Universal laws and architecture:Foundations for Sustainable Infrastructure
John DoyleControl and Dynamical Systems, EE, BioE
Caltech
Caltech smartgrid research
• Motivation: –uncertainty +–large scale
• Optimization and control– demand response– power flow (Javad Lavaei)
• PIs: Low, Chandy, Wierman,...
Current control
local
global
slow fast
relaysystem
SCADAEMS
• centralized• state estimation• contingency analysis• optimal power flow• simulation• human in loop • decentralized
• mechanical
mainly centralized, open-loop preventive, slow timescale
Our approach
local
global
relaysystem
SCADAEMS
endpoint basedscalable control
• local algorithms• global perspective
We have technologies to monitor/control 1000x fasternot the fundamental theories and algorithms
slow fast
scalable, decentralized, real-time feedback, sec-min timescale
Architectural transformation
Bell: telephone
1876
Tesla: multi-phase AC
1888 Both started as natural monopoliesBoth provided a single commodityBoth grew rapidly through two WWs 1980-90s
1980-90s
Deregulationstarted
Deregulationstarted
Power network will go through similararchitectural transformation in the next couple decades that phone network has gone through
?
1969:DARPAnet
Convergenceto Internet
2000s
Enron, blackouts
Architectural transformation... to become more interactive, more distributed, more open, more autonomous, and with greateruser participation
... while maintaining security & reliability
Caltech smartgrid research
• Motivation: –uncertainty +–large scale
• Optimization and control– demand response– power flow (Javad Lavaei)
• PIs: Low, Chandy, Wierman,...
Proceedings of the IEEE, Jan 2007
Chang, Low, Calderbank, Doyle
ORoptimization
What’s next?
Fundamentals!
A rant
“Universal laws and architectures?”
• Universal “conservation laws” (constraints)• Universal architectures (constraints that deconstrain)• Mention recent papers*• Focus on broader context not in papers• Lots of theorems• Lots of case studies
*try to get you to read them?
*
Systems
Fundamentals!
A rant
• Networking and “clean slate” architectures – wireless end systems– info or content centric application layer– integrate routing, control, scheduling, coding, caching – control of cyber-physical– PC, OS, VLSI, antennas, etc (IT components)
• Lots from cell biology– glycolytic oscillations for hard limits– bacterial layering for architecture
• Neuroscience• Smartgrid, cyber-phys• Wildfire ecology• Medical physiology• Earthquakes• Lots of aerospace• Physics: turbulence, stat mech (QM?)• “Toy”: Lego, clothing, buildings, …
Fundamentals!
my case studies
accessibleaccountableaccurateadaptableadministrableaffordableauditableautonomyavailablecredibleprocess
capablecompatiblecomposable configurablecorrectnesscustomizabledebugabledegradabledeterminabledemonstrable
dependabledeployablediscoverable distributabledurableeffectiveefficientevolvableextensiblefailure transparentfault-tolerantfidelityflexibleinspectableinstallableIntegrityinterchangeableinteroperable learnablemaintainable
manageablemobilemodifiablemodularnomadicoperableorthogonalityportableprecisionpredictableproducibleprovablerecoverablerelevantreliablerepeatablereproducibleresilientresponsivereusable robust
safety scalableseamlessself-sustainableserviceablesupportablesecurablesimplicitystablestandards
compliantsurvivablesustainabletailorabletestabletimelytraceableubiquitousunderstandableupgradableusable
Requirements on systems and architectures
accessibleaccountableaccurateadaptableadministrableaffordableauditableautonomyavailablecredibleprocess
capablecompatiblecomposable configurablecorrectnesscustomizabledebugabledegradabledeterminabledemonstrable
dependabledeployablediscoverable distributabledurableeffectiveefficientevolvableextensiblefailure transparentfault-tolerantfidelityflexibleinspectableinstallableIntegrityinterchangeableinteroperable learnablemaintainable
manageablemobilemodifiablemodularnomadicoperableorthogonalityportableprecisionpredictableproducibleprovablerecoverablerelevantreliablerepeatablereproducibleresilientresponsivereusable robust
safety scalableseamlessself-sustainableserviceablesupportablesecurablesimplestablestandards
compliantsurvivablesustainabletailorabletestabletimelytraceableubiquitousunderstandableupgradableusable
Simplified, minimal requirements
accessibleaccountableaccurateadaptableadministrableaffordableauditableautonomyavailablecredibleprocess
capablecompatiblecomposable configurablecorrectnesscustomizabledebugabledegradabledeterminabledemonstrable
dependabledeployablediscoverable distributabledurableeffective
evolvableextensiblefailure transparentfault-tolerantfidelityflexibleinspectableinstallableIntegrityinterchangeableinteroperable learnablemaintainable
manageablemobilemodifiablemodularnomadicoperableorthogonalityportableprecisionpredictableproducibleprovablerecoverablerelevantreliablerepeatablereproducibleresilientresponsivereusable
safety scalableseamlessself-sustainableserviceablesupportablesecurable
stablestandards
compliantsurvivable
tailorabletestabletimelytraceableubiquitousunderstandableupgradableusable
Requirements on systems and architectures
efficient
robust
simple
sustainable
Requirements on systems and architectures
efficient
robust
simple
sustainablefragile
wasteful
complex
Wildfire ecosystem as ideal example
• Cycles on years to decades timescale• Regime shifts: grass vs shrub vs tree• Fire= keystone “specie”
– Metabolism: consumes vegetation– Doesn’t (co-)evolve– Simplifies co-evolution spirals and metabolisms
• 4 ecosystems globally with convergent evo– So Cal, Australia, S Africa, E Mediterranean – Similar vegetation mix– Invasive species
This paper aims to bridge progress in neuroscience involving sophisticated quantitative analysis of behavior, including the use of robust control, with other relevant conceptual and theoretical frameworks from systems engineering, systems biology, and mathematics.
Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011
Very accessibleNo math
wasteful
fragile
efficient
robust
Want to understand the space of systems/architectures
Hard limits on robust efficiency?
Case studies?
Strategies?
Architectures? Want robust and efficient systems and architectures
.1%
1%
10%
100%
http://phe.rockefeller.edu/Daedalus/Elektron/
F Efficiency
Exponential improvement in efficiency F
log F
.1%
1%
10%
100%
http://phe.rockefeller.edu/Daedalus/Elektron/
F Efficiency
When will lamps be 200% efficient?
Exponential improvement
Note: this is real data!
Solving all energy
problems?
log F
.1%
1%
10%
50%
http://phe.rockefeller.edu/Daedalus/Elektron/
1
F
F
F Efficiency
Oops… never.
Note: need to plot it right.
When will lamps be 200% efficient?
Control, OR Comms
Compute Physics
ShannonBode
TuringGodel
EinsteinHeisenberg
Carnot
Boltzmann
Theory?Deep, but fragmented, incoherent, incomplete
Nash
Von Neumann
KalmanPontryagin
Control Comms
Compute Physics
ShannonBode
TuringGodel
Einstein
Heisenberg
Carnot
Boltzmann
wasteful?
fragile?
slow?
?
• Each theory one dimension• Tradeoffs across dimensions• Assume architectures a priori• Progress is encouraging, but…
K Nielsen, PG Sorensen, F Hynne, H-G Busse. Sustained oscillations in glycolysis: an experimental and theoretical study of chaotic and complex periodic behavior and of quenching of simple oscillations. Biophys Chem 72:49-62 (1998).
Experiments
CSTR, yeast extracts
Figure S4. Simulation of two state model (S7.1) qualitatively recapitulates experimental observation from CSTR studies [5] and [12]. As the flow of material in/out of the system is increased, the system enters a limit cycle and then stabilizes again. For this simulation, we take q=a=Vm=1, k=0.2, g=1, u=0.01, h=2.5.
0 20 40 60 80 100 120 140 160 180 2000
1
2
3
4
v=0.03
0 20 40 60 80 100 120 140 160 180 2000
0.5
1
1.5
2
v=0.1
0 20 40 60 80 100 120 140 160 180 2000.2
0.4
0.6
0.8
1
v=0.2
“Standard” Simulation
Figure S4. Simulation of two state model (S7.1) qualitatively recapitulates experimental observation from CSTR studies [5] and [12]. As the flow of material in/out of the system is increased, the system enters a limit cycle and then stabilizes again. For this simulation, we take q=a=Vm=1, k=0.2, g=1, u=0.01, h=2.5.
0 20 40 60 80 100 120 140 160 180 2000
1
2
3
4
v=0.03
0 20 40 60 80 100 120 140 160 180 2000
0.5
1
1.5
2
v=0.1
0 20 40 60 80 100 120 140 160 180 2000.2
0.4
0.6
0.8
1
v=0.2
SimulationExperimentsWhy?
Glycolytic “circuit” and oscillations
• Most studied, persistent mystery in cell dynamics
• End of an old story (why oscillations)– side effect of hard robustness/efficiency tradeoffs– no purpose per se– just needed a theorem
• Beginning of a new one – robustness/efficiency tradeoffs– complexity and architecture– need more theorems and applications
Fundamentals!
robust?
efficient? wasteful?
fragile?
Robust=maintain energy charge w/fluctuating cell demand Tradeoffs?
Hard limit?
x? y?
autocatalytic?a?
h?
g?control?
Rest?PK?
PFK?
rate k?
Efficient=minimize metabolic overhead
simple enzyme
Fragility
Metabolic Overhead
complex enzyme
lnz p
z p
2 20
1ln ln
z z pS j d
z z p
Theorem!
Fragilityhard limits
simple
Overhead, waste
complex
• General• Rigorous• First principle
• Domain specific• Ad hoc• Phenomenological
Plugging in domain details
?
Control Comms
Physics
Wiener
BodeKalman
Heisenberg
Carnot
Boltzmann
robust control
• Fundamental multiscale physics• Foundations, origins of
– noise – dissipation– amplification– catalysis
• General• Rigorous• First principle
?
Shannon
Stat physics
Complex networks
PhysicsHeisenberg
Carnot
Boltzmann
Control Comms
Compute
“New sciences” of complexity and networksedge of chaos, self-organized
criticality, scale-free,…
Wildly “successful”
doesn’t work
Stat physics
Complex networks
PhysicsHeisenberg
Carnot
Boltzmann
Control Comms
Compute
Alderson &Doyle, Contrasting Views of Complexity and
Their Implications for Network-Centric
Infrastructure,IEEE TRANS ON SMC,
JULY 2010
“New sciences” of complexity and networksedge of chaos, self-organized
criticality, scale-free,…
Stat physics
Complex networks
PhysicsHeisenberg
Carnot
Boltzmann
Control Comms
Compute
Alderson &Doyle, Contrasting Views of Complexity and Their Implications for
Network-Centric Infrastructure,IEEE TRANS ON SMC,
JULY 2010
Sandberg, Delvenne, & Doyle, On Lossless Approximations, the Fluctuation-Dissipation Theorem, and Limitations of Measurement,IEEE TRANS ON AC, FEBRUARY, 2011
Stat physics,
Complex networks
PhysicsHeisenberg
Carnot
Boltzmann
Control Comms
Compute
Sandberg, Delvenne, & Doyle, On Lossless Approximations, the Fluctuation-Dissipation Theorem, and Limitations of Measurement,IEEE TRANS ON AC, FEBRUARY, 2011
fluids, QM
“orthophysics”
From prediction to mechanism to control
Fundamentals!
J. Fluid Mech (2010)
Transition to Turbulence
FlowStreamlinedLaminar Flow
Turbulent Flow
Increasing Drag,
Fuel/Energy Use and
Cost
Turbulence and drag?
Physics of Fluids (2011)
wU
z x
y
uzx
yFlow
upflowhigh-speed
region
downflowlow speed
streak
Blunted turbulent velocity profile
Laminar
Turbulent
wU3D coupling
Coherent structures and turbulent drag
simple tech
complex tech
How general is this picture?
wasteful
fragile
efficient
robust
Implications for human evolution?Cognition?Technology?
TCPIP
Deconstrained(Hardware)
Deconstrained(Applications)
Layered architectures
ConstrainedNetworks
“constraints that deconstrain” (Gerhart and Kirschner)
Original design challenge?
TCP/IP
Deconstrained(Hardware)
Deconstrained(Applications)
Constrained • Expensive mainframes• Trusted end systems• Homogeneous• Sender centric• Unreliable comms
Facilitated wild evolutionCreated
• whole new ecosystem• completely opposite
Networked OS
Evolution and architecture
Nothing in biology makes sense except in the light of evolution
Theodosius Dobzhansky(see also de Chardin)
Nothing in evolution makes sense except in the light of biology
?????
weak fragileslow
strongrobustfast
Human evolution
ApesHow is this progress?
handsfeetskeletonmuscle skingutlong helpless childhood
All very different.
inefficientwasteful
weak fragile(slow)
efficient
strongrobust(fast)
Biology
Human evolution Apes
handsfeetskeletonmuscleskingut
inefficientwasteful
weak fragile
efficient(slow)
strongrobust
Biology
Hard tradeoffs?
Apes
Architecture?Evolvable?
inefficientwasteful
weak fragile
efficient(slow)
strongrobust
Biology+
sticksstones
fire +Technology
Architecture?
weak fragile
efficient(slow)
strongrobust
handsfeetskeletonmuscleskingut
+sticksstones
fire
From weak prey to invincible
predator
Before much brain expansion?
weak fragile
efficient(slow)
strongrobust
handsfeetskeletonmuscleskingut
+sticksstones
fire
From weak prey to invincible
predator
Before much brain expansion?
Key point:Our physiology,
technology, and brains
have co-evolved
Huge implications.
weak fragile
efficient(slow)
strongrobust
handsfeetskeletonmuscleskingut
+sticksstones
fire
From weak prey to invincible
predator
Before much brain expansion?
Key point needing more discussion:The evolutionary
challenge of big brains is homeostasis, not
basal metabolic load.
Huge implications.
Fundamentals!
inefficientwasteful
weak fragile
efficient(slow)
strongrobust
Biology+
sticksstones
fire +Technology
Architecture?
Biology
sticksstonesfire
+Technology
feetskeletonmuscleskinguthands
Human complexity?
wasteful
fragile
efficient
robust
Consequences of our evolutionary
history.
This paper aims to bridge progress in neuroscience involving sophisticated quantitative analysis of behavior, including the use of robust control, with other relevant conceptual and theoretical frameworks from systems engineering, systems biology, and mathematics.
Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011
Very accessibleNo math
Meta-layers
Physiology
Organs
Prediction GoalsActions
errorsActions
Cor
tex
Fast,Limited scope
Slow,Broad scope
Which blue line is longer?
“Seeing is dreaming?”
“Seeing is believing?”
Physiology
Organs
Neu
rons
Neu
rons
Neu
rons
Cor
tex
Cel
ls
Cor
tex
Cor
tex
Layered architectures
Cells
3D+time
Simulation+ complex
models(“priors”)
Seeing is dreaming
Consciousperception
Consciousperception
Zzzzzz…..
3D+time
Simulation+ complex
models(“priors”)
Seeing is dreaming
Consciousperception
Con
scio
uspe
rcep
tion
Prediction
errors
3D+time
Simulation+ complex
models(“priors”)
Seeing is dreaming
Consciousperception
Con
scio
uspe
rcep
tion
Prediction
errors
Seeing is believing
3D+time
Simulation+ complex
models(“priors”)
Seeing is dreaming
Consciousperception
Con
scio
uspe
rcep
tion
Prediction
errors
Seeing is believing
Our most integrated perception
of the world
3D+time
Simulation+ complex
models(“priors”)
Seeing is dreaming
Consciousperception
Consciousperception
Zzzzzz…..
3D+time
Simulation+ complex
models(“priors”)
Seeing is dreaming
Consciousperception
Con
scio
uspe
rcep
tion
Prediction
errors
Seeing is believing
Our most integrated perception of the world
Physiology
Organs
Prediction
GoalsActions
errors
Actions
Consciousperception
Prediction
Goals
Consciousperception
But ultimately, only actions matter.
sourcereceiver
signalinggene expression
metabolismlineage
Biological pathways
Recurring theme: research starts here, but then…
Primary function
sourcereceiver
control
energy
materials
signalinggene expression
metabolismlineage
More complex
feedback
sourcereceiver
control
energymaterials
Physiology
Organs
Prediction
GoalsActions
errors
Actions
Prediction
Goals
Consciousperception
fast
fast
Meta-layers
Physiology
Organs
Prediction GoalsActions
errorsActions
Cor
tex
Fast,Limited scope
Slow,Broad scope
Unfortunately, we’re not sure how this all works.
Meta-layers
Physiology
Organs
Prediction GoalsActions
errorsActions
Cor
tex
Fast,Limited scope
Slow,Broad scope
Which blue line is longer?
“Seeing is dreaming?”
“Seeing is believing?”
Meta-layers
Physiology
Organs
Prediction GoalsActions
errorsActions
Cor
tex
Fast,Limited scope
Slow,Broad scope
Meta-layers
Physiolog y
Organs
Pre
dic t io
n
Goal s
Actio
ns
errors
Actio
ns
Cortex
Fast,Limited scope
Slow,Broad scope
UAV
Com
ms
Meta-layers
Physiolog y
Organs
Pre
dic t io
n
Goal s
Actio
ns
errors
Actio
ns
Cortex
Fast,Limited scope
Slow,Broad scope
Dis
turb
ance
Plant
RemoteSensor
Sensor
Actuator
Interface
Control
Layered architectures
Layered architectures
Meta-layers
Physiology
Organs
Prediction GoalsActions
errorsActions
Cor
tex
Fast,Limited scope
Slow,Broad scope
Com
ms
Dis
turb
anc
e
Plant
RemoteSensor
SensorActuator
Interface Control
Com
ms D
istu
rban
ce
Plant
RemoteSensor
Sensor
Actuator
Interface
Control
?
Deconstrained(Hardware)
Deconstrained(Applications)
Next layered architectures
Constrained
Control, share, virtualize, and manage resources
This paper aims to bridge progress in neuroscience involving sophisticated quantitative analysis of behavior, including the use of robust control, with other relevant conceptual and theoretical frameworks from systems engineering, systems biology, and mathematics.
Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011
Very accessibleNo math
Bas
e
Insu
latio
n
She
ll
OutfitBody Environment
Shirt
Slacks
JacketTie T-Shirt
Socks
Shoes CoatShorts
Gar
men
t
Gar
men
t
Gar
men
t
OutfitBody Environment
• Complexity Robustness • Layers must be hidden to be robust• Choice (management and control) is more complex than assembly
Choice=Mgmt/ctrl
Assembly
Gar
men
t
Gar
men
t
Gar
men
t
Outfit
Cloth
Thread
Fiber
Garment
Gar
men
t
Cloth
Thread
Fiber
GarmentG
arm
ent
Cloth
Thread
Fiber
Garment
Layering within garments (textiles)
Cloth
Thread
Fiber
Garments
Weave
Sew
Spin
Universal strategies?
Prevents unraveling of lower layers
Cloth
Thread
Fiber
Garments
Xform
Xform
Xform
Universal strategies?
Garments have limited access to
threads and fibers
constraints on cross-layer interactions
quantization for robustness
Even though garments seem analog/continuous
Prevents unraveling of lower layers
Cloth
Thread
Fiber
Garments
Xform Ctrl Mgmt
Networked, universal,
layeredXform Ctrl Mgmt
Xform Ctrl Mgmt
Xform Ctrl Mgmt
Co
ntro
l
Su
pp
lyComplexity?
Cloth
Thread
Fiber
Garments
Xform
Xform
XformAss
emb
ly• Control << assembly• “weak linkage” (G&H)
Co
ntro
l
Flux of material
Cloth
Thread
Fiber
Garments
Xform
Xform
Xform
Ass
emb
ly
• Control >> assembly
• Fashion
Design >> construction
• Supply chain
Management >> manufacture
assemblytransport
Co
ntro
l
Complexity
Cloth
Thread
Fiber
Garments
Xform
Xform
Xform
Ass
emb
ly
Co
ntro
l
ComplexityControl >> assembly
Env
ironm
ent
Gar
men
t
Gar
men
t
Gar
men
t
Outfit
Choice=Mgmt/ctrl
Assembly
Body
Fiber
Geographically diverse sources
Diverse fabric
Functionally diverse garments
General purpose machines Diverse Thread
sew
knit, weave
spin
Gar
men
t
Gar
men
t
Gar
men
t
Cloth
Thread
Fiber
Garment
Gar
men
t
Cloth
Thread
Fiber
Garment
Gar
men
t
Cloth
Thread
Fiber
Garment
Outfit
Cloth
Thread
Fiber
Garment
complementary views
Outfit
Bas
e
Insu
latio
n
She
ll
OutfitBody Environment
Garments fit the standard view of “modules”• Strong internal connection• Weak external connectionOK, but not the most important
Gar
men
t
Gar
men
t
Gar
men
t
Cloth
Thread
Fiber
GarmentG
arm
ent
Cloth
Thread
Fiber
Garment
Gar
men
t
Cloth
Thread
Fiber
Garment
Outfit
This architectural view of modularity is more fundamental and has two different dimensions:1. inner to middle to outer garments2. fiber to thread to cloth to garment
Lack a taxonomy to describe these1. stack?2. compose?stack?
com
pose
?
Physiology
Organs
Neu
rons
Neu
rons
Neu
rons
Cor
tex
Cel
ls
Cor
tex
Cor
tex
Layered architectures
Cells
stack?
com
pose
?
Meta-layers
Physiology
Organs
Prediction GoalsActions
errorsActions
Cor
tex
Fast,Limited scope
Slow,Broad scope
complementary views of the brain
Sejnowski lab
Physiology
Organs
errors
Consciousperception
Outfit
Gar
men
t
Gar
men
t
Gar
men
t
Assembly
Cloth
Thread
Fiber
Garments
Xform
Xform
Xform
Ass
emb
ly
sourcereceiver
materials
Forward pathways
Prediction
GoalsActions
Prediction
GoalsConsciousperception
Choice=Mgmt/ctrl
Con
trolsourcereceiver
control
energymaterials
Feedback>>Forward
Complexity
Physiology
Organs
Prediction
GoalsActions
errors
Actions
Prediction
GoalsConsciousperception
Outfit
Gar
men
t
Gar
men
t
Gar
men
t
Choice=Mgmt/ctrl
Assembly
Cloth
Thread
Fiber
Garments
Xform
Xform
Xform
Ass
emb
ly
Con
trolsourcereceiver
control
energymaterials
Feedback>>Forward
Complexity
Robust Fragile
Human complexity
Metabolism Regeneration & repair Healing wound /infect
Obesity, diabetes Cancer AutoImmune/Inflame
Start with physiology
Lots of triage
Robust
Human complexity
Metabolism Regeneration & repair Healing wound /infect
Efficient Mobility Survive uncertain food supply Recover from moderate trauma
and infection
Robust Fragile
Mechanism?
Metabolism Regeneration & repair Healing wound /infect
Fat accumulation Insulin resistance Proliferation Inflammation
Obesity, diabetes Cancer AutoImmune/Inflame
Fat accumulation Insulin resistance Proliferation Inflammation
Robust Fragile
What’s the difference?
Metabolism Regeneration & repair Healing wound /infect
Obesity, diabetes Cancer AutoImmune/Inflame
Fat accumulation Insulin resistance Proliferation Inflammation
ControlledDynamic
UncontrolledChronic
ControlledDynamic
Low meanHigh variability
Fat accumulation Insulin resistance Proliferation Inflammation
ControlledDynamic
UncontrolledChronic
Low meanHigh variability
High meanLow variability
Fat accumulation Insulin resistance Proliferation Inflammation
Death
Robust Fragile
Restoring robustness?
Metabolism Regeneration & repair Healing wound /infect
Obesity, diabetes Cancer AutoImmune/Inflame
Fat accumulation Insulin resistance Proliferation Inflammation
ControlledDynamic
UncontrolledChronic
Low meanHigh variability
High meanLow variability
Fat accumulation Insulin resistance Proliferation Inflammation
Robust Yet Fragile
Human complexity
Metabolism Regeneration & repair Immune/inflammation Microbe symbionts Neuro-endocrine Complex societies Advanced technologies Risk “management”
Obesity, diabetes Cancer AutoImmune/Inflame Parasites, infection Addiction, psychosis,… Epidemics, war,… Disasters, global &!%$# Obfuscate, amplify,…
Accident or necessity?
Robust Fragile Metabolism Regeneration & repair Healing wound /infect
Obesity, diabetes Cancer AutoImmune/Inflame
Fat accumulation Insulin resistance Proliferation Inflammation
• Fragility Hijacking, side effects, unintended… • Of mechanisms evolved for robustness • Complexity control, robust/fragile tradeoffs• Math: robust/fragile constraints (“conservation laws”)
Accident or necessity?
Both Fundamentals!
wasteful
fragile
efficient
robust
Want to understand the space of systems/architectures
Want robust and efficient systems and architectures
Hard limits on robust efficiency?
Architectures?
TCPIP
Deconstrained(Hardware)
Deconstrained(Applications)
Layered architectures
ConstrainedNetworks
“constraints that deconstrain” (Gerhart and Kirschner)
Original design challenge?
TCP/IP
Deconstrained(Hardware)
Deconstrained(Applications)
Constrained • Expensive mainframes• Trusted end systems• Homogeneous• Sender centric• Unreliable comms
Facilitated wild evolutionCreated
• whole new ecosystem• completely opposite
Networked OS
TCP/IP
Deconstrained(Hardware)
Deconstrained(Applications)
Constrained
Facilitated wild evolutionCreated
• whole new ecosystem• completely opposite
Why?
• OS better starting point than phone/comms systems
• Extreme robustness confers surprising evolvability
• Creative engineers• Rode hardware evolution
Networked OS
Architecture
OS
Deconstrained(Hardware)
Deconstrained(Applications)
Layered architectures
Constrained Control, share, virtualize, and manage resources
ProcessingMemoryI/O
Few global variables
Don’t cross layers
Essentials
Cata
bolis
m
AA
Ribosome
RNA
RNAp
transl.Proteins
xRNA transc.
Prec
urso
rs
Nucl.
AA
DNA
DNAp
Repl. Gene
ATP
ATP
Enzymes
Building Blocks
Shared protocols
Deconstrained (diverse)
Environments
Deconstrained (diverse) Genomes
Bacterial biosphere
Architecture =
Constraints that
Deconstrain
Layered architectures
Cat
abol
ism
AA
Ribosome
RNARNAp
transl.Proteins
xRNAtransc.
Pre
curs
ors
Nucl.
AA
DNADNAp
Repl. Gene
ATP
ATP
Enzymes
Building Blocks
Crosslayer autocatalysis
Macro-layers
Inside every cellalmost
Cata
bolis
m
AA
Ribosome
RNA
RNAp
transl.Proteins
xRNA transc.
Prec
urso
rs
Nucl.
AA
DNA
DNAp
Repl. Gene
ATP
ATP
Enzymes
Building Blocks
Core conserved constraints facilitate
tradeoffs
Deconstrained phenotype
Deconstrained genome
What makes the bacterial biosphere so adaptable?
Active control of the genome (facilitated variation)
Environment
Action
Layered architecture
OS
Deconstrained(Hardware)
Deconstrained(Applications)
Layered architectures
Constrained Control, share, virtualize, and manage resources
ProcessingMemoryI/O
Few global variables
Don’t cross layersDirect
access to physical
memory?
Cat
abol
ism
AA
Ribosome
RNA
RNAp
transl.Proteins
xRNA transc.
Pre
curs
ors
Nucl.
AA
DNA
DNAp
Repl. Gene
ATP
ATP
Enzymes
Building Blocks
Shared protocols
Deconstrained (diverse)
Environments
Deconstrained (diverse) Genomes
Bacterial biosphere
Architecture =
Constraints that
Deconstrain
Few global variables
Don’t cross layers
Problems with leaky layering
Modularity benefits are lost• Global variables? @$%*&!^%@& • Poor portability of applications• Insecurity of physical address space• Fragile to application crashes• No scalability of virtual/real addressing
• Limits optimization/control by duality?
Fragilities of layering/virtualization
“Universal” fragilities that must be avoided• Hijacking, parasitism, predation
– Universals are vulnerable– Universals are valuable
• Cryptic, hidden – breakdowns/failures– unintended consequences
• Hyper-evolvable but with frozen core
TCP/IP
Deconstrained(Hardware)
Deconstrained(Applications)
Layered architectures
Constrained Control, share, virtualize, and manage resources
I/OCommsLatency?Storage?Processing?
Few global variables?
Don’t cross layers?
Original design challenge?
TCP/IP
Deconstrained(Hardware)
Deconstrained(Applications)
Constrained • Expensive mainframes• Trusted end systems• Homogeneous• Sender centric• Unreliable hardware
Facilitated wild evolutionCreated
• whole new ecosystem• complete opposite
Networked OS
CPU/Mem
Dev2CPU/
Mem
Dev CPU/
Mem
Dev2
Dev2
App AppIPC
Global and direct access to
physical address!
DNS
IP addresses interfaces
(not nodes)
caltech.edu?
131.215.9.49
CPU/Mem
Dev2CPU/
Mem
Dev CPU/
Mem
Dev2
Dev2
App AppIPC
Global and direct access to
physical address!
Robust?• Secure• Scalable• Verifiable• Evolvable• Maintainable• Designable• …
DNS
IP addresses interfaces
(not nodes)
Physical
IP
TCP
Application
Naming and addressing need to be • resolved within layer• translated between layers• not exposed outside of layer
Related “issues”• VPNs• NATS• Firewalls• Multihoming• Mobility• Routing table size• Overlays• …
?
Deconstrained(Hardware)
Deconstrained(Applications)
Next layered architectures
Constrained Control, share, virtualize, and manage resources
CommsMemory, storageLatencyProcessingCyber-physical
Few global variables
Don’t cross layers
Every layer has
different diverse graphs.
Architecture is least graph topology.
Architecture facilitates arbitrary graphs.
Persistent errors and confusion (“network science”)
Physical
IP
TCP
Application
Smart Antennas (Javad Lavaei w/ Ali Hajimiri)
Security, co-channel interference, power consumption
Conventional Antenna Smart Antenna
1) Multiple active elements: Easy to program Hard to implement
2) Multiple passive elements: Easy to implement Hard to program
Smart Antennas
Passively Controllable Smart (PCS) Antenna
PCS Antenna: One active element, reflectors and several parasitic elements
This type of antenna is easy to program and easy to implement but “hard” to solve (e.g. 4 weeks offline computation). We solved the problem in 1 sec with huge improvement:
This paper aims to bridge progress in neuroscience involving sophisticated quantitative analysis of behavior, including the use of robust control, with other relevant conceptual and theoretical frameworks from systems engineering, systems biology, and mathematics.
Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011
Meta-layers
Physiology
Organs
Prediction
Goals
Actionserrors
Actions
Cortex
Fast,Limited scope
Slow,Broad scope
Which blue line is longer?
“Seeing is dreaming?”
“Seeing is believing?”
Few global variables
Don’t cross layers
Stat physics
Complex networks
PhysicsHeisenberg
Carnot
Boltzmann
Control Comms
Compute
Complex systems?
Complex systems?
Fragile
• Scale• Dynamics• Nonlinearity• Nonequlibrium• Open• Feedback• Adaptation• Intractability• Emergence• …
Even small amounts can create bewildering complexity
Complex systems?
Fragile
• Scale• Dynamics• Nonlinearity• Nonequlibrium• Open• Feedback• Adaptation• Intractability• Emergence• …
• Scale• Dynamics• Nonlinearity• Nonequlibrium• Open• Feedback• Adaptation• Intractability• Emergence• …
Robust
Complex systems?
• Resources• Controlled• Organized• Structured• Extreme• Architected• …
Robust complexity
• Scale• Dynamics• Nonlinearity• Nonequlibrium• Open• Feedback• Adaptation• Intractability• Emergence• …
New words
Fragile complexity
Emergulent
Emergulence at the edge of
chaocritiplexity
• Scale• Dynamics• Nonlinearity• Nonequlibrium• Open• Feedback• Adaptation• Intractability• Emergence• …
wU
z x
y
Blunted turbulent velocity profile
Laminar
Turbulent
wU
0u
1uu u p u
t R
“turbulence is a highly nonlinear
phenomena”
0u
1uu u p u
t R
Small Large
RobustSimple
2d, linearOrganizedComputer
Fragilechaocritical3d, nonlinear
Irreducibile?
Complexity?
mildly nonlinear
highly nonlinear
Model