Universal laws and architecture
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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 global SCADA EMS • 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 Our approach global SCADA EMS scalable, decentralized, real-time feedback, sec-min timescale endpoint based scalable control • local algorithms • global perspective 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 Tesla: multi-phase AC ? Deregulation started Enron, blackouts 1888 1876 Both started as natural monopolies Both provided a single commodity Both grew rapidly through two WWs Bell: telephone 1980-90s 2000s 1980-90s 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? Chang, Low, Calderbank, Doyle 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? 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, … my case studies 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 manageable mobile modifiable modular nomadic operable efficient orthogonality evolvable portable extensible precision failure predictable transparent producible fault-tolerant provable fidelity recoverable flexible relevant inspectable reliable installable repeatable Integrity reproducible interchangeable resilient interoperable responsive learnable reusable maintainable robust safety scalable seamless self-sustainable serviceable supportable securable simple stable standards compliant survivable sustainable tailorable testable timely traceable ubiquitous understandable upgradable usable 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 manageable safety mobile scalable modifiable seamless modular self-sustainable nomadic serviceable operable supportable orthogonality securable efficient evolvable portable simple extensible precision stable failure predictable standards transparent producible compliant fault-tolerant provable survivable fidelity recoverable sustainable flexible relevant tailorable inspectable reliable testable installable repeatable timely Integrity reproducible traceable interchangeable resilient ubiquitous interoperable responsive understandable learnable reusable upgradable maintainable usable robust Requirements on systems and architectures efficient simple sustainable robust Requirements on systems and architectures efficient simple sustainable robust Requirements on systems and architectures fragile sustainable robust simple complex efficient wasteful What we want fragile sustainable robust simple complex efficient wasteful What we get fragile robust simple complex efficient wasteful What we get fragile robust simple complex efficient wasteful 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 Today 2050 efficient wasteful Physics Future evolution of the “smart” grid? fragile sustainable robust simple complex efficient wasteful 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? Architectures? robust Want robust and efficient systems and architectures efficient wasteful Technology? fragile At best we get one robust efficient wasteful ??? fragile Often neither robust efficient wasteful ??? Bad architectures? fragile ? gap? Bad theory? robust efficient ? wasteful Exponential improvement in efficiency F 100% 10% log F 1% F Efficiency .1% http://phe.rockefeller.edu/Daedalus/Elektron/ Solving all energy problems? When will lamps be 200% efficient? 100% Exponential improvement 10% log F 1% F Efficiency .1% Note: this is real data! http://phe.rockefeller.edu/Daedalus/Elektron/ When will lamps be 200% efficient? Oops… never. 50% F 1 F 10% 1% F Efficiency .1% Note: need to plot it right. http://phe.rockefeller.edu/Daedalus/Elektron/ Control, OR Bode Nash Von Neumann Comms Kalman Pontryagin Shannon Theory? Deep, but fragmented, incoherent, incomplete Carnot Boltzmann Turing Godel Compute Heisenberg Einstein Physics Control Comms Shannon Bode fragile? ? slow? • • • • Each theory one dimension Tradeoffs across dimensions Assume architectures a priori Progress is encouraging, but… wasteful? Carnot Boltzmann Turing Godel Compute Heisenberg Einstein Physics 2.5d space of systems and architectures fragile robust efficient wasteful Conservation “laws”? fragile Case studies Sharpen hard bounds 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). 4 “Standard” Simulation 3 v=0.03 2 1 0 0 20 40 60 80 100 120 140 160 180 200 2 v=0.1 1.5 1 0.5 0 0 20 40 60 80 100 120 140 160 180 200 1 v=0.2 0.8 0.6 0.4 0.2 0 20 40 60 80 100 120 140 160 180 200 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. 4 Experiments 3 2 Why? Simulation v=0.03 1 0 0 20 40 60 80 100 120 140 160 180 200 2 v=0.1 1.5 1 0.5 0 0 20 40 60 80 100 120 140 160 180 200 1 v=0.2 0.8 0.6 0.4 0.2 0 20 40 60 80 100 120 140 160 180 200 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 autocatalytic? PFK? a? h? fragile? x? Robust =maintain energy charge w/fluctuating cell demand control? y? g? PK? Tradeoffs? Rest? rate k? robust? efficient? wasteful? Efficient=minimize metabolic overhead Theorem! z z p ln S j 2 d ln 2 0 z p z 1 Fragility ln z p z p simple enzyme complex enzyme Metabolic Overhead Fragility hard limits • General • Rigorous • First principle simple complex Overhead, waste Plugging in domain details ? • Domain specific • Ad hoc • Phenomenological Wiener Control Bode Kalman robust control Comms Shannon • Fundamental multiscale physics • Foundations, origins of – noise – dissipation – amplification – catalysis Carnot • General • Rigorous • First principle ? Boltzmann Heisenberg Physics Control Comms Complex networks Wildly “successful” 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 53 Control Comms Complex networks Compute Alderson &Doyle, Contrasting Views of Complexity and Their Implications for Network-Centric Infrastructure, IEEE TRANS ON SMC, JULY 2010 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 Alderson &Doyle, Contrasting Views of Complexity and Their Implications for Network-Centric Infrastructure, IEEE TRANS ON SMC, JULY 2010 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 From prediction to mechanism to control Complex networks Compute Sandberg, Delvenne, & Doyle, On Lossless Approximations, the Fluctuation-Dissipation Theorem, and Limitations of Measurement, IEEE TRANS ON AC, FEBRUARY, 2011 “orthophysics” Stat physics, fluids, QM Carnot Boltzmann Heisenberg Physics J. Fluid Mech (2010) Streamlined Laminar Flow Turbulence and drag? Turbulent Flow Physics of Fluids (2011) y z Uw y Flow z u x x Coherent structures and turbulent drag high-speed region upflow downflow 3D coupling low speed streak Blunted turbulent velocity profile Turbulent Laminar Uw Uw Turbulent fragile robust Laminar ? efficient Laminar Turbulent wasteful How general is this picture? fragile simple tech robust Implications for human evolution? Cognition? Technology? complex tech efficient wasteful Layered architectures Diverse applications TCP IP MAC Switch MAC MAC Pt to Pt Pt to Pt Physical Proceedings of the IEEE, Jan 2007 Chang, Low, Calderbank, and Doyle OR optimization Too clever? Diverse applications TCP IP Diverse Physical Layered architectures Deconstrained (Applications) Networks TCP IP Constrained Deconstrained (Hardware) “constraints that deconstrain” (Gerhart and Kirschner) Original design challenge? Deconstrained (Applications) Constrained TCP/ IP Deconstrained (Hardware) 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 weak fragile slow Human evolution strong robust fast hands feet All very skeleton different. muscle skin gut long helpless childhood Apes How is this progress? weak fragile (slow) Human evolution hands feet skeleton muscle skin gut Apes strong robust (fast) efficient inefficient wasteful weak fragile Architecture? Evolvable? Hard tradeoffs? Apes strong robust efficient (slow) inefficient wasteful weak fragile Architecture? + strong robust sticks stones fire efficient (slow) inefficient wasteful hands feet skeleton muscle skin gut weak fragile + strong robust sticks stones fire efficient (slow) From weak prey to invincible predator Before much brain expansion? hands feet skeleton muscle skin gut weak fragile + sticks strong stones robust fire efficient (slow) Key point: Our physiology, technology, and brains have coevolved From weak prey to invincible predator Before much brain expansion? Huge implications. hands feet skeleton muscle skin gut weak fragile + sticks strong stones robust fire efficient (slow) Key point needing more discussion: The evolutionary challenge of big brains is homeostasis, not basal metabolic load. From weak prey to invincible predator Before much brain expansion? Huge implications. weak fragile Architecture? + strong robust sticks stones fire efficient (slow) inefficient wasteful Human complexity? fragile sticks stones fire feet skeleton muscle skin gut hands Consequences of our evolutionary history. robust efficient wasteful 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?” Slow, Broad scope Which blue Meta-layers Fast, Limited scope line is longer? delay=death sense move Spine Reflect Reflex sense move Spine Reflect Reflex sense move Spine Reflect Reflex sense move Spine Reflect Reflex sense move Spine Layered architectures Physiology Organs Cells Spine errors move Actions sense Goals Actions Reflex Reflect Prediction Cortex Meta-layers Prediction Goals Actions errors Physiology Actions Organs Seeing is dreaming Conscious perception 3D +time Simulation Conscious perception Zzzzzz….. Same size? Same size Same size Same size? Vision: evolved for complex simulation and control, not 2d static pictures Seeing is dreaming 3D Conscious perception +time Simulation + complex models (“priors”) Conscious perception Zzzzzz….. Conscious perception Seeing is dreaming 3D +time Simulation + complex models (“priors”) Conscious perception Prediction errors Seeing is dreaming Conscious perception Seeing is believing 3D +time Simulation + complex models (“priors”) Conscious perception Prediction errors Seeing is dreaming Conscious perception Seeing is believing 3D +time Simulation + complex models (“priors”) Conscious perception Prediction errors Our most integrated perception of the world 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? Which blue line is longer? With social pressure, this one. Standard social psychology experiment. Seeing is dreaming 3D Conscious perception +time Simulation + complex models (“priors”) Conscious perception Zzzzzz….. Seeing is dreaming Conscious perception Seeing is believing 3D +time Simulation + complex models (“priors”) Conscious perception Our most integrated Prediction perception of the world errors But ultimately, only actions matter. Conscious perception Prediction Prediction Goals Actions Goals Conscious perception errors Physiology Actions Organs signaling gene expression metabolism lineage receiver Biological pathways source Primary function Recurring theme: research starts here, but then… signaling gene expression metabolism lineage receiver source control energy materials More complex feedback complexity for robustness receiver source robustness control energy materials efficiency errors Physiology fast Prediction Prediction Goals Goals Actions Conscious perception receiver Organs Actions source control fast energy materials Slow, Broad scope Unfortunately, we’re not sure how this all works. Meta-layers Fast, Limited scope “Seeing is dreaming?” “Seeing is believing?” Slow, Broad scope Which blue Meta-layers Fast, Limited scope line is longer? Slow, Broad scope Meta-layers Fast, Limited scope Slow, Broad scope UAV Fast, Limited scope Layered architectures Comms Interface Fast, Limite d scope Remote Sensor Sensor Actuator Disturbance Slow, Broad scope Plant Comms Interface Remote Sensor Sensor Actuator Disturbance Layered architectures Plant Comms Interface Remote Sensor Disturba nce Layered architectures Sensor Plant Actuator Comms Meta-layers Interface Slow, Broad scope Remote Sensor Disturba nce Layered architectures Sensor Plant Actuator Fast, Limited scope Next layered architectures Control, share, virtualize, and manage resources ? Comms Interface Constrained Disturbance Deconstrained (Applications) Remote Sensor Sensor Deconstrained (Hardware) Actuator Plant Other examples Clothing Lego Money Cell biology 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 T-Shirt Shirt Jacket Tie Shorts Shoes Slacks Coat Socks Outfit Shell Insulation Environment Base Body Outfit Shell Insulation Environment Base Body • Complexity Robustness • Layers must be hidden to be robust • Choice (management and control) is more complex than assembly Outfit Assembly Environment Garment Garment Choice= Mgmt/ctrl Garment Body Outfit Garment Garment Environment Garment Body Garment Garment Garment Outfit Layering within garments (textiles) Outfit 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 Universal strategies? Even though garments seem analog/continuous Garments Xform Garments have limited access to threads and fibers Cloth quantization for robustness Xform Thread Xform constraints on cross-layer interactions Fiber Prevents unraveling of lower layers Complexity? Networked, Garments Supply Cloth Xform Thread Xform Ctrl Mgmt Ctrl Mgmt Ctrl Mgmt Ctrl Mgmt Control Xform universal, layered Fiber Xform Flux of material • Control << assembly • “weak linkage” (G&H) Garments Xform Xform Thread Xform Fiber Control Assembly Cloth Complexity Garments Control Assembly Xform Cloth • Control >> assembly • Fashion Design >> construction Xform Thread Xform Fiber • Supply chain Management >> manufacture assembly transport Garments Xform Thread Body Garment Cloth Garment Assembly Xform Garment Control Choice= Mgmt/ctrl Environment Outfit Assembly Xform Fiber Complexity Control >> assembly Functionally diverse garments sew Diverse fabric General purpose machines knit, weave Diverse Thread spin Fiber Geographically diverse sources complementary views Outfit Outfit Garment Garment Garment Garment Garment Garment Garment Cloth Cloth Cloth Thread Thread Thread Fiber Fiber Fiber Cloth Thread Fiber Garments fit the standard view of “modules” • Strong internal connection • Weak external connection OK, but not the most important Outfit Shell Insulation Environment Base Body This architectural view of modularity is more fundamental and has two different dimensions: 1. inner to middle to outer garments 2. fiber to thread to cloth to garment Outfit Fiber Cloth Garment Garment Thread compose? Garment Cloth Garment Garment Garment Cloth stack? Thread Thread Fiber Fiber Lack a taxonomy to describe these 1. stack? 2. compose? compose? Layered architectures Physiology stack? Organs Cells complementary views of the brain Slow, Broad scope Meta-layers Fast, Limited scope Sejnowski lab errors Physiology Conscious perception Organs Forward pathways Xform Garment Garment Garment Cloth receiver Assembly Xform source Thread Xform Fiber materials Assembly Outfit Garments Complexity Prediction Goals Conscious perception Prediction Goals Actions source control energy materials Control Choice= Mgmt/ctrl receiver Feedback >>Forward Complexity errors Physiology Goals Conscious perception Prediction Goals Actions Garment Garment Garment Choice= Mgmt/ctrl receiver Assembly Feedback >>Forward Garments Control Outfit Organs Actions Xform Assembly Prediction source control energy materials Cloth Xform Thread Xform Fiber Human complexity Robust Fragile Metabolism 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 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 Metabolism Regeneration & repair Healing wound /infect Controlled Dynamic Obesity, diabetes Cancer AutoImmune/Inflame Fat accumulation Insulin resistance Proliferation Inflammation Uncontrolled Chronic Controlled Dynamic Low mean High variability Fat accumulation Insulin resistance Proliferation Inflammation Death Controlled Dynamic Low mean High variability Fat accumulation Insulin resistance Proliferation Inflammation Uncontrolled Chronic High mean Low variability Restoring robustness? Robust Metabolism Regeneration & repair Healing wound /infect Fat accumulation Insulin resistance Proliferation Inflammation Fragile Obesity, diabetes Cancer AutoImmune/Inflame Fat accumulation Insulin resistance Proliferation Inflammation Controlled Dynamic Uncontrolled Chronic Low mean High variability High mean Low variability weak fragile ++Technology strong robust efficient (slow) inefficient wasteful Human complexity Robust Metabolism Regeneration & repair Immune/inflammation Microbe symbionts Neuro-endocrine Complex societies Advanced technologies Risk “management” Yet Fragile Obesity, diabetes Cancer AutoImmune/Inflame Parasites, infection Addiction, psychosis,… Epidemics, war,… Disasters, global &!%$# Obfuscate, amplify,… Accident or necessity? Robust Fragile Metabolism Obesity, diabetes Regeneration &repair Cancer Fat accumulation Healing wound /infect AutoImmune/Inflame 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 Accident or necessity? Constraints (that deconstrain) fragile robust Hard tradeoffs? efficient wasteful fragile Amory B. Lovins, Reinventing Fire robust efficient wasteful Want to understand the space of systems/architectures fragile robust Architectures? Want robust and efficient systems and architectures efficient wasteful Layered architectures Diverse applications TCP IP MAC Switch MAC MAC Pt to Pt Pt to Pt Physical Proceedings of the IEEE, Jan 2007 Chang, Low, Calderbank, and Doyle OR optimization Too clever? Diverse applications TCP IP Diverse Physical Layered architectures Deconstrained (Applications) Networks TCP IP Constrained Deconstrained (Hardware) “constraints that deconstrain” (Gerhart and Kirschner) Original design challenge? Deconstrained (Applications) Constrained TCP/ IP Deconstrained (Hardware) Networked OS • Expensive mainframes • Trusted end systems • Homogeneous • Sender centric • Unreliable comms Facilitated wild evolution Created • whole new ecosystem • completely opposite Networked OS Deconstrained (Applications) Constrained TCP/ IP Deconstrained (Hardware) • 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 Deconstrained (Applications) Essentials Few global variables Don’t cross layers Constrained OS Control, share, virtualize, and manage resources Deconstrained (Hardware) Processing Memory I/O Layered architectures Deconstrained (diverse) Environments Precursors Catabolism ATP Shared protocols Enzymes Building Blocks AA Proteins transl. ATP Ribosome RNA transc. xRNA RNAp DNA Repl. Gene DNAp Deconstrained (diverse) Genomes Bacterial biosphere Architecture = Constraints that Deconstrain Precursors Catabolism Inside every cell almost ATP Enzymes Macro-layers Building Blocks Crosslayer autocatalysis AA transl. Proteins ATP Ribosome RNA transc. xRNA DNA Repl. Gene RNAp DNAp What makes the bacterial biosphere so adaptable? Environment Deconstrained phenotype Core conserved constraints facilitate tradeoffs Precursors Catabolism ATP Enzymes Building Blocks Action AA Proteins transl. ATP Ribosome RNA transc. xRNA RNAp DNA Repl. Gene DNAp Deconstrained genome Active control of the genome (facilitated variation) Layered architectures Deconstrained (Applications) Few global variables Don’t cross layers Direct access to Control, share, virtualize, Constrained OS physical and manage resources memory? Processing Memory Deconstrained I/O (Hardware) Deconstrained (diverse) Environments Few global variables Architecture Precursors ATP Catabolism Bacterial biosphere Enzymes Building Blocks AA Shared protocols = Constraints that Don’t cross layers Deconstrain Proteins transl. ATP Ribosome RNA transc. xRNA RNAp DNA Repl. Gene DNAp 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? Constrained TCP/ IP Control, share, virtualize, and manage resources Deconstrained (Hardware) I/O Comms Latency? Storage? Processing? Original design challenge? Deconstrained (Applications) Constrained TCP/ IP Deconstrained (Hardware) Networked OS • Expensive mainframes • Trusted end systems • Homogeneous • Sender centric • Unreliable hardware Facilitated wild evolution Created • whole new ecosystem • complete opposite IPC App caltech.edu? App DNS IP addresses interfaces (not nodes) Global and direct access to physical address! 131.215.9.49 IPC App App DNS Robust? • Secure • Scalable • Verifiable • Evolvable • Maintainable • Designable •… IP addresses interfaces (not nodes) Global and direct access to physical address! 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 •… Application TCP IP Physical Next layered architectures Deconstrained (Applications) Few global variables Don’t cross layers Constrained ? Control, share, virtualize, and manage resources Deconstrained (Hardware) Comms Memory, storage Latency Processing Cyber-physical Persistent errors and confusion (“network science”) Every layer has different diverse graphs. Architecture is least graph topology. Application TCP IP Physical Architecture facilitates arbitrary graphs. 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 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 “Seeing is dreaming?” “Seeing is believing?” Which blue line is longer? Few global variables Don’t cross layers Complex systems? Control Comms Complex networks Compute Stat physics Carnot Boltzmann Heisenberg Physics Complex systems? Fragile Even small amounts can create bewildering complexity • • • • • • • • • • Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … 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 Emergulence at the edge of chaocritiplexity Fragile complexity • • • • • • • • • • Scale Dynamics Nonlinearity Nonequlibrium Open Feedback Adaptation Intractability Emergence … u 1 u u p u t R u 0 z “turbulence is a highly nonlinear phenomena” Uw y x Blunted turbulent velocity profile Turbulent Laminar Uw u 1 u u p u t R Complexity? u 0 Model Small Simple Robust 2d, linear chaocritical Fragile 3d, nonlinear mildly nonlinear Large Organized Computer Irreducibile? highly nonlinear Uw Turbulent fragile robust Laminar ? efficient Laminar Turbulent wasteful Wiener Control Bode Kalman • General • Rigorous • First principle robust control Comms Shannon • Foundations, origins of – noise – dissipation – amplification – catalysis Carnot Boltzmann Heisenberg Physics
