John Doyle Control and Dynamical Systems, EE, BioE Caltech Universal laws and architecture: Foundations for Sustainable Infrastructure John Doyle Control 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 SCADA EMS relay system global centralized state estimation contingency analysis optimal power flow simulation human in loop decentralized mechanical relay system local slow fast mainly centralized, open-loop preventive, slow timescale

scalable, decentralized, real-time feedback, sec-min timescale Our approach SCADA EMS endpoint based scalable control local algorithms global perspective global relay system local slow fast We have technologies to monitor/control 1000x faster not the fundamental theories and algorithms

Architectural transformation Power network will go through similar architectural transformation in the next couple decades that phone network has gone through ? Deregulation started Tesla: multi-phase AC Enron, blackouts 1888 Both started as natural monopolies Both provided a single commodity Both grew rapidly through two WWs 1980-90s 2000s 1876 1980-90s Bell: telephone Deregulation started 1969: DARPAnet Convergence to Internet

Architectural transformation ... to become more interactive, more distributed, more open, more autonomous, and with greater user 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 OR optimization What’s next? Fundamentals! A rant Chang, Low, Calderbank, Doyle

“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 * Fundamentals! A rant *try to get you to read them? Systems

my case studies 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

sustainable Requirements on systems and architectures accessible accountable accurate adaptable administrable affordable auditable autonomy available credible process capable compatible composable configurable correctness customizable debugable degradable determinable demonstrable dependable deployable discoverable distributable durable effective efficient evolvable extensible failure transparent fault-tolerant fidelity flexible inspectable installable Integrity interchangeable interoperable learnable maintainable manageable mobile modifiable modular nomadic operable orthogonality portable precision predictable producible provable recoverable relevant reliable repeatable reproducible resilient responsive reusable robust safety scalable seamless self-sustainable serviceable supportable securable simplicity stable standards compliant survivable sustainable tailorable testable timely traceable ubiquitous understandable upgradable usable

Simplified, minimal requirements accessible accountable accurate adaptable administrable affordable auditable autonomy available credible process capable compatible composable configurable correctness customizable debugable degradable determinable demonstrable dependable deployable discoverable distributable durable effective efficient evolvable extensible failure transparent fault-tolerant fidelity flexible inspectable installable Integrity interchangeable interoperable learnable maintainable manageable mobile modifiable modular nomadic operable orthogonality portable precision predictable producible provable recoverable relevant reliable repeatable reproducible resilient responsive reusable robust safety scalable seamless self-sustainable serviceable supportable securable simple stable standards compliant survivable sustainable tailorable testable timely traceable ubiquitous understandable upgradable usable

efficient simple sustainable robust Requirements on systems and architectures accessible accountable accurate adaptable administrable affordable auditable autonomy available credible process capable compatible composable configurable correctness customizable debugable degradable determinable demonstrable dependable deployable discoverable distributable durable effective evolvable extensible failure transparent fault-tolerant fidelity flexible inspectable installable Integrity interchangeable interoperable learnable maintainable manageable mobile modifiable modular nomadic operable orthogonality portable precision predictable producible provable recoverable relevant reliable repeatable reproducible resilient responsive reusable safety scalable seamless self-sustainable serviceable supportable securable stable standards compliant survivable tailorable testable timely traceable ubiquitous understandable upgradable usable efficient simple sustainable robust

efficient simple sustainable robust Requirements on systems and architectures efficient simple sustainable robust

efficient simple sustainable robust Requirements on systems and architectures efficient simple sustainable robust

sustainable fragile robust simple efficient wasteful complex Requirements on systems and architectures sustainable fragile robust simple efficient wasteful complex

sustainable fragile robust simple efficient wasteful complex What we want sustainable fragile robust simple efficient wasteful complex

What we get fragile robust simple efficient wasteful complex

What we get fragile robust simple efficient wasteful complex

Hard limits fragileY Bad Optimal Impossible robustY robustX fragileX Cannot be simultaneously robust in all ways to all perturbations.

Sometimes? fragileY Bad Optimal robustY robustZ robustX fragileX fragileZ

Oops? fragileY Bad Optimal robustY robustZ robustX fragileX fragileZ

sustainable fragile robust simple efficient wasteful complex Requirements on systems and architectures sustainable fragile robust simple efficient wasteful complex

Amory B. Lovins, Reinventing Fire

I’m interested in fire… Very accessible No math

Accessible ecology UG 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

2050 Today efficient wasteful Physics

sustainable fragile robust simple efficient wasteful complex Future evolution of the “smart” grid? sustainable fragile robust simple efficient wasteful complex

Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011 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. Very accessible No math Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011

IEEE TRANS ON SYSTEMS, MAN, AND CYBERNETICS, JULY 2010, Alderson and Doyle Very accessible No math

Want to understand the space of systems/architectures Case studies? fragile Strategies? Hard limits on robust efficiency? Architectures? Want robust and efficient systems and architectures robust efficient wasteful

Technology? fragile At best we get one robust efficient wasteful

??? fragile Often neither robust efficient wasteful

??? ? ? Bad architectures? gap? Bad theory? fragile robust efficient wasteful

Exponential improvement in efficiency F 100% 10% 1% .1% http://phe.rockefeller.edu/Daedalus/Elektron/

When will lamps be 200% efficient? Solving all energy problems? When will lamps be 200% efficient? 100% Exponential improvement 10% 1% .1% Note: this is real data! http://phe.rockefeller.edu/Daedalus/Elektron/

When will lamps be 200% efficient? Oops… never. 50% 10% 1% .1% Note: need to plot it right. http://phe.rockefeller.edu/Daedalus/Elektron/

Deep, but fragmented, incoherent, incomplete Control, OR Comms Kalman Pontryagin Shannon Bode Nash Theory? Deep, but fragmented, incoherent, incomplete Von Neumann Carnot Boltzmann Turing Godel Heisenberg Compute Einstein Physics

? fragile? wasteful? Control Comms Each theory  one dimension Shannon Bode Each theory  one dimension Tradeoffs across dimensions Assume architectures a priori Progress is encouraging, but… fragile? slow? ? wasteful? Carnot Boltzmann Turing Godel Heisenberg Compute Physics Einstein

complex simple 2.5d space of systems and architectures fragile robust wasteful efficient

Conservation “laws”? fragile Case studies Sharpen hard bounds Hard limit Case studies wasteful

UG biochem, math, control theory Chandra, Buzi, and Doyle Most important paper so far.

CSTR, yeast extracts Experiments 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).

“Standard” Simulation 20 40 60 80 100 120 140 160 180 200 1 2 3 4 v=0.03 0.5 1.5 v=0.1 0.2 0.4 0.6 0.8 v=0.2 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.

Why? Experiments Simulation 20 40 60 80 100 120 140 160 180 200 1 2 3 4 v=0.03 0.5 1.5 v=0.1 0.2 0.4 0.6 0.8 v=0.2 Experiments Simulation Why? 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.

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!

x? y? autocatalytic? a? fragile? h? g? Robust PFK? fragile? Tradeoffs? Hard limit? h? x? control? y? g? Robust =maintain energy charge w/fluctuating cell demand PK? Rest? rate k? robust? efficient? wasteful? Efficient=minimize metabolic overhead

Theorem! Fragility simple enzyme complex enzyme Metabolic Overhead

? Fragility hard limits General Rigorous First principle simple complex ? Overhead, waste Domain specific Ad hoc Phenomenological Plugging in domain details

? Control Comms Fundamental multiscale physics Foundations, origins of Wiener Comms Bode Shannon robust control Kalman Fundamental multiscale physics Foundations, origins of noise dissipation amplification catalysis General Rigorous First principle ? Carnot Boltzmann Heisenberg Physics

“New sciences” of complexity and networks Control Comms Wildly “successful” Complex networks Compute “New sciences” of complexity and networks edge of chaos, self-organized criticality, scale-free,… Stat physics Carnot Boltzmann Heisenberg Physics

Popular but wrong D. Alderson, NPS

“New sciences” of complexity and networks Alderson &Doyle, Contrasting Views of Complexity and Their Implications for Network-Centric Infrastructure, IEEE TRANS ON SMC, JULY 2010 Control Comms Complex networks Compute doesn’t work Stat physics “New sciences” of complexity and networks edge of chaos, self-organized criticality, scale-free,… Carnot Boltzmann Heisenberg Physics

Control Comms Complex networks Compute Stat physics Physics Alderson &Doyle, Contrasting Views of Complexity and Their Implications for Network-Centric Infrastructure, IEEE TRANS ON SMC, JULY 2010 Control Comms Complex networks Compute Sandberg, Delvenne, & Doyle, On Lossless Approximations, the Fluctuation-Dissipation Theorem, and Limitations of Measurement, IEEE TRANS ON AC, FEBRUARY, 2011 Stat physics Carnot Boltzmann Heisenberg Physics

Control Comms Complex networks Compute “orthophysics” Stat physics, From prediction to mechanism to control Complex networks Fundamentals! Compute “orthophysics” Sandberg, Delvenne, & Doyle, On Lossless Approximations, the Fluctuation-Dissipation Theorem, and Limitations of Measurement, IEEE TRANS ON AC, FEBRUARY, 2011 Stat physics, fluids, QM Carnot Boltzmann Heisenberg Physics

Turbulence and drag? J. Fluid Mech (2010) Flow Streamlined Laminar Flow Transition to Turbulence Increasing Drag, Fuel/Energy Use and Cost Turbulent Flow

Coherent structures and turbulent drag Physics of Fluids (2011) y y Flow z x z x Coherent structures and turbulent drag high-speed region 3D coupling upflow Blunted turbulent velocity profile low speed streak downflow Turbulent Laminar

? Laminar Control? Turbulent Turbulent Laminar fragile robust Fundamentals! Turbulent Laminar fragile Laminar Control? Turbulent ? robust efficient wasteful

How general is this picture? Implications for human evolution? Cognition? Technology? fragile simple tech robust complex tech efficient wasteful

Layered architectures Diverse applications TCP IP MAC MAC MAC Switch Pt to Pt Pt to Pt Physical

Proceedings of the IEEE, Jan 2007 OR optimization Chang, Low, Calderbank, and Doyle

Too clever? Diverse applications TCP IP Diverse Physical

Layered architectures Deconstrained (Applications) TCP Constrained Networks IP Deconstrained (Hardware) “constraints that deconstrain” (Gerhart and Kirschner)

Original design challenge? TCP/ IP Deconstrained (Hardware) (Applications) Constrained Networked OS Expensive mainframes Trusted end systems Homogeneous Sender centric Unreliable comms Facilitated wild evolution Created whole new ecosystem completely opposite

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

Human evolution Apes hands feet skeleton muscle All very different. skin gut long helpless childhood All very different. weak fragile slow Human evolution Apes strong robust fast How is this progress?

Human evolution Apes Biology hands feet skeleton muscle skin gut weak fragile (slow) Human evolution Apes Biology strong robust (fast) efficient inefficient wasteful

Apes Biology Architecture? Hard tradeoffs? weak fragile Evolvable? strong robust efficient (slow) inefficient wasteful

+ Biology Architecture? weak fragile sticks stones fire strong robust +Technology efficient (slow) inefficient wasteful

+ hands feet skeleton muscle weak fragile skin gut From weak prey to invincible predator + sticks stones fire strong robust Before much brain expansion? efficient (slow)

Key point: Our physiology, technology, and brains have co-evolved hands feet skeleton muscle skin gut weak fragile From weak prey to invincible predator + sticks stones fire strong robust Before much brain expansion? Huge implications. efficient (slow)

+ Key point needing more discussion: hands feet The evolutionary challenge of big brains is homeostasis, not basal metabolic load. hands feet skeleton muscle skin gut weak fragile From weak prey to invincible predator + sticks stones fire Fundamentals! strong robust Before much brain expansion? Huge implications. efficient (slow)

+ Biology Architecture? weak fragile sticks stones fire strong robust +Technology efficient (slow) inefficient wasteful

Human complexity? Consequences of our evolutionary history. feet fragile feet skeleton muscle skin gut hands sticks stones fire Biology robust +Technology wasteful efficient

Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011 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. Very accessible No math Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011

“Seeing is dreaming?” “Seeing is believing?” Cortex Actions Which blue line is longer? Meta-layers Physiology Organs Prediction Goals Actions errors Cortex Fast, Limited scope Slow, Broad scope .

delay=death sense Spine move

Reflect Reflex sense Spine move

Reflect Reflex sense Spine move

Reflect Layered Reflex sense Spine move

sense move Spine Reflect Reflex Layered

Layered architectures Physiology Organs Cortex Cortex Cortex . Cells Neurons Neurons Neurons Cells

Actions Layered Spine Reflex move sense Goals Actions errors Reflect Prediction Goals Actions Actions errors

Cortex Actions Goals Actions errors Meta-layers Physiology Organs Prediction Goals Actions Physiology Organs Cortex Actions . errors

3D Seeing is dreaming Zzzzzz….. +time Simulation Conscious perception

Same size?

Same size

Same size

Same size? Vision: evolved for complex simulation and control, not 2d static pictures

3D Seeing is dreaming Zzzzzz….. Conscious perception +time Simulation + complex models (“priors”) Zzzzzz….. Conscious perception . Conscious perception

3D Prediction Seeing is dreaming errors Conscious perception +time Simulation + complex models (“priors”) Prediction Conscious perception . Conscious perception errors

3D Prediction Seeing is dreaming Seeing is believing errors Conscious +time Simulation + complex models (“priors”) Seeing is believing Prediction Conscious perception . Conscious perception errors

Our most integrated perception of the world Seeing is dreaming Our most integrated perception of the world 3D +time Simulation + complex models (“priors”) Seeing is believing Prediction Conscious perception . Conscious perception errors

Which blue line is longer?

Which blue line is longer?

Which blue line is longer?

Which blue line is longer?

Which blue line is longer?

Which blue line is longer?

With social pressure, this one. Which blue line is longer? With social pressure, this one. Standard social psychology experiment.

3D Seeing is dreaming Zzzzzz….. Conscious perception +time Simulation + complex models (“priors”) Zzzzzz….. Conscious perception . Conscious perception

Our most integrated perception of the world Seeing is dreaming 3D +time Simulation + complex models (“priors”) Seeing is believing Prediction Our most integrated perception of the world Conscious perception . Conscious perception errors

Actions Actions Goals Goals But ultimately, only actions matter. Prediction Goals Actions Physiology Organs Actions Conscious perception Prediction Goals . Conscious perception errors

Recurring theme: research starts here, but then… Biological pathways signaling gene expression metabolism lineage source receiver Primary function Recurring theme: research starts here, but then…

signaling gene expression metabolism lineage control energy receiver source control energy More complex feedback materials

complexity for robustness receiver source robustness control efficiency energy materials

energy materials Actions Goals fast Actions Goals fast errors control Physiology Organs Prediction Goals Actions fast Prediction Goals Actions Conscious perception source receiver control energy materials fast

Cortex Actions Goals errors Slow, Broad scope Meta-layers Physiology Organs Prediction Goals Actions errors Cortex Fast, Limited scope . Unfortunately, we’re not sure how this all works.

“Seeing is dreaming?” “Seeing is believing?” Cortex Actions Which blue line is longer? Meta-layers Physiology Organs Prediction Goals Actions errors Cortex Fast, Limited scope Slow, Broad scope .

Cortex Actions Goals errors Slow, Broad scope Meta-layers Physiology Organs Prediction Goals Actions errors Cortex Fast, Limited scope Slow, Broad scope .

UAV Cortex Actions Goals errors Slow, Broad scope Meta-layers Fast, Physiology Organs Actions Prediction Goals errors Cortex Fast, Limited scope Slow, Broad scope UAV .

Layered architectures Meta-layers Physiology Organs Actions Prediction Goals errors Cortex Fast, Limited scope Slow, Broad scope Interface Comms Disturbance Remote Sensor Control Plant Sensor Actuator

Layered architectures Interface Comms Disturbance Remote Sensor Control Plant Sensor Actuator

Layered architectures Comms Disturbance Plant Remote Sensor Actuator Interface Control

Layered architectures Meta-layers Physiology Organs Prediction Goals Actions errors Cortex Fast, Limited scope Slow, Broad scope Comms Disturbance Plant Remote Sensor Actuator Interface Control

Next layered architectures Deconstrained (Applications) Interface Control, share, virtualize, and manage resources Comms Disturbance Constrained Remote Sensor ? Control Plant Sensor Deconstrained (Hardware) Actuator

Clothing Lego Money Cell biology Other examples Clothing Lego Money Cell biology

Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011 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. Very accessible No math Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011

Outfit Base Insulation Shell Body Environment Shirt Slacks Jacket Tie Socks Shoes Coat Shorts Outfit Body Environment Base Insulation Shell

Outfit Body Environment Base Insulation Shell

Outfit Choice= Mgmt/ctrl Garment Garment Garment Complexity  Robustness Layers must be hidden to be robust Choice (management and control) is more complex than assembly Outfit Choice= Mgmt/ctrl Body Environment Garment Garment Garment Assembly

Outfit Body Environment Garment Garment Garment

Garment Outfit

Outfit Garment Garment Garment Garment Garment Layering within garments (textiles) Outfit Garment Garment Garment Garment Garment Garment Garment Garment Cloth Cloth Cloth Thread Thread Thread Fiber Fiber Fiber

Garments Cloth Thread Fiber

Universal strategies? Garments Sew Cloth Weave Thread Spin Fiber Prevents unraveling of lower layers

constraints on cross-layer interactions quantization for robustness Universal strategies? Even though garments seem analog/continuous Garments have limited access to threads and fibers Garments Xform Cloth constraints on cross-layer interactions quantization for robustness Xform Thread Xform Fiber Prevents unraveling of lower layers

Networked, universal, layered Complexity? Networked, universal, layered Control Garments Xform Ctrl Mgmt Cloth Supply Xform Ctrl Mgmt Thread Xform Ctrl Mgmt Fiber Xform Ctrl Mgmt

Flux of material Assembly Control << assembly “weak linkage” (G&H) Garments Assembly Xform Cloth Xform Control Thread Xform Fiber

Control Complexity Assembly Control >> assembly Fashion Design >> construction Supply chain Management >> manufacture assembly transport Garments Control Xform Cloth Assembly Xform Thread Xform Fiber

Control >> assembly Garment Outfit Choice= Mgmt/ctrl Assembly Environment Garments Control Xform Cloth Body Assembly Xform Thread Xform Fiber Complexity Control >> assembly

Functionally diverse garments sew Diverse fabric General purpose machines knit, weave Diverse Thread spin Fiber Geographically diverse sources

Outfit Outfit Garment Garment Garment Garment Garment complementary views Outfit Outfit Garment Garment Garment Garment Garment Garment Garment Garment Garment Cloth Cloth Cloth Cloth Thread Thread Thread Thread Fiber Fiber Fiber Fiber

Outfit Base Insulation Shell Garments fit the standard view of “modules” Strong internal connection Weak external connection OK, but not the most important Outfit Body Environment Base Insulation Shell

Outfit Garment Garment Garment Garment Garment This architectural view of modularity is more fundamental and has two different dimensions: inner to middle to outer garments fiber to thread to cloth to garment Outfit Lack a taxonomy to describe these stack? compose? Garment Garment Garment Garment Garment Garment Garment Garment compose? Cloth Cloth Cloth stack? Thread Thread Thread Fiber Fiber Fiber

Layered architectures Physiology Organs Cortex Cortex Cortex compose? stack? . Cells Neurons Neurons Neurons Cells

complementary views of the brain Meta-layers Physiology Organs Prediction Goals Actions errors Cortex Fast, Limited scope Slow, Broad scope . Sejnowski lab

Forward pathways Outfit Assembly Garment materials errors Conscious Physiology Organs errors Conscious perception Forward pathways Cloth Thread Fiber Garments Xform Assembly Outfit Garment Assembly source receiver materials

Control Goals Actions Complexity Goals Feedback >>Forward Prediction Goals Actions Prediction Goals Conscious perception Choice= Mgmt/ctrl Feedback >>Forward Control receiver source control energy materials

Control Complexity Goals Actions Outfit Feedback >>Forward Physiology Organs Prediction Goals Actions errors Conscious perception Complexity Cloth Thread Fiber Garments Xform Assembly Control Outfit Garment Choice= Mgmt/ctrl Assembly Feedback >>Forward source receiver control energy materials

Human complexity Robust Fragile Metabolism Obesity, diabetes Regeneration & repair Healing wound /infect Obesity, diabetes Cancer AutoImmune/Inflame Start with physiology Lots of triage

Human complexity Robust Metabolism Regeneration & repair Healing wound /infect Efficient Mobility Survive uncertain food supply Recover from moderate trauma and infection

Mechanism? Robust Fragile Metabolism Obesity, diabetes Regeneration & repair Healing wound /infect Fat accumulation Insulin resistance Proliferation Inflammation Obesity, diabetes Cancer AutoImmune/Inflame Fat accumulation Insulin resistance Proliferation Inflammation

What’s the difference? Robust Fragile Controlled Dynamic Uncontrolled Metabolism Regeneration & repair Healing wound /infect Obesity, diabetes Cancer AutoImmune/Inflame Fat accumulation Insulin resistance Proliferation Inflammation Controlled Dynamic Uncontrolled Chronic

Controlled Dynamic Low mean High variability Fat accumulation Insulin resistance Proliferation Inflammation Controlled Dynamic Low mean High variability

Death Controlled Dynamic Uncontrolled Chronic Low mean High mean Fat accumulation Insulin resistance Proliferation Inflammation Controlled Dynamic Uncontrolled Chronic Low mean High variability High mean Low variability

Restoring robustness? Robust Fragile Controlled Dynamic Uncontrolled Metabolism Regeneration & repair Healing wound /infect Obesity, diabetes Cancer AutoImmune/Inflame Fat accumulation Insulin resistance Proliferation Inflammation Fat accumulation Insulin resistance Proliferation Inflammation Controlled Dynamic Uncontrolled Chronic Low mean High variability High mean Low variability

++Technology Biology weak fragile strong robust +Technology efficient (slow) inefficient wasteful

Human complexity Accident or necessity? Robust Yet Fragile 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?

Both 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”) Both Fundamentals! Accident or necessity?

Architecture? Hard tradeoffs? Constraints (that deconstrain) fragile robust Hard tradeoffs? wasteful efficient

fragile Amory B. Lovins, Reinventing Fire robust wasteful efficient

Want to understand the space of systems/architectures fragile Architectures? Hard limits on robust efficiency? Want robust and efficient systems and architectures robust efficient wasteful

Layered architectures Diverse applications TCP IP MAC MAC MAC Switch Pt to Pt Pt to Pt Physical

Proceedings of the IEEE, Jan 2007 OR optimization Chang, Low, Calderbank, and Doyle

Too clever? Diverse applications TCP IP Diverse Physical

Layered architectures Deconstrained (Applications) TCP Constrained Networks IP Deconstrained (Hardware) “constraints that deconstrain” (Gerhart and Kirschner)

Original design challenge? TCP/ IP Deconstrained (Hardware) (Applications) Constrained Networked OS Expensive mainframes Trusted end systems Homogeneous Sender centric Unreliable comms Facilitated wild evolution Created whole new ecosystem completely opposite

Networked OS Architecture TCP/ IP Deconstrained (Hardware) (Applications) Constrained OS better starting point than phone/comms systems Extreme robustness confers surprising evolvability Creative engineers Rode hardware evolution Facilitated wild evolution Created whole new ecosystem completely opposite Why? Architecture

Layered architectures Essentials Deconstrained (Applications) Few global variables Don’t cross layers Control, share, virtualize, and manage resources Constrained OS Processing Memory I/O Deconstrained (Hardware)

Layered architectures Bacterial biosphere Deconstrained (diverse) Environments Architecture = Constraints that Deconstrain Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. DNA DNAp Repl. Gene ATP Enzymes Building Blocks Shared protocols Deconstrained (diverse) Genomes

almost Inside every cell ATP Macro-layers Precursors Catabolism AA Enzymes Nucl. Macro-layers Building Blocks AA transl. Proteins ATP Ribosome RNA transc. xRNA Crosslayer autocatalysis RNAp DNA Repl. Gene DNAp

What makes the bacterial biosphere so adaptable? Environment Deconstrained phenotype Action Core conserved constraints facilitate tradeoffs Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. DNA DNAp Repl. Gene ATP Enzymes Building Blocks Layered architecture Active control of the genome (facilitated variation) Deconstrained genome

Layered architectures Deconstrained (Applications) Few global variables Don’t cross layers Direct access to physical memory? Control, share, virtualize, and manage resources Constrained OS Processing Memory I/O Deconstrained (Hardware)

Few global variables Don’t cross layers Bacterial biosphere Deconstrained (diverse) Environments Few global variables Architecture = Constraints that Deconstrain Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. DNA DNAp Repl. Gene ATP Enzymes Building Blocks Shared protocols Don’t cross layers Deconstrained (diverse) Genomes

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

Layered architectures Deconstrained (Applications) Few global variables? Don’t cross layers? TCP/ IP Control, share, virtualize, and manage resources Constrained I/O Comms Latency? Storage? Processing? Deconstrained (Hardware)

Original design challenge? TCP/ IP Deconstrained (Hardware) (Applications) Constrained Networked OS Expensive mainframes Trusted end systems Homogeneous Sender centric Unreliable hardware Facilitated wild evolution Created whole new ecosystem complete opposite

IP addresses interfaces (not nodes) IPC App App DNS caltech.edu? Global and direct access to physical address! IP addresses interfaces (not nodes) 131.215.9.49 Dev2 Dev2 Dev CPU/Mem Dev2 CPU/Mem CPU/Mem

IP addresses interfaces (not nodes) IPC App App DNS Global and direct access to physical address! Robust? Secure Scalable Verifiable Evolvable Maintainable Designable … IP addresses interfaces (not nodes) Dev2 Dev2 Dev CPU/Mem Dev2 CPU/Mem CPU/Mem

Naming and addressing need to be resolved within layer translated between layers not exposed outside of layer Physical IP TCP Application Related “issues” VPNs NATS Firewalls Multihoming Mobility Routing table size Overlays …

? Next layered architectures Deconstrained (Applications) Few global variables Don’t cross layers ? Control, share, virtualize, and manage resources Constrained Comms Memory, storage Latency Processing Cyber-physical Deconstrained (Hardware)

Persistent errors and confusion (“network science”) Architecture is least graph topology. Every layer has different diverse graphs. Application Architecture facilitates arbitrary graphs. TCP IP Physical

Smart Antennas (Javad Lavaei w/ Ali Hajimiri) Security, co-channel interference, power consumption Conventional Antenna Smart Antenna Smart Antennas 1) Multiple active elements: Easy to program Hard to implement 2) Multiple passive elements: Easy to implement Hard to program

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

Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011 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

Few global variables Don’t cross layers “Seeing is dreaming?” “Seeing is believing?” Which blue line is longer? Meta-layers Physiology Organs Actions Prediction Goals errors Cortex Fast, Limited scope Slow, Broad scope Few global variables Don’t cross layers .

Complex systems? Control Comms Complex networks Compute Stat physics Carnot Boltzmann Heisenberg Physics

Complex systems? Fragile Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … Even small amounts can create bewildering complexity

Complex systems? Robust Fragile Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence …

Complex systems? Robust complexity Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … Resources Controlled Organized Structured Extreme Architected …

New words Emergulent Fragile complexity Emergulence at the edge of Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … Emergulence at the edge of chaocritiplexity

“turbulence is a highly nonlinear phenomena” x z “turbulence is a highly nonlinear phenomena” Blunted turbulent velocity profile Laminar Turbulent

Irreducibile? Complexity? Small Large Robust Simple 2d, linear Fragile Model Small Large Robust Simple 2d, linear Organized Computer Fragile chaocritical 3d, nonlinear Irreducibile? mildly nonlinear highly nonlinear

? Laminar Control? Turbulent Turbulent Laminar fragile robust Fundamentals! Turbulent Laminar fragile Laminar Control? Turbulent ? robust efficient wasteful

Foundations, origins of noise dissipation amplification catalysis Control Wiener Comms Bode Shannon robust control Kalman Foundations, origins of noise dissipation amplification catalysis General Rigorous First principle Carnot Boltzmann Heisenberg Physics