Universal laws and architecture: Foundations for Sustainable Infrastructure

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



dependabledeployablediscoverable distributabledurableeffectiveefficientevolvableextensiblefailure transparentfault-tolerantfidelityflexibleinspectableinstallableIntegrityinterchangeableinteroperable learnablemaintainable

manageablemobilemodifiablemodularnomadicoperableorthogonalityportableprecisionpredictableproducibleprovablerecoverablerelevantreliablerepeatablereproducibleresilientresponsivereusable robust

safety scalableseamlessself-sustainableserviceablesupportablesecurablesimplestablestandards

compliantsurvivablesustainabletailorabletestabletimelytraceableubiquitousunderstandableupgradableusable

Simplified, minimal requirements



dependabledeployablediscoverable distributabledurableeffective

evolvableextensiblefailure transparentfault-tolerantfidelityflexibleinspectableinstallableIntegrityinterchangeableinteroperable learnablemaintainable

manageablemobilemodifiablemodularnomadicoperableorthogonalityportableprecisionpredictableproducibleprovablerecoverablerelevantreliablerepeatablereproducibleresilientresponsivereusable

safety scalableseamlessself-sustainableserviceablesupportablesecurable

stablestandards

compliantsurvivable

tailorabletestabletimelytraceableubiquitousunderstandableupgradableusable


efficient

robust

simple

sustainable


efficient

robust

sustainable

simple


efficient

robust

simple

sustainablefragile

wasteful

complex

What we want

efficient

robust

simple

fragile

wasteful

complex

sustainable

efficient

robust

simple

fragile

wasteful

complex

What we get

Amory B. Lovins, Reinventing Fire

Very accessibleNo math

I’m interested in fire…

Accessible ecologyUG math

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

wastefulefficient

Today2050

Physics

Future evolution of the “smart” grid?

efficient

robust

simple

sustainablefragile

wasteful

complex

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


IEEE TRANS ON SYSTEMS, MAN, AND CYBERNETICS, JULY 2010, Alderson and Doyle


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

wasteful

fragile

robust

efficient

At best we get one

Technology?

wasteful

fragile

robust

efficient

Often neither

???

Bad theory?

???

?

?

Bad architectures?

wasteful

fragile

gap?

robust

efficient

.1%

1%

10%

100%

http://phe.rockefeller.edu/Daedalus/Elektron/

F Efficiency

Exponential improvement in efficiency F

log F

.1%

1%

10%

100%


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%


1FF

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…

2.5d space of systems and architectures

wasteful

fragile

efficient

robustsim

ple

complex

Case studies

wasteful

fragile

Sharpen hard bounds

Hard limit

Conservation “laws”?

Chandra, Buzi, and Doyle

UG biochem, math, control theory

Most important paper so far.

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 pz 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


?

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”

D. Alderson, NPS 53

Popular but wrong

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

uz x

yFlow

upflowhigh-speed

region

downflowlow speed

streak

Blunted turbulent velocity profile

Laminar

Turbulent

wU3D coupling

Coherent structures and turbulent drag

wasteful

fragile Laminar

Turbulent

efficient

robust

Laminar

Turbulent

wU

?

Control?

Fundamentals!

simple tech

complex tech

How general is this picture?

wasteful

fragile

efficient

robust

Implications for human evolution?Cognition?Technology?

TCPIP

Physical

MACSwitch

MAC MACPt to Pt Pt to Pt

Diverse applications

Layered architectures


Chang, Low, Calderbank, and Doyle

ORoptimization

TCPIP

Physical


Diverse

Too clever?

TCPIP

Deconstrained(Hardware)

Deconstrained(Applications)


ConstrainedNetworks

“constraints that deconstrain” (Gerhart and Kirschner)

Original design challenge?

TCP/IP



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

?????

natural selection + genetic drift + mutation + gene flow

++ architecture

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


+sticksstones

fire

From weak prey to invincible

predator

Before much brain expansion?

weak fragile

efficient(slow)

strongrobust


+sticksstones

fire


predator


Key point:Our physiology,

technology, and brains

have co-evolved

Huge implications.

weak fragile

efficient(slow)

strongrobust


+sticksstones

fire


predator


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.

Meta-layers

PhysiologyOrgans

Prediction GoalsActions

errorsActions

Corte

x

Fast,Limited scope

Slow,Broad scope

Which blue line is longer?

“Seeing is dreaming?”

“Seeing is believing?”

sense

move Spine

delay=death

sense

move Spine

Reflex

Reflect

sense

move Spine

Reflect

Reflex

Layered

Physiology

Organs

Neu

rons

Neu

rons

Neu

rons

Cor

tex

Cel

ls

Cor

tex

Cor

tex


Cells

sens

e

mov

eSp

ine

Refle

ct

Refle

x

Layered

Prediction

GoalsActions

errors

Actions

Physiology

Organs

Meta-layers

Prediction

GoalsActions

errors

ActionsCor

tex

3D+time

Simulation

Seeing is dreaming

Consciousperception

Consciousperception Zzzzzz…..

Same size?

Same size

Same size?

Vision: evolved for complex simulation and control, not

2d static pictures

3D+time

Simulation+ complex

models(“priors”)

Seeing is dreaming

Consciousperception


3D+time

Simulation+ complex


Seeing is dreaming

Consciousperception

Con

scio

uspe

rcep

tion

Prediction

errors

3D+time

Simulation+ complex


Seeing is dreaming

Consciousperception

Con

scio

uspe

rcep

tion

Prediction

errors

Seeing is believing

3D+time

Simulation+ complex


Seeing is dreaming

Consciousperception

Con

scio

uspe

rcep

tion

Prediction

errors

Seeing is believing

Our most integrated perception

of the world


With social

pressure, this one.

Standard social psychology experiment.

3D+time

Simulation+ complex


Seeing is dreaming

Consciousperception


3D+time

Simulation+ complex


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

energy

materials

complexity for robustness

efficiency

robustness

sourcereceiver

control

energymaterials

Physiology

Organs

Prediction

GoalsActions

errors

ActionsPrediction

Goals

Consciousperception

fast

fast

Meta-layers

PhysiologyOrgans


errorsActions

Corte

x

Fast,Limited scope

Slow,Broad scope

Unfortunately, we’re not sure how this all works.

Meta-layers

PhysiologyOrgans


errorsActions

Corte

x

Fast,Limited scope

Slow,Broad scope




Meta-layers

Phys iolo gy

Orga ns

Predic tion

Goa ls

Ac tio ns

erro rs

Ac tio ns

Cortex

Fast,Limited scope

Slow,Broad scope

UAV

Com

ms

Meta-layers

Phys iolo gy

Orga ns

Predic tion

Goa ls

Ac tio ns

erro rs

Ac tio ns

Cortex

Fast,Limited scope

Slow,Broad scope

Dis

turb

ance

Plant

RemoteSensor

SensorActuator

Interface

Control


Com

ms D

istu

rban

ce

Plant

RemoteSensor

SensorActuator

Interface

Control



Com

ms

Dis

turb

anc

e

Plant

RemoteSensor

SensorActuator

Interface Control


Meta-layers

PhysiologyOrgans


errorsActions

Corte

x

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

SensorActuator

Interface

Control

?



Next layered architectures

Constrained

Control, share, virtualize, and manage resources

Other examples

ClothingLego

MoneyCell biology

Bas

e

Insu

latio

n

She

ll

OutfitBody Environment

Shirt

Slacks

JacketTie T-Shirt

Socks

Shoes CoatShorts

Bas

e

Insu

latio

n

She

ll


Gar

men

t

Gar

men

t

Gar

men

t


• 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


Gar

men

t

Gar

men

t

Gar

men

t

Outfit

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

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

Control

Supp

lyComplexity?

Cloth

Thread

Fiber

Garments

Xform

Xform

XformAss

embl

y• Control << assembly• “weak linkage” (G&H)

Control

Flux of material

Cloth

Thread

Fiber

Garments

Xform

Xform

Xform

Ass

embl

y

• Control >> assembly

• FashionDesign >> construction

• Supply chainManagement

>> manufacture assemblytransport

Control

Complexity

Cloth

Thread

Fiber

Garments

Xform

Xform

Xform

Ass

embl

y

Control

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


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

tCloth

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


Cells

stack?

com

pose

?

Meta-layers

PhysiologyOrgans


errorsActions

Corte

x

Fast,Limited scope

Slow,Broad scope

complementary views of the brain

Sejnowski lab

Physiology

Organs

errorsConsciousperception

Outfit

Gar

men

t

Gar

men

t

Gar

men

t

Assembly

Cloth

Thread

Fiber

Garments

Xform

Xform

Xform Ass

embl

y

sourcereceiver

materials

Forward pathways

Prediction

GoalsActions

Prediction

GoalsConsciousperception

Choice=Mgmt/ctrl

Controlsourcereceiver

controlenergy

materials

Feedback>>Forward

Complexity

Physiology

Organs

Prediction

GoalsActions

errors

ActionsPrediction

GoalsConsciousperception

Outfit

Gar

men

t

Gar

men

t

Gar

men

t

Choice=Mgmt/ctrl

Assembly

Cloth

Thread

Fiber

Garments

Xform

Xform

Xform

Ass

embl

y

Controlsourcereceiver

controlenergy

materials

Feedback>>Forward

Complexity

Robust FragileHuman complexity

Metabolism Regeneration & repair Healing wound /infect

Obesity, diabetes Cancer AutoImmune/Inflame

Start with physiology

Lots of triage

RobustHuman complexity


Efficient Mobility Survive uncertain food supply Recover from moderate trauma

and infection

Robust FragileMechanism?


Fat accumulation Insulin resistance Proliferation Inflammation



Robust FragileWhat’s the difference?




ControlledDynamic

UncontrolledChronic

ControlledDynamicLow meanHigh variability


ControlledDynamic

UncontrolledChronic

Low meanHigh variability

High meanLow variability


Death

Robust FragileRestoring robustness?




ControlledDynamic

UncontrolledChronic

Low meanHigh variability

High meanLow variability


inefficientwasteful

weak fragile

efficient(slow)

strongrobust

Biology+Technology

++Technology

Robust Yet FragileHuman 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



• 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 Hard tradeoffs?

Architecture?

Constraints(that deconstrain)

wasteful

fragile

efficient

robustAmory B. Lovins, Reinventing Fire

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

Physical

MACSwitch

MAC MACPt to Pt Pt to Pt




Chang, Low, Calderbank, and Doyle

ORoptimization

TCPIP

Physical


Diverse

Too clever?

TCPIP




ConstrainedNetworks

“constraints that deconstrain” (Gerhart and Kirschner)


TCP/IP



Constrained • Expensive mainframes• Trusted end systems• Homogeneous• Sender centric• Unreliable comms



Networked OS

TCP/IP



Constrained



Why?

• OS better starting point than phone/comms systems

• Extreme robustness confers surprising evolvability

• Creative engineers• Rode hardware evolution

Networked OS

Architecture

OS




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


Cat

abol

ism

AA

Ribosome

RNARNAp

transl.Proteins

xRNAtransc.

Prec

urso

rs

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





ProcessingMemoryI/O


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



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





I/OCommsLatency?Storage?Processing?

Few global variables?

Don’t cross layers?


TCP/IP



Constrained • Expensive mainframes• Trusted end systems• Homogeneous• Sender centric• Unreliable hardware


• 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• …

?



Next layered architectures


CommsMemory, storageLatencyProcessingCyber-physical



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:

IEEE TRANS ON SYSTEMS, MAN, AND CYBERNETICS, JULY 2010, Alderson and Doyle

Csete and Doyle

Meta-layers

Physiology

Organs

Prediction

Goals

Actionserrors

Actions

Cortex

Fast,Limited scope

Slow,Broad scope






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



Robust

Complex systems?

• Resources• Controlled• Organized• Structured• Extreme• Architected• …

Robust complexity


New words

Fragile complexity

Emergulent

Emergulence at the edge of

chaocritiplexity


wU

z x

y

Blunted turbulent velocity profile

Laminar

Turbulent

wU

0u

1u u u p ut R

“turbulence is a highly nonlinear

phenomena”

0u

1u u u p ut R

Small Large

RobustSimple

2d, linearOrganizedComputer

Fragilechaocritical3d, nonlinear

Irreducibile?

Complexity?

mildly nonlinear

highly nonlinear

Model

wasteful

fragile Laminar

Turbulent

efficient

robust

Laminar

Turbulent

wU

?

Control?

Fundamentals!

Control Comms

Physics

Wiener

BodeKalman

Heisenberg

Carnot

Boltzmann

robust control

• Foundations, origins of– noise – dissipation– amplification– catalysis


Shannon

Universal laws and architecture: Foundations for Sustainable Infrastructure

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