From Programs to Systems – Building a Smarter World

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Professor Professor Joseph Sifakis gave a lecture on From Programs to Systems – Building a Smarter World in the Distinguished Lecturer Series - Leon The Mathematician.

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From Programs to Systems – Building a Smarter World

  1. 1. From Programs to Systems: Building a Smarter World Aristotle University of Thessaloniki - Department of Informatics Distinguished Lecturer Series – Leon the Mathematician May 4, 2012Joseph SifakisVERIMAG Laboratory and EPFL
  2. 2. The Evolution of IST  Convergence between Computing Cloud and Telecommunications Computing  Graphic Interfaces, Mouse Scientific Computing Multi-core – Defence Applications Systems WEB – Information Society 1945 1980 1990 20101936 1970 2000 2015 Embedded Systems: Foundations - Computing + Physicality Alan Turing,  Seamless revolution Kurt Gödel  95% of chips are embedded  Information Systems: The Internet of Things: Commercial Applications Convergence between  Integrated circuits Embedded Systems and the Internet Evolution driven by exponential progress in technology and explosion of applications
  3. 3. Systems Everywhere – Moore’s Law Price of 1 MB of memory76 000 € 6 000 € 450 € 1973 1977 120 € 30 € 1981 4.5 € 1984 0.46 € 1987 0.06 € 1990Moore’s Law: the number of 1995 0.004 €transistors that can be placedinexpensively on an integrated 2000circuit doubles approximately 2005every two years
  4. 4. Systems Everywhere Systems integrating software and hardwarejointly and specifically designed to provide given functionalities, which are often critical. 4
  5. 5. Systems Everywhere 40 chips elsewhere 80 chips ATM, cell phone, PDA, in home appliances TV, DVD player, phone, games, washer, dryer, dishwasher, etc. 40 chips Each person at work uses about printers, scanners, PC, 250 chips phone systems, etc. each day 70 chips1 billion transistors in the car used per person door opener, ABS, airbag, GPS, each day (2008) radio, engine control, etc.
  6. 6. Systems Everywhere – ImpactSystem technologies  have a tremendous economic and societal impact  they allow addressing global challenges for growth, health, services and innovationIBM’s view for a Smarter PlanetSystems are  Instrumented The state of almost everything can be measured, sensed and monitored  Interconnected People and objects can communicate and interact with each other in entirely new ways  Intelligent Enhanced predictability of events and optimal use of resources 6
  7. 7.  From Programs to Systems  System Design  Basic Technologies  Applications Three Grand ChallengesO  Marry Physicality and ComputationVE  Component-based DesignRV  AdaptivityIE  A Vision for CSW 7
  8. 8. From Programs to SystemsProgram System fi(ii) = oi on …… o2,o1 in …… i,2,i1i o f(i) = o Physical Environment I/O values  I/O streams of values Terminating  Non-terminating Deterministic  Non-deterministic Platform-independent  Platform-dependent behavior behavior Theory of computation  No unified theoretical framework
  9. 9. From Programs to Systems SW+HW Real-Time Control Resources Buildings Transport Communications Health
  10. 10. From Programs to Systems Software HW and Physical World
  11. 11.  From Programs to Systems  System Design  Basic Technologies  Applications Three Grand ChallengesO  Marry Physicality and ComputationVE  Component-based DesignRV  AdaptivityIE  A Vision for CSW 11
  12. 12. System Design – Simplified ViewSystem design is the process leading to a mixed software-hardware systemmeeting given requirements The expected behavior of the Requirements system to be designed withDifferent respect to its potential usersfrom and its environmentpure SWor pure HWdesign! Executable platform- Program independent model meeting the requirements SW System composed of HW HW and SW – the HW platform may be given
  13. 13. System Design – New TrendsNew trends break with traditional Computing Systems Engineering.It is hard to jointly meet technical requirements such as:  Reactivity: responding within known and guaranteed delay Ex : flight controller  Autonomy: provide continuous service without human intervention Ex : no manual start, optimal power management  Dependability: guaranteed minimal service in any case Ex : attacks, hardware failures, software execution errors  Scalability: at runtime or evolutionary growth (linear performance increase with resources) Ex : reconfiguration, scalable services ...and also take into account economic requirements for optimal cost/quality Technological challenge : Capacity to build systems of guaranteed functionality and quality, at an acceptable cost. 13
  14. 14. System Design – State of the Art We master – at a high cost – two types of systems which areTODAY difficult to integrate:  Safety and/or security critical systems of low complexity  Flight controller, smart card  Complex « best effort » systems  Telecommunication systems, web-based applications We need:  Affordable critical systemsTOMORROW Ex : transport, health, energy management  Successful integration of heterogeneous systems of systems  Internet of Things  Intelligent Transport Systems  Smart Grids  « Ambient Intelligence»
  15. 15. Air Traffic Control – the Next Generation Is it … attainable ? 15
  16. 16. System Design – Still a long way to go Computing systems engineering lacks similar constructivity resultsTraditional systemsengineering disciplines are only partial answers to particularbased on solid theory for design problemsbuilding artefacts with predictability is hard to guaranteepredictable behaviour over at design timetheir life-time. a posteriori validation remains essential for ensuring correctness
  17. 17. System Design – Still a long way to goDesign of large IT systems is a risky undertaking, mobilizing hundreds of engineersover several years.Difficulties  Complexity – mainly for building systems by reusing existing components  Requirements are often incomplete, and ambiguous (specified in natural language)  Design approaches are empirical and based on the expertise and experience of teamsConsequences  Large IT projects are often over budget, over time, and deliver poor quality.  Of these, 40% fail, 30% partially succeed, 30% succeed.
  18. 18. Technological CapabilitiesComputing Systems Design System Design – An Increasing Gap
  19. 19.  From Programs to Systems  System Design  Basic Technologies  Applications Three Grand ChallengesO  Marry Physicality and ComputationVE  Component-based DesignRV  AdaptivityIE  A Vision for CSW 19
  20. 20. Basic technologies – multicore systemsA multicore system integrates many cores(processors) on a chip The switch to multicore systems  is not at all the consequence of a scientific breakthrough,  is primarily due to technology walls that prevent from pushing forward the efficient implementation of traditional uniprocessor designs in silicon Increasing applications efficiency, simply by upgrading the hardware without significantly changing the software, is not anymore possible for multicore systems. The promise of parallel machines delivering almost unlimited computing power is not new. For decades there have been numerous industrial attempts in this direction, but almost all failed.
  21. 21. Basic technologies – multicore systems “Software is struggling to keep pace with the fast growth of multicore processors” “Running advanced multicore machines with todays software is like "putting a Ferrari engine in a go-cart,“ "Many of the software configurations in use today will be challenged to support the hardware configurations possible, and those will be accelerating in the future." Gartner, Research Note, January 2009 21
  22. 22. Basic technologies – Sensor Networks
  23. 23. Basic Technologies – Internet and the WEB THE WEB Streaming Collaborative work Social Networks Browser Search EngineUser ISP Internet Backbone NAP INTERNET LAN DSL, Wireless User WEB server Satellite Services Peering platform
  24. 24. Internet-based Systems – SecuritySecurity: protection of information from unauthorized access, use, disclosure,disruption, modification or destruction.Key requirements:  Confidentiality is the property of preventing disclosure of information to unauthorized individuals or systems e.g. for credit card transactions  Integrity means that data cannot be modified without authorization e.g. resistance to viruses  Availability, that is information must be available when it is needed e.g. preventing denial-of-service attacks.  Authenticity, that is ensuring that the transactions, communications and data are genuine.  Non-repudiation that is ensuring that a party in a dispute cannot repudiate, or refute the validity of a statement or contract e.g. electronic check or contract.
  25. 25. Internet-based Systems – The Internet of ThingsThe “Internet of Things” will be the result of the convergence betweenEmbedded Systems and the Internet.Basic idea: Use the Internet Protocol Suite for Human/ES or ES/ES interactionThis is not as easy as it seems.Some major breakthroughs needed:  Wireless sensor networks and RFID technologies.  Advances in miniaturization and nanotechnology mean that smaller and smaller things will have the ability to interact and connect.  Getting IP down to small devices and implementing features for QoS control and responsiveness.  Standards e.g. for naming objects, tools and platforms.  Improving overall security, and reliability of the internet.Evolution towards specific “critical” internets ?
  26. 26.  From Programs to Systems  System Design  Basic Technologies  Applications Three Grand ChallengesO  Marry Physicality and ComputationVE  Component-based DesignRV  AdaptivityIE  A Vision for CSW 26
  27. 27. Applications – Smart Transportation SystemsMake transportation safer, more efficient, less pollutingActive safety:assist/protect the driverby using drive-by-wireand brake-by-wiretechnologyAutomated Highways:intelligent transportationsystem technologydesigned to provide fordriverless cars on specificrights-of-way. 27
  28. 28. System Design – e-HealthBrain monitoring Robot-assisted surgery Blood pressure meter ECG Thermometer Remote Medical Assistant 28
  29. 29. Applications – Smart Grids
  30. 30. Applications – Ambient Intelligence
  31. 31.  From Programs to Systems  System Design  Basic Technologies  Applications Three Grand ChallengesO  Marry Physicality and ComputationVE  Component-based DesignRV  AdaptivityIE  A Vision for CSW 31
  32. 32. Three Grand ChallengesSystems are Instrumented : we need models encompassing continuous and discrete dynamics to predict the global behavior of a system  Marry Physicality and Computation Interconnected: we need theory, models and tools for building complex systems by assembling components  Component-based Design Intelligent: we need systems which adapt their behavior to changing environment  Adaptivity
  33. 33.  From Programs to Systems  System Design  Basic Technologies  Applications Three Grand ChallengesO  Marry Physicality and ComputationVE  Component-based DesignRV  AdaptivityIE  A Vision for CSW 33
  34. 34. Marry Physicality and ComputationHW Platform: CPU speed memory power failure rates temperature Software: SYSTEM application SW middleware OS Environment:  deadlines  jitter  throughput 34
  35. 35. Marry Physicality and ComputationHW Platform: SW Design CPU speed cannot ignore HW design memory power failure rates temperature Software: SYSTEM application SW middleware OS Environment:  deadlines  jitter  throughput 35
  36. 36. Marry Physicality and ComputationHW Platform: CPU speed memory power failure rates temperature Software: SYSTEM application SW middleware OS Environment:  deadlines  jitter SW Design  throughput cannot ignore control design 36
  37. 37. Marry Physicality and Computation System Design coherently HW Platform: integrates all these  CPU speed  memory  power  failure rates  temperature Software: SYSTEM application SW middleware OS Environment:  deadlines  jitter  throughputWe need to revisit and revise computing to integrate methods from EE and Control 37
  38. 38. Physicality and Computation – ExampleMatlab/Simulink 38
  39. 39. Marry Physicality and Computation – ExampleUML Model(Rational Rose)
  40. 40.  From Programs to Systems  System Design  Basic Technologies  Applications Three Grand ChallengesO  Marry Physicality and ComputationVE  Component-based DesignRV  AdaptivityIE  A Vision for CSW 40
  41. 41. Component-based Design Building complex systems by composing components (simpler systems). Is essential for any engineering discipline. This confers numerous advantages such as mastering complexity, productivity and correctness through reuse Component composition orchestrates interactions between components. It lies at the heart of the parallel computing challenge.No Common Component Model for Systems Engineering!
  42. 42. Component-based Design – The Babel of Languages System designers deal with a large variety of components, with different characteristics, from a large variety of viewpoints, each highlighting different dimensions of a system TSpaces Concurrent Fortran Java C Softbench NesC SWbus BPEL, Corba SES/Workbench MPI SysML Javabeans AADL .NET Statecharts Fractal Matlab/Simulink, Verilog VHDL SystemC TLMConsequences: Using semantically unrelated formalisms e.g. for programming, HW description and simulation, breaks continuity of the design flow and jeopardizes its coherency System development is decoupled from validation and evaluation.
  43. 43. Component-based Design – CompositionalityRules guaranteeing that composite components inherit good properties ofconstituent components
  44. 44. Component-based Design – CompositionalityRules guaranteeing that composite components inherit good properties ofconstituent components
  45. 45. Component-based Design – ComposabilityRules guaranteeing thatcomposition does notjeopardize good propertiesof constituent components
  46. 46. Component-based Design – ComposabilityRules guaranteeing thatcomposition does notjeopardize good propertiesof constituent components
  47. 47.  From Programs to Systems  System Design  Basic Technologies  Applications Three Grand ChallengesO  Marry Physicality and ComputationVE  Component-based DesignRV  AdaptivityIE  A Vision for CSW 47
  48. 48. Strong AI Vision – Matching Human Intelligence Considers that human intelligence can be so precisely described that it can be matched by a machine Supercomputers are used to accomplish specific problem solving or reasoning tasks IBM “WATSON” (2011) The Jeopardy!-playing IBM Deep Blue (1997) question answering system
  49. 49. Adaptivity – Coping with Uncertainty Learning CONTROLLER Objective Management PlanningParameter System state steering Mitigation HW failures SYSTEM Security Threats Desi Varying ET gn Error Varying Load s
  50. 50. Adaptivity – Coping with UncertaintySystems must provide services meeting given requirements in interactionwith uncertain and non predictable environments: External environment  non deterministic behavior e.g. varying throughput, attacks Hardware platforms  Variability due to manufacturing errors or aging  Varying execution times due to layering, caches, speculative execution Lower BCET WCET Upper Bound Bound Execution Times Possible ET Estimated ET
  51. 51. Adaptivity – Critical vs. Best Effort Engineering Two diverging design paradigmsBAD STATES ERROR STATES     Critical systems engineering based Best effort engineeringon worst-case analysis and static based on average case analysisresource reservation e.g. hard real- and dynamic resourcetime approaches, massive management e.g. Quality ofredundancy. Service for optimizing speed, memory, bandwidth, power.Leads to over-dimensioned systems No guaranteed availability
  52. 52. Adaptivity – Enhancing PredictabilitySeparation between critical and best-effort designs is not anymoreaffordable. In a car  more than 50 ECU’s each designed separately  federated architectures by using networks  poor global reliability and high development costs1. Predictability through determinism - reduce intrinsic and estimateduncertainty  Simplify HW architectures e.g. no caches, no out-of-order execution  Time-deterministic observable behavior e.g. time triggered systems2. Adaptive architectures integrating both critical and best-effort servicessharing an amount of global resources used  first and foremost for ensuring critical services  secondarily, for meeting quality of service requirements for non critical services
  53. 53. Adaptivity – Adaptive Control Learning Movie would have been better … Management of objectives Go to: 1) Stadium 2) Movie 3) Restaurant Planning 53
  54. 54.  From Programs to Systems  System Design  Basic Technologies  Applications Three Grand ChallengesO  Marry Physicality and ComputationVE  Component-based DesignRV  AdaptivityIE  A Vision for CSW 54
  55. 55. A Vision for Computer ScienceComputer Science  Is a young and rapidly evolving discipline due to exponential progress of technology and applications  Focuses on building systems and thus system design is central to the discipline  Existing models of computation should be extended to encompass physicality – physical resources e.g. memory, time, energy should become first class concepts  Complements and enriches our knowledge with theory and models that allow a deeper understanding of discrete dynamic systems  Proposes a constructive and operational view of the world which complements the classic declarative approach adopted by Physics
  56. 56. A Vision for Computer Science – The Frontiers of CS Mathematics LogicPhysics Computer Biology Science
  57. 57. A Vision for Computer Science – The Frontiers of CS Physics Computer Sc.• Deals with phenomena of the • Deals with the representation, « real » physical world transformation and (transformations of matter and transmission of information energy) • Focuses mainly on the• Focuses mainly on the construction of systems discovery of physical laws. • Computing systems –• Physical systems – Analytic Machines models • Discrete mathematics – Logic• Continuous mathematics• Differential equations - • Automata, Algorithms, robustness Complexity Theory• Predictability for classical • Verification, Testing, Physics • Young fast evolving discipline• Mature discipline 57
  58. 58. A Vision for Computer Science – The Frontiers of CSArtificial vs. Natural IntelligenceLiving organisms intimately combine interacting physical and computationalphenomena that have a deep impact on their development and evolution Shared characteristics with computing systems use of memory distinction between hardware and software use of languages Remarkable differences : robustness of computation built-in mechanisms for adaptivity emergence of abstractions – conceptsInteractions and cross-fertilization Non von Neumann computing  Neuromorphic and Cognitive Computing CAD methods&tools  Synthetic Biology
  59. 59. A Vision for Computer Science – The Frontiers of CS Sentience Abstraction Emotion Robust Computation LearningData Recognition Data Synthesis Mathematics
  60. 60. A Vision for Computer Science – Research in CS
  61. 61. A Vision for Computer Science – Research in CSThe Business as Usual SyndromeFollowing beaten tracks rather taking the risk of exploringnew ideasThe Hype SyndromeAll of the following were once hyped as mainbreakthroughs in CS: Artificial Intelligence, Fifth Generation Computers, Program Synthesis, True Concurrency, Program proofsThe Nice Theory Syndrome The proper goal of theory in any field is to make models that accurately describe real systems. These models should help system builders do their jobs better. Unfortunately, the current scope and focus of research in CS fail to address basic problems raised by system design and engineering
  62. 62. A Vision for Computer Science – Research in CSNice theory is not always practically relevant“Make everything as simple as possible, but not simpler”“….. in the academic world the theories that are more likely toattract a devoted following are those that best allow a clever butnot very original young man to demonstrate his cleverness.”…..but we need nice theory for solving practically relevantproblems“Perfection is reached not when there is no longer anything toadd, but when there is no longer anything to take away”
  63. 63. A Vision for Computer Science – Research in CSIs it possible to find a mathematically elegant and still practicabletheoretical framework for CS?Physics and Biology study a given “reality” The key issue is discovering laws governing phenomena "The most incomprehensible thing about the world is that it is at all comprehensible."Computer Science deals with building artifacts The key issue is Constructivity that is the capability to build cost-effectively correct systems
  64. 64. A Vision for Computer Science – Teaching CS Prepare students to deal with constant change induced by technology and applications Learn principles rather than facts (foundations, architectures, protocols, compilers, simulation …) Think in terms of systems (design process, tools, interaction with users and physical environment) Keep aware of limitations of existing theory of computing Introduce principles, paradigms, techniques from Control Theory and Electrical Engineering Put emphasis on information and computation as universal concepts applicable not only to computers Provide the background for triggering critical thinking, understanding and mastering the digital world.
  65. 65. The World without ICT Thank You

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