella (Done)
Tools for social network analysis
Alessandro Montalti (Done)
MS-BioNet to AlchemistXML translator
Luca Nenni (Done)
Alchemist2Blender
Enrico Polverelli (Done)
Gradient based patterns
Michele Pratiﬃ (Done)
Image importer for Alchemist
Giacomo Pronti (Done)
SAPERE incarnation main author
Luca Ricci (Ongoing)
Map importer for Alchemist
Andrea Vandin (IMT Lucca)
PVeStA integration

Development Future activities
There is a lot of work to do!
MS-Bionet compatibility layer
AlcheGRID
Alchemist2Blender improvement
OpenStreet Map importer and Google Map importer
Blender Integration improvement
Gnuplot integration
CellML / SBML to AlchemistXML
Chemistry Incarnation review
Alchemist for Bio DSL
Realistic biological gradients
GPX tracks loader
RDF to Alchemist-SAPERE translator
These are examples, if you have something in mind, be proactive!

Bibliography I
Banks, J. (1999).
Introduction to simulation.
In Farrington, P., Nembhard, H. B., Sturrock, D. T., and Evans,
G. W., editors, Proceedings of the 1999 Winter Simulation
Conference, pages 7–13.
Gillespie, D. T. (1977).
Exact stochastic simulation of coupled chemical reactions.
Journal of Physical Chemistry, 81(25):2340–2361.
Klugl, F. and Norling, E. (2006).
Agent-based simulation: Social science simulation and beyond.
Technical report, The Eighth European Agent Systems Summer
School (EASSS 2006), Annecy.

Bibliography II
Parisi, D. (2001).
Simulazioni - La realt`a rifatta al computer.
Societ`a editrice il Mulino.
Slepoy, A., Thompson, A. P., and Plimpton, S. J. (2008).
A constant-time kinetic Monte Carlo algorithm for simulation of large
biochemical reaction networks.
The Journal of Chemical Physics, 128(20):205101+.

SAPERE Self-aware Pervasive Service Ecosystems
From Engineer to Alchemist, There and Back Again: An
Alchemist Tale
Danilo Pianini – danilo.pianini@unibo.it
Alma Mater Studiorum—Universit`a di Bologna
Cesena, May 8th, 2012

From Engineer to Alchemist, There and Back Again: An Alchemist Tale

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