Engineering computational ecosystems (2nd year PhD seminar)

2nd year Ph.D. Seminar
Engineering Computational Ecosystems
Danilo Pianini – danilo.pianini@unibo.it
Supervisor: prof. Mirko Viroli
Alma Mater Studiorum—Universit`a di Bologna
June 7, 2013
Danilo Pianini (UniBo) Computational Ecosystems June 7, 2013 1 / 78

Outline Every living creature on this earth dies alone.
1 Pervasive Computing
(Near) Future city scenario
(Near) Future devices
Pervasive displays
Case studies
2 Pervasive Ecosystems
Cloud or pervasive?
Self-organising spatial data structures
3 Engineering Pervasive Ecosystems
Anticipative adaptation
The SAPERE EU Project
Alchemist
Simulations and demos
4 Open issues
Programming the emergence
Real-world setup

Pervasive Computing
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

Pervasive Computing (Near) Future city scenario
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

Shibuya Crossing

People

Pervasive Computing (Near) Future devices
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

Ready-to-use devices
Images courtesy of Alois Ferscha (Pervasive Computing Group, Johannes Kepler Universit¨at Linz)

Almost ready devices
Images courtesy of Alois Ferscha (Pervasive Computing Group, Johannes Kepler Universit¨at Linz)

Maybe in few decades...

Pervasive Computing Pervasive displays
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

More and more pervasive inside
Image courtesy of Alois Ferscha (Pervasive Computing Group, Johannes Kepler Universit¨at Linz)
→

More and more pervasive outside
. . . . . . .

Pervasive Computing Case studies
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

Pervasive displays: multiple view

Pervasive displays: local sharing

Pervasive displays: steering

Pervasive displays: crowd steering

Some interesting services and features
Display must adapt to people behaviour
Displays show information based on majority of people around
Alerts are shown as a given person passes nearby
Content redirected from smartphone to display
Adjacent displays show a common, bigger content
Displays coordinate to avoid irritating users
Displays coordinate to provide visualisation streams
Public displays sharedly used by friends to interact
Using eye-glasses for immersed interaction
People may change their behaviour because of that
Single User Steering via Public Displays
Crowd Balancing
Evacuation
Adaptive Advertisement
Collective Guidance

Pervasive Ecosystems
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

Pervasive Ecosystems Cloud or pervasive?
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

A standard, centralised SOA solution – cloud oriented

A standard, centralised SOA solution
A centralised solution
One service (typically in the cloud) for:
Discovery: what components are available in the system?
Context: where are components? (behaviour specialisation)
Orchestration: coordinating components
Data storage: depositing/retrieving data
Adaptation: reacting to contingencies
All components interact through such middleware services
Does it scale well?
Can displays promptly react?
Can we store streams of sensor data in the cloud?
What about privacy?

Eco-inspired SOA solution

Eco-inspired SOA solution
Fully decentralised middleware services
Locations become very small and form a huge dynamic set
Contextualisation, discovery, and orchestration almost vanish
Middleware service just as a single space distributed on all nodes
In overall we have a network of spaces with service “tags”
Adaptation is achieved by simple rules combining tags
Drawing a bridge with natural ecosystems
We have a set of spatially situated entities interacting according to
well-deﬁned set of natural laws enforced by the spatial environment in
which they situate, and adaptively self-organizing their interaction
dynamics according to their shape and structure
[Zambonelli and Viroli, 2011]

Towards a computational continuum
A pervasive (eco)system:
is composed of myriads of devices (nodes)
contains devices located in space and time
has a notion of “node density”
As such, it is similar to particle systems, and the higher the density, the
more this system approximates a spatio-temporal computational
continuum.

Pervasive Ecosystems Self-organising spatial data structures
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

Computational fields
A way to engineer self-organising applications in different scenarios
Sensor networks (aggregating/diffusing data)
[Beal and Bachrach, 2006]
Swarm and modular robotics (cooperative team work)
[Bachrach et al., 2010]
Pervasive computing (crowd steering, coordinated visualisation)
[Viroli et al., 2011]
Rough idea
Two simple rules for creating a spatial structure:
1 Diffuse spatial enriched information among neighbours
2 Aggregate the information received by neighbours considering the
spatial information

A 3D rendering of a uniform gradient structure

Towards a library of gradient-based computational ﬁelds
Gradient (discrete, ﬂat continuous, structured continuous)

Towards a library of gradient-based computational ﬁelds
Gradient (discrete, ﬂat continuous, structured continuous)
Other structures (channel, shrinking crown, sector, partition, shadow, . . . )

Creating optimal (multi-)paths
Enable devices used to steering from a source to a target
Open issue: ﬁelds react to current event, but they do not provide
anticipative adaptation.
If obstacles are dynamically added and moved
If there is knowledge about WHEN those events will happen
How to improve?

Engineering Pervasive Ecosystems
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

Engineering Pervasive Ecosystems Anticipative adaptation
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

When spatial gradient is not enough

Anticipative gradient [Montagna et al., 2012a]
Anticipative gradient merges three strategies to provide anticipative
adaptation: in case a future event is detected
In case a future event is on the optimal path, also store paths not
passing through it
If you are in an area in which, if you followed the gradient, you would
have to wait
Either circumvent (green) or proceed and wait (red)

Engineering Pervasive Ecosystems The SAPERE EU Project
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

The SAPERE Project
http://www.sapere-project.eu

Eco-laws and Live Semantic Annotations
Live Semantic Annotations (LSA)
A unified description for devices, data, services
Is about interface, status, and behaviour of a component
It provides semantic information, and it is dynamic
Eco-Laws
They resemble chemical reactions
They take some reagent LSA, and provide some product LSA
They can diffuse an LSA in the neighborhood
They can aggregate LSAs like in chemical bonding
They form a small & fixed set of natural eco-laws

Pervasive Ecosystems
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

Engineering Pervasive Ecosystems Alchemist
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

Computational ecosystems: functional guarantees
Houston, we’ve got a problem.
Prediction diﬃculties
Thousands-devices scale system
Autonomous devices
Interaction plays a huge role
Formal proof
Mathematical guarantee
Only tackles simple cases
Model checking
Space state explosion for non-trivial cases

Computational ecosystems: simulation
Open issue! How to simulate a (bio)chemical inspired computational
ecosystem?
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 simulation approach [Pianini et al., 2013]
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

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

Existing algorithms
Next Reaction [Gibson and Bruck, 2000]
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

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

Computational ecosystems: Alchemist

Some Features in short
Kinetic Monte Carlo engine
Parallelised engine
Mobility support
Dynamic connectivity support
Complex data items
Reaction-like programming
Parallel executor
Built-in approximate stochastic model checker
Alchemist2Blender
PVeStA integration
Built from scratch

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)

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

Engineering Pervasive Ecosystems Simulations and demos
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

Crowd evacuation

Crowd steering

Crowd steering with anticipative adaptation

Realistic pedestrians

Morphogenesis

Morphogenesis [Montagna et al., 2012b]

Open issues
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

Open issues Programming the emergence
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

Abstraction level
We can not (yet) program the emergence.
Chemical-inspired rules are powerful, but hard to control
Interactions among zilions of possibly mobile nodes may expose
unpredicted situations
We would like to express a spatial pattern, and automatically get the
single node program

Simulator improvement
Functional guarantees prior-to-deployment: large scale realistic simulations
Real life data (e.g. from Wien marathon)
Realistic environment (e.g. from Google Maps)
Realistic human behaviour (already partially there)

Open issues Real-world setup
Outline
Pervasive displays
Case studies
Cloud or pervasive?
Alchemist
4 Open issues
Real-world setup

Communication
P2P Communication issues
Bluetooth: tight bandwidth, slow device detection
ZigBee: sensor oriented, not enough diffused
WiFi: difference between master mode and “normal” mode
Near Field Communication: too Near!
Wifi direct: we need to deepen
What do you suggest?

Localisation and orientation
Outdoor
GPS: very good
Computer vision: good if the seeing node knows where it is
Latitude API: Android only, mixes 3G, WiFi, GPS and onboard
magnetometer. Very good
Indoor
GPS: useless within buildings
Computer vision: good if the seeing node knows where it is
Latitude API: Inaccurate, but promising
Signal power (Bluetooth, WiFi): Almost useless
Ultra Wide Band: extremely promising, but requires speciﬁc setup
Resonant coupling [Pirkl and Lukowicz, 2012]: promising!
Ideas?

Other issues
Identification
Relation between devices and user
Device identification: addresses, ids
User identification: computer vision, explicit login
Privacy
Security

Bibliography I
Bachrach, J., Beal, J., and McLurkin, J. (2010).
Composable continuous-space programs for robotic swarms.
Neural Computing and Applications, 19(6).
Beal, J. and Bachrach, J. (2006).
Infrastructure for engineered emergence on sensor/actuator networks.
IEEE Intelligent Systems, 21(2):10–19.
Gibson, M. A. and Bruck, J. (2000).
Eﬃcient Exact Stochastic Simulation of Chemical Systems with Many
Species and Many Channels.
The Journal of Physical Chemistry A, 104(9):1876–1889.
Gillespie, D. T. (1977).
Exact stochastic simulation of coupled chemical reactions.
Journal of Physical Chemistry, 81(25):2340–2361.

Bibliography II
Montagna, S., Pianini, D., and Viroli, M. (2012a).
Gradient-based self-organisation patterns of anticipative adaptation.
In Proceedings of 6th IEEE International Conference on Self-Adaptive
and Self-Organizing Systems (SASO 2012), pages 169–174.
Montagna, S., Pianini, D., and Viroli, M. (2012b).
A model for drosophila melanogaster development from a single cell to
stripe pattern formation.
In Shin, D., Hung, C.-C., and Hong, J., editors, Proceedings of the
27th Annual ACM Symposium on Applied Computing (SAC 2012),
pages 1406–1412, Riva del Garda (Trento), Italy. ACM.
Pianini, D., Montagna, S., and Viroli, M. (2013).
Chemical-oriented simulation of computational systems with
Alchemist.
Journal of Simulation.

Bibliography III
Pirkl, G. and Lukowicz, P. (2012).
Robust, low cost indoor positioning using magnetic resonant coupling.
In UbiComp, pages 431–440.
Viroli, M., Casadei, M., Montagna, S., and Zambonelli, F. (2011).
Spatial coordination of pervasive services through chemical-inspired
tuple spaces.
ACM Transactions on Autonomous and Adaptive Systems, 6(2):14:1 –
14:24.
Zambonelli, F. and Viroli, M. (2011).
A survey on nature-inspired metaphors for pervasive service
ecosystems.
International Journal of Pervasive Computing and Communications,
7(3):186–204.

SAPERE Self-aware Pervasive Service Ecosystems
Engineering Computational Ecosystems
Danilo Pianini – danilo.pianini@unibo.it
Supervisor: prof. Mirko Viroli
Alma Mater Studiorum—Universit`a di Bologna
June 7, 2013

Engineering computational ecosystems (2nd year PhD seminar)

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