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Universal laws and architecture: Foundations for Sustainable Infrastructure John Doyle Control and Dynamical Systems, EE, BioE Caltech.

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Presentation on theme: "Universal laws and architecture: Foundations for Sustainable Infrastructure John Doyle Control and Dynamical Systems, EE, BioE Caltech."— Presentation transcript:

1 Universal laws and architecture: Foundations for Sustainable Infrastructure John Doyle Control and Dynamical Systems, EE, BioE Caltech

2 Caltech smartgrid research Motivation: –uncertainty + –large scale Optimization and control – demand response – power flow (Javad Lavaei) PIs: Low, Chandy, Wierman,...

3 Current control local global slow fast relay system SCADA EMS centralized state estimation contingency analysis optimal power flow simulation human in loop decentralized mechanical mainly centralized, open-loop preventive, slow timescale

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

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

6 Architectural transformation... to become more interactive, more distributed, more open, more autonomous, and with greater user participation... while maintaining security & reliability

7 Caltech smartgrid research Motivation: –uncertainty + –large scale Optimization and control – demand response – power flow (Javad Lavaei) PIs: Low, Chandy, Wierman,...

8 Proceedings of the IEEE, Jan 2007 Chang, Low, Calderbank, Doyle OR optimization What’s next? Fundamentals! A rant

9 “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

10 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

11 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 Requirements on systems and architectures

12 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 Simplified, minimal requirements

13 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 Requirements on systems and architectures efficient robust simple sustainable

14 Requirements on systems and architectures efficient robust sustainable simple

15 Requirements on systems and architectures efficient robust sustainable simple

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

17 What we want efficient robust simple fragile wasteful complex sustainable

18 efficient robust simple fragile wasteful complex What we get

19 efficient robust simple fragile wasteful complex What we get

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

21 robustY fragileY robustXfragileX Bad Optimal Sometimes? robustZ fragileZ

22 robustY fragileY robustXfragileX Bad Oops? robustZ fragileZ Optimal

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

24 Amory B. Lovins, Reinventing Fire

25 Very accessible No math I’m interested in fire…

26 Accessible ecology UG math

27 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

28 wastefulefficient Today 2050 Physics

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

30 This paper aims to bridge progress in neuroscience involving sophisticated quantitative analysis of behavior, including the use of robust control, with other relevant conceptual and theoretical frameworks from systems engineering, systems biology, and mathematics. Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011 Very accessible No math

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

32 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

33 wasteful fragile robust efficient At best we get one Technology?

34 wasteful fragile robust efficient Often neither  ??? 

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

36 .1% 1% 10% 100% http://phe.rockefeller.edu/Daedalus/Elektron/ Exponential improvement in efficiency F

37 .1% 1% 10% 100% http://phe.rockefeller.edu/Daedalus/Elektron/ When will lamps be 200% efficient? Exponential improvement Note: this is real data! Solving all energy problems?

38 .1% 1% 10% 50% http://phe.rockefeller.edu/Daedalus/Elektron/ Oops… never. Note: need to plot it right. When will lamps be 200% efficient?

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

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

41 2.5d space of systems and architectures wasteful fragile efficient robust simple complex

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

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

44 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

45 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. “Standard” Simulation

46 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. SimulationExperiments Why?

47 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!

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

49 simple enzyme Fragility Metabolic Overhead complex enzyme Theorem!

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

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

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

53 D. Alderson, NPS53 Popular but wrong

54 doesn’t work Stat physics Complex networks Physics Heisenberg Carnot Boltzmann ControlComms 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 networks edge of chaos, self-organized criticality, scale-free,…

55 Stat physics Complex networks Physics Heisenberg Carnot Boltzmann ControlComms 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

56 Stat physics, Complex networks Physics Heisenberg Carnot Boltzmann ControlComms 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!

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

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

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

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

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

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

63 TCP IP Physical Diverse applications Diverse Too clever?

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

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

66 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 ?????

67 natural selection + genetic drift + mutation + gene flow ++ architecture

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

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

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

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

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

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

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

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

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

77 This paper aims to bridge progress in neuroscience involving sophisticated quantitative analysis of behavior, including the use of robust control, with other relevant conceptual and theoretical frameworks from systems engineering, systems biology, and mathematics. Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011 Very accessible No math

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

79

80 sense move Spine delay=death

81 sense move Spine Reflex Reflect

82 sense move Spine Reflex Reflect

83 sense move Spine Reflect Reflex Layered

84 sense move Spine Reflect Reflex Layered

85 Physiology Organs Neurons Cortex Cells Cortex Layered architectures Cells

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

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

88 3D +time Simulation Seeing is dreaming Conscious perception Conscious perception Zzzzzz…..

89 Same size?

90

91 Same size

92

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

94 3D +time Simulation + complex models (“priors”) Seeing is dreaming Conscious perception Conscious perception Zzzzzz…..

95 3D +time Simulation + complex models (“priors”) Seeing is dreaming Conscious perception Conscious perception Prediction errors

96 3D +time Simulation + complex models (“priors”) Seeing is dreaming Conscious perception Conscious perception Prediction errors Seeing is believing

97 3D +time Simulation + complex models (“priors”) Seeing is dreaming Conscious perception Conscious perception Prediction errors Seeing is believing Our most integrated perception of the world

98 Which blue line is longer?

99

100

101

102

103

104 With social pressure, this one. Standard social psychology experiment.

105

106 3D +time Simulation + complex models (“priors”) Seeing is dreaming Conscious perception Conscious perception Zzzzzz…..

107 3D +time Simulation + complex models (“priors”) Seeing is dreaming Conscious perception Conscious perception Prediction errors Seeing is believing Our most integrated perception of the world

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

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

110 sourcereceiver control energy materials signaling gene expression metabolism lineage More complex feedback

111 sourcereceiver control energy materials complexity for robustness efficiency robustness

112 sourcereceiver control energy materials Physiology Organs Prediction Goals Actions errors Actions Prediction Goals Conscious perception fast

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

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

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

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

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

118 Comms Disturbance Plant Remote Sensor Actuator Interface Control Layered architectures

119 Comms Disturba nce Plant Remote Sensor Actuator Interface Control

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

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

122 Other examples Clothing Lego Money Cell biology

123 This paper aims to bridge progress in neuroscience involving sophisticated quantitative analysis of behavior, including the use of robust control, with other relevant conceptual and theoretical frameworks from systems engineering, systems biology, and mathematics. Doyle and Csete, Proc Nat Acad Sci USA, online JULY 25 2011 Very accessible No math

124 Base Insulation Shell Outfit BodyEnvironment Shirt Slacks Jacket Tie T-Shirt Socks ShoesCoat Shorts

125

126 Base Insulation Shell Outfit BodyEnvironment

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

128 Garment Outfit BodyEnvironment

129 Garment Outfit

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

131 Cloth Thread Fiber Garments

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

133 Cloth Thread Fiber Garments 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

134 Cloth Thread Fiber Garments XformCtrlMgmt Networked, universal, layered XformCtrlMgmt XformCtrlMgmt XformCtrlMgmt Control Supply Complexity?

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

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

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

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

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

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

141 Garment Cloth Thread Fiber Garment Cloth Thread Fiber Garment Cloth Thread Fiber Garment Outfit 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 Lack a taxonomy to describe these 1.stack? 2.compose? stack? compose?

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

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

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

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

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

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

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

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

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

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

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

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

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

155 RobustYet Fragile Human complexity Metabolism Regeneration & repair Immune/inflammation Microbe symbionts Neuro-endocrine  Complex societies  Advanced technologies  Risk “management”  Obesity, diabetes  Cancer  AutoImmune/Inflame  Parasites, infection  Addiction, psychosis,…  Epidemics, war,…  Disasters, global &!%$#  Obfuscate, amplify,… Accident or necessity?

156 RobustFragile Metabolism Regeneration & repair Healing wound /infect  Obesity, diabetes  Cancer  AutoImmune/Inflame  Fat accumulation  Insulin resistance  Proliferation  Inflammation Fragility  Hijacking, side effects, unintended… Of mechanisms evolved for robustness Complexity  control, robust/fragile tradeoffs Math: robust/fragile constraints (“conservation laws”) Accident or necessity? Both Fundamentals!

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

158 wasteful fragile efficient robust Amory B. Lovins, Reinventing Fire

159 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?

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

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

162 TCP IP Physical Diverse applications Diverse Too clever?

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

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

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

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

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

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

169 Catabolism AA Ribosome RNA RNAp transl. Proteins xRNA transc. Precursors Nucl. AA DNA DNAp Repl. Gene 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

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

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

172 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?

173 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

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

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

176 App IPC Global and direct access to physical address! DNS IP addresses interfaces (not nodes) caltech.edu? 131.215.9.49

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

178 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 …

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

180 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

181 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

182 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:

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

184 Csete and Doyle

185 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

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

187 Stat physics Complex networks Physics Heisenberg Carnot Boltzmann ControlComms Compute Complex systems?

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

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

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

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

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

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

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

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


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