Elastic Software Infrastructure to Support the Industrial Internet
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The document discusses the integration of complex physical machinery with networked sensors in the context of the Industrial Internet, highlighting significant advancements in cyber-physical systems (CPS) and cloud computing. It outlines the challenges and opportunities presented by ultra-large-scale CPS, emphasizing the need for improved programming models and fault-tolerant systems to effectively leverage 'elastic hardware'. The presentation also stresses the importance of rethinking cloud computing principles to address the unique requirements of elastic CPS applications, particularly in real-time environments.
Elastic Software Infrastructure to Support the Industrial Internet
- 1. Elastic Software Infrastructure to Support the Industrial Internet Douglas C. Schmidt [email protected] Institute for Software Integrated Systems Vanderbilt University Nashville, TN RTI Webinar Series, October 23rd, 2013
- 2. Outline of Presentation • Context & terminology • Prior R&D progress • Current R&D trends & challenges • A promising solution • Concluding remarks 2
- 3. Outline of Presentation • Context & terminology • Prior R&D progress • Current R&D trends & challenges • A promising solution • Concluding remarks 3
- 4. Overview of the Industrial Internet • The Industrial Internet is a term coined by GE that refers to the integration of complex physical machinery with networked sensors & software 4 en.wikipedia.org/wiki/Industrial_Internet
- 5. Overview of the Industrial Internet • The Industrial Internet is a term coined by GE that refers to the integration of complex physical machinery with networked sensors & software • It combines fields such as machine learning, big data, the Internet of things, & machine-to-machine communication to 5
- 6. Overview of the Industrial Internet • The Industrial Internet is a term coined by GE that refers to the integration of complex physical machinery with networked sensors & software • It combines fields such as machine learning, big data, the Internet of things, & machine-to-machine communication to • Connect machines embedded with sensors to other machines (& end users) 6
- 7. Overview of the Industrial Internet • The Industrial Internet is a term coined by GE that refers to the integration of complex physical machinery with networked sensors & software • It combines fields such as machine learning, big data, the Internet of things, & machine-to-machine communication to • Connect machines embedded with sensors to other machines (& end users) • Enable access & control of mechanical devices 7
- 8. Overview of the Industrial Internet • The Industrial Internet is a term coined by GE that refers to the integration of complex physical machinery with networked sensors & software • It combines fields such as machine learning, big data, the Internet of things, & machine-to-machine communication to • Connect machines embedded with sensors to other machines (& end users) • Enable access & control of mechanical devices • Extract data from these devices, make sense of it, & deliver the right information to the right people at the right time (& in real-time) 8
- 9. Overview of the Industrial Internet • The Industrial Internet is a term coined by GE that refers to the integration of complex physical machinery with networked sensors & software • It combines fields such as machine learning, big data, the Internet of things, & machine-to-machine communication to • Connect machines embedded with sensors to other machines (& end users) • Enable access & control of mechanical devices • Extract data from these devices, make sense of it, & deliver the right information to the right people at the right time (& in real-time) • Derive some form of value in terms of improved utility, & cost savings 9
- 10. Overview of the Industrial Internet • The Industrial Internet is a term coined by GE that refers to the integration of complex physical machinery with networked sensors & software • It combines fields such as machine learning, big data, the Internet of things, & machine-to-machine communication to • Connect machines embedded with sensors to other machines (& end users) • Enable access & control of mechanical devices • Extract data from these devices, make sense of it, & deliver the right information to the right people at the right time (& in real-time) • Derive some form of value in terms of improved utility, & cost savings 10 At the heart of the Industrial Internet are cyber-physical systems & clouds
- 11. Overview of Cyber-Physical Systems • A cyber-physical system (CPS) features a tight coordination between the system’s computational & physical elements 11 en.wikipedia.org/wiki/Cyber-physical_system
- 12. Overview of Cyber-Physical Systems • A cyber-physical system (CPS) features a tight coordination between the system’s computational & physical elements • CPSs increasingly use networked processing elements to control physical, chemical, or biological processes or devices 12 www.ge.com/stories/industrial-internet has other apt examples
- 13. Overview of Cyber-Physical Systems • A cyber-physical system (CPS) features a tight coordination between the system’s computational & physical elements • CPSs increasingly use networked processing elements to control physical, chemical, or bi-ological processes or devices • In CPSs the ―right answer‖ delivered too late becomes the ―wrong answer‖ • i.e., dependability has a temporal dimension (& increasingly a security dimension) 13 This talk focuses on distributed CPSs rather than standalone CPSs
- 14. Overview of Cloud Computing • Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources • e.g., networks, servers, Measured service storage, applications, & services On-demand self-service 14 Resource pooling Rapid elasticity Broad network access
- 15. Overview of Cloud Computing • Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources • These resources can be Measured service rapidly provisioned & released with minimal management effort or service provider interaction On-demand self-service Resource pooling Rapid elasticity Broad network access 15 csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf
- 16. Overview of Cloud Computing • Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources • These resources can be rapidly provisioned & released with minimal management effort or service provider interaction • Cloud offerings enable ―economies of scale‖ via multi-tenancy & elasticity • e.g., run atop shared (often virtualized) data access, storage, hardware, software, middleware, etc. 16
- 17. Overview of Cloud Computing • Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources • These resources can be rapidly provisioned & released with minimal management effort or service provider interaction • Cloud offerings enable ―economies of scale‖ via multi-tenancy & elasticity • Cloud services don’t require users to know of the configuration & physical location of the computing & communication infrastructure delivering services • Similar to traditional utilities, such as power grids, water, sewer, as well as datacom/telecom service providers 17
- 18. Overview of Cloud Computing • Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources • These resources can be rapidly provisioned & released with minimal management effort or service provider interaction • Cloud offerings enable ―economies of scale‖ via multi-tenancy & elasticity • Cloud services don’t require users to know of the configuration & physical location of the computing & communication infrastructure delivering services • Similar to traditional utilities, such as power grids, water, sewer, as well as datacom/telecom service providers 18 Some implementations of cloud computing may be at odds with CPS needs..
- 19. Outline of Presentation • Context & terminology • Prior R&D progress • Current R&D trends & challenges • A promising solution • Concluding remarks 19
- 20. Prior R&D Progress for Cyber-Physical Systems From this design paradigm… Nav Air Frame WTS AP FLIR SPLnner IFF Cyclic Exec The designs of legacy CPSs tend to be: • Stovepiped • Proprietary • Brittle & non-adaptive • Expensive to develop & evolve • Vulnerable 20 Problem: Small changes can break nearly anything & everything
- 21. Prior R&D Progress for Cyber-Physical Systems …and this operational paradigm… Utility “Curve” Utility Real-time QoS requirements for legacy CPSs: • Ensure predictable end-to-end QoS, e.g., • Bound latency, jitter, & footprint • Bound priority inversions • Allocate & manage resources statically & avoid sharing “Broken” “Works” Resources “Hard” Requirements 21 Problem: Lack of any resource can break nearly everything
- 22. Prior R&D Progress for Cyber-Physical Systems …and this operational paradigm… Real-time QoS requirements for legacy CPSs: • Ensure predictable end-to-end QoS, e.g., • Bound latency, jitter, & footprint • Bound priority inversions • Allocate & manage resources statically & avoid sharing 22 This is not at all what we think of as a computing cloud!
- 23. Prior R&D Progress for Cyber-Physical Systems …to this design paradigm… Air Frame AP Event Channel PLanner Nav WTS Replication Service IFF FLIR Information Backbone The designs of today’s leading-edge CPSs tend to be more: • Layered & componentized • Standards- & COTS-based • Robust to failures & adaptive to operating conditions • Cost effective to evolve & retarget 23 Result: changing requirements & environments can be handled more flexibly
- 24. Prior R&D Progress for Cyber-Physical Systems …and this operational paradigm… Utility Desired Utility Curve “Working Range” Resources • Ensure acceptable end-to-end QoS, e.g., “Softer” Requirements • Minimize latency, jitter, & footprint • Minimize priority inversions • Resources are allocated/managed dynamically & can be shared 24 Result: better support for operations with scarce/contended resources
- 25. Prior R&D Progress for Cyber-Physical Systems …and this operational paradigm… • Ensure acceptable end-to-end QoS, e.g., • Minimize latency, jitter, & footprint • Minimize priority inversions • Resources are allocated/managed dynamically & can be shared 25 Some CPS operating platforms have much in common with computing clouds
- 26. Prior R&D Progress for Cyber-Physical Systems …and this operational paradigm… • Ensure acceptable end-to-end QoS, e.g., • Minimize latency, jitter, & footprint • Minimize priority inversions • Resources are allocated/managed dynamically & can be shared 26 See www.dre.vanderbilt.edu/~schmidt/JSS-DRM.pdf for more info
- 27. New Challenge: Ultra-Large-Scale Cyber-Physical Systems Key problem space challenges • Dynamic behavior • Transient overloads • Time-critical tasks • Context-specific requirements • Resource conflicts • Interdependence of (sub)systems • Integration with legacy (sub)systems Key solution space challenges • Enormous accidental & inherent complexities • Continuous evolution & change • Highly heterogeneous platform, language, & tool environments 27
- 28. New Challenge: Ultra-Large-Scale Cyber-Physical Systems Key problem space challenges • Dynamic behavior • Transient overloads • Time-critical tasks • Context-specific requirements • Resource conflicts • Interdependence of (sub)systems • Integration with legacy (sub)systems Key solution space challenges • Enormous accidental & inherent complexities • Continuous evolution & change • Highly heterogeneous platform, language, & tool environments Mapping problem space requirements28 solution space artifacts is very hard! to
- 29. New Challenge: Ultra-Large-Scale Cyber-Physical Systems Key problem space challenges • Dynamic behavior • Transient overloads • Time-critical tasks • Context-specific requirements • Resource conflicts • Interdependence of (sub)systems • Integration with legacy (sub)systems Key solution space challenges • Enormous accidental & inherent complexities • Continuous evolution & change • Highly heterogeneous platform, language, & tool environments 29 See www.dre.vanderbilt.edu/~schmidt/PDF/FOME-HCDS-paper.pdf for more
- 30. New Challenge: Ultra-Large-Scale Cyber-Physical Systems Key problem space challenges • Dynamic behavior • Transient overloads • Time-critical tasks • Context-specific requirements • Resource conflicts • Interdependence of (sub)systems • Integration with legacy (sub)systems Key solution space challenges • Enormous accidental & inherent complexities • Continuous evolution & change • Highly heterogeneous platform, language, & tool environments 30 Ultra-Large-Scale CPSs are well beyond scope of today’s computing clouds
- 31. New Challenge: Ultra-Large-Scale Cyber-Physical Systems Key problem space challenges • Dynamic behavior • Transient overloads • Time-critical tasks • Context-specific requirements • Resource conflicts • Interdependence of (sub)systems • Integration with legacy (sub)systems Key solution space challenges • Enormous accidental & inherent complexities • Continuous evolution & change • Highly heterogeneous platform, language, & tool environments 31
- 32. New Challenge: Ultra-Large-Scale Cyber-Physical Systems Key problem space challenges • Dynamic behavior ―Gentlemen, we • Transient overloads have • Time-critical tasks run out of money. It is time • Context-specific requirements to start • Resource conflicts thinking.‖ • Interdependence of (sub)systems • Integration with legacy (sub)systems Key solution space challenges • Enormous accidental & inherent complexities • Continuous evolution & change • Highly heterogeneous platform, language, & tool environments 32 en.wikiquote.org/wiki/Talk:Winston_Churchill
- 33. Outline of Presentation • Context & terminology • Prior R&D progress • Current R&D trends & challenges • A promising solution • Concluding remarks 33
- 34. Convenient Trend: Elastic Hardware Platforms • ―Elastic hardware‖ based on multi-core & distributed-core architectures now available at reasonable prices 34 en.wikipedia.org/wiki/Elasticity_(cloud_computing) has more info
- 35. Convenient Trend: Elastic Hardware Platforms • • ―Elastic hardware‖ based on multi-core & distributed-core architectures now available at reasonable prices Elastic hardware has potential to substantially accelerate performance by parallelizing application work-loads & autoscaling data processing at runtime – Goal is to add/utilize more hardware without changing application business logic or configurations 35
- 36. Convenient Trend: Elastic Hardware Platforms • • • ―Elastic hardware‖ based on multi-core & distributed-core architectures now available at reasonable prices Elastic hardware has potential to substantially accelerate performance by parallelizing application work-loads & autoscaling data processing at runtime Current focus of elastic hardware is largely on web hosting applications in public cloud environments 36
- 37. Convenient Trend: Elastic Hardware Platforms • • • ―Elastic hardware‖ based on multi-core & distributed-core architectures now available at reasonable prices Elastic hardware has potential to substantially accelerate performance by parallelizing application work-loads & autoscaling data processing at runtime Current focus of elastic hardware is largely on web hosting applications in public cloud environments 37 Elastic hardware is necessary—but not sufficient—for elastic CPS applications
- 38. Impediments to Applying Elastic Hardware for CPSs • Inadequate programming models ISR Processing – Complicated & obtrusive APIs – Can’t use hardware predictably & scalably 38 SCADA Systems Air Traffic Mgmt Aerospace
- 39. Impediments to Applying Elastic Hardware for CPSs • • Inadequate programming models Inadequate knowledge of real-time, concurrency, & networking ISR Processing – e.g., high probability of race conditions, deadlocks, priority inversion, & missed deadlines 39 SCADA Systems Air Traffic Mgmt Aerospace
- 40. Impediments to Applying Elastic Hardware for CPSs • • • Inadequate programming models Inadequate knowledge of real-time, concurrency, & networking Inadequate mechanisms to transition seamlessly from multi- to distributed-core environments ISR Processing 40 SCADA Systems Air Traffic Mgmt Aerospace
- 41. Impediments to Applying Elastic Hardware for CPSs • • • • Inadequate programming models Inadequate knowledge of real-time, concurrency, & networking Inadequate mechanisms to transition seamlessly from multi- to distributed-core environments Inadequate quality-of-service (QoS) support at scale ISR Processing – e.g., lack of system-wide control over key QoS impacting resource usage & end-to-end data deliver semantics 41 SCADA Systems Air Traffic Mgmt Aerospace
- 42. Impediments to Applying Elastic Hardware for CPSs • Inadequate programming models ISR Processing SCADA Systems Air Traffic Mgmt Aerospace – Complicated & obtrusive APIs – Can’t use hardware predictably & scalably • Inadequate knowledge of real-time, concurrency, & networking – e.g., high probability of race conditions, deadlocks, priority inversion, & missed deadlines • • Inadequate mechanisms to transition seamlessly from multi- to distributedcore environments Inadequate quality-of-service (QoS) support at scale – e.g., lack of system-wide control over key QoS impacting resource usage & end-to-end data deliver semantics 42 Some impediments affect many types of systems, some mostly affect CPSs
- 43. Key Research Challenges for Elastic CPSs 1. Precise auto-scaling of ISR Processing SCADA Systems Air Traffic Mgmt Aerospace resources with a systemwide end-to-end focus 2. Flexible optimization algorithms to balance realtime constraints with cost & other goals 3. Improved fault-tolerance fail-over that supports realtime requirements 4. Data provisioning & load balancing algorithms that consider physical properties of computations & storage 43 Meeting these challenges requires rethinking some cloud computing tenets
- 44. Key Research Challenges for Elastic CPSs 1. Precise auto-scaling of ISR Processing resources with a systemwide end-to-end focus – State-of-the-art in autoscaling algo-rithms manage services in isolation • CPSs require autoscaling algo-rithms to operate on end-to-end task chains SCADA Systems Air Traffic Mgmt CPU utilization 44 Aerospace
- 45. Key Research Challenges for Elastic CPSs 1. Precise auto-scaling of ISR Processing resources with a systemwide end-to-end focus – State-of-the-art in autoscaling algo-rithms manage services in isolation – Physical stability & safety properties may require exceedingly complex analyses • e.g., reachability of hybrid cyber-physical states SCADA Systems Air Traffic Mgmt CPU utilization 45 Aerospace
- 46. Key Research Challenges for Elastic CPSs algorithms to balance realtime constraints with cost & other goals – CPS deployments must be schedulable on all resources acquired from cloud providers to ensure real-time response times, while optimizing desired objective functions • e.g., minimizing costs ISR Processing SCADA Systems Air Traffic Mgmt Aerospace Multi-dimensional Resource Management Cost 2. Flexible optimization 46
- 47. Key Research Challenges for Elastic CPSs algorithms to balance realtime constraints with cost & other goals – CPS deployments must be schedulable on all resources acquired from cloud providers to ensure real-time response times, while optimizing desired objective functions – Principled means are needed to co-schedule and/or per-form admission control & eviction of mixed-criticality task sets deployed on cloud resources ISR Processing SCADA Systems Air Traffic Mgmt Aerospace Multi-dimensional Resource Management Cost 2. Flexible optimization 47
- 48. Key Research Challenges for Elastic CPSs 3. Improved fault-tolerance ISR Processing fail-over that supports real-time requirements – Some cloud platforms tolerate faults for provisioned re-sources • This is insufficient for CPSs where realtime fault-tolerance of end-to-end task chains must be met simultaneously 48 SCADA Systems Air Traffic Mgmt Aerospace
- 49. Key Research Challenges for Elastic CPSs 3. Improved fault-tolerance ISR Processing fail-over that supports real-time requirements – Some cloud platforms tolerate faults for provisioned re-sources – Reasoning about the consequences of faults is an important open re-search area due to the complex & stochastic nature of many CPSs 49 SCADA Systems Air Traffic Mgmt Aerospace
- 50. Key Research Challenges for Elastic CPSs 4. Data provisioning & load balancing algorithms that consider physical properties of computations & storage – CPSs generate load on a computing cloud due to physical stimuli ISR Processing cache affinity geographic associations 50 SCADA Systems Air Traffic Mgmt Aerospace social network linkages power consumption
- 51. Key Research Challenges for Elastic CPSs 4. Data provisioning & load balancing algorithms that consider physical properties of computations & storage – CPSs generate load on a computing cloud due to physical stimuli – To build more scalable & high-performance CPSs, algorithms & techniques are needed to • Exploit physical characteristics of data & computation ISR Processing cache affinity geographic associations 51 SCADA Systems Air Traffic Mgmt Aerospace social network linkages power consumption
- 52. Key Research Challenges for Elastic CPSs 4. Data provisioning & load balancing algorithms that consider physical properties of computations & storage – CPSs generate load on a computing cloud due to physical stimuli – To build more scalable & high-performance CPSs, algorithms & techniques are needed to • Exploit physical characteristics of data & computation • Improve the distribution of work in a computing cloud ISR Processing cache affinity geographic associations 52 SCADA Systems Air Traffic Mgmt Aerospace social network linkages power consumption
- 53. Key Research Challenges for Elastic CPSs 4. Data provisioning & load balancing algorithms that consider physical properties of computations & storage – CPSs generate load on a computing cloud due to physical stimuli – To build more scalable & high-performance CPSs, algorithms & techniques are needed to • Exploit physical characteristics of data & computation • Improve the distribution of work in a computing cloud ISR Processing cache affinity geographic associations SCADA Systems Air Traffic Mgmt Aerospace social network linkages power consumption 53 We need a holistic solution that provides an elastic CPS software infrastructure
- 54. Outline of Presentation • Context & terminology • Prior R&D progress • Current R&D trends & challenges • A promising solution • Concluding remarks 54
- 55. Requirements for Elastic CPS Software Infrastructure • Flexibility – Loosely coupled components that can be analyzed, replaced, reused, distributed, & parallelized dependably ISR Processing SCADA Systems Air Traffic Mgmt Dynamic Discovery Load Balancing Dependability Middle ware Low Latency 55 Data Distribution Aerospace
- 56. Requirements for Elastic CPS Software Infrastructure • Flexibility – Loosely coupled components that can be analyzed, replaced, reused, distributed, & parallelized dependably • Adaptability – Provide APIs that adapt to existing code, rather than always having to adapt code to an API ISR Processing SCADA Systems Air Traffic Mgmt Dynamic Discovery Load Balancing Dependability Middle ware Low Latency 56 Data Distribution Aerospace
- 57. Requirements for Elastic CPS Software Infrastructure • Flexibility – Loosely coupled components that can be analyzed, replaced, reused, distributed, & parallelized dependably • Adaptability – Provide APIs that adapt to existing code, rather than always having to adapt code to an API • ISR Processing SCADA Systems Air Traffic Mgmt Dynamic Discovery Load Balancing Dependability Middle ware Uniformity – Seamless (ideally standards-based) support for multi-core & distributed-core Low Latency 57 Data Distribution Aerospace
- 58. Requirements for Elastic CPS Software Infrastructure • Flexibility – Loosely coupled components that can be analyzed, replaced, reused, distributed, & parallelized dependably • Adaptability – Provide APIs that adapt to existing code, rather than always having to adapt code to an API • • ISR Processing SCADA Systems Air Traffic Mgmt Dynamic Discovery Load Balancing Dependability Middle ware Uniformity – Seamless (ideally standards-based) support for multi-core & distributed-core Low Latency Scalability – Static & dynamic load balancing ensures best & dependable utilization of available elastic hardware resources 58 Data Distribution Aerospace
- 59. Requirements for Elastic CPS Software Infrastructure • Flexibility – Loosely coupled components that can be analyzed, replaced, reused, distributed, & parallelized dependably • Adaptability – Provide APIs that adapt to existing code, rather than always having to adapt code to an API • • Uniformity – Seamless (ideally standards-based) support for multi-core & distributed-core ISR Processing SCADA Systems Air Traffic Mgmt Aerospace Dynamic Discovery Load Balancing Dependability Middle ware Low Latency Data Distribution Scalability – Static & dynamic load balancing ensures best & dependable utilization of available elastic hardware resources 59 Middleware is a key element of elastic CPS software infrastructure
- 60. Key Layers of CPS Software Infrastructure ISR Processing SCADA Systems Provide mechanisms to manage end-system resources, e.g., CPU scheduling, inter-process communication, memory management, & file systems Air Traffic Mgmt Aerospace Domain-Specific Services Common Middleware Services Distribution Middleware Host Infrastructure Middleware Operating Systems & Protocols 60
- 61. Key Layers of CPS Software Infrastructure ISR Processing SCADA Systems Encapsulates & enhances native OS mechanisms to create reusable network programming components Air Traffic Mgmt Aerospace Domain-Specific Services Common Middleware Services Distribution Middleware Host Infrastructure Middleware Operating Systems & Protocols 61
- 62. Key Layers of CPS Software Infrastructure ISR Processing SCADA Systems Defines higher-level programming models whose reusable APIs & components automate & extend native OS capabilities across distribution boundaries Air Traffic Mgmt Aerospace Domain-Specific Services Common Middleware Services Distribution Middleware Host Infrastructure Middleware Operating Systems & Protocols 62
- 63. Key Layers of CPS Software Infrastructure ISR Processing SCADA Systems Augment distribution middleware by defining higherlevel domain-independent services that focus on programming ―business logic‖ Air Traffic Mgmt Aerospace Domain-Specific Services Common Middleware Services Distribution Middleware Host Infrastructure Middleware Operating Systems & Protocols 63
- 64. Key Layers of CPS Software Infrastructure ISR Processing SCADA Systems Tailored to requirements of particular domains, such as SCADA, avionics, aerospace, vehtronics, C4ISR, air traffic management, integrated healthcare, etc. Air Traffic Mgmt Aerospace Domain-Specific Services Common Middleware Services Distribution Middleware Host Infrastructure Middleware Operating Systems & Protocols 64
- 65. Promising Elastic CPS Middleware: DDS • The OMG Data Distribution Service (DDS) promotes a pattern language that yields loosely coupled, polyglot, evolvable, scalable, efficient & dependable CPSs ISR Processing SCADA Systems Air Traffic Mgmt Aerospace 65 en.wikipedia.org/wiki/Data_Distribution_Service has a good DDS overview
- 66. Promising Elastic CPS Middleware: DDS • The OMG Data Distribution Service (DDS) promotes a pattern language that yields loosely coupled, polyglot, evolvable, scalable, efficient & dependable CPSs – DDS supports relational & OO information modeling ISR Processing • Data-Centric Publish- Subscribe (DCPS) & Data Local Reconstruction Layer (DLRL) 66 SCADA Systems Air Traffic Mgmt Aerospace
- 67. Promising Elastic CPS Middleware: DDS • The OMG Data Distribution Service (DDS) promotes a pattern language that yields loosely coupled, polyglot, evolvable, scalable, efficient & dependable CPSs – DDS supports flat, relational, & OO information modeling – DDS global data space allows apps to read/write data anonymously & asynchronously, decoupled in space & time ISR Processing SCADA Systems Topic Data Reader Domain Participant Subscriber Global Data Space 67 Data Writer Air Traffic Mgmt Aerospace Topic Data Writer Publisher Data Reader Data Reader Subscriber
- 68. Promising Elastic CPS Middleware: DDS • The OMG Data Distribution Service (DDS) promotes a pattern language that yields loosely coupled, polyglot, evolvable, scalable, efficient & dependable CPSs – DDS supports flat, relational, & OO information modeling – DDS global data space allows apps to read/write data anonymously & asynchronously, decoupled in space & time – DDS pub/sub model allows apps to produce/consume information into/from the global data space ISR Processing SCADA Systems Topic Data Reader Domain Participant Subscriber Global Data Space 68 Data Writer Air Traffic Mgmt Aerospace Topic Data Writer Publisher Data Reader Data Reader Subscriber
- 69. Promising Elastic CPS Middleware: DDS • The OMG Data Distribution Service (DDS) promotes a pattern language that yields loosely coupled, polyglot, evolvable, scalable, efficient & dependable CPSs – DDS supports flat, relational, & OO information modeling – DDS global data space allows apps to read/write data anonymously & asynchronously, decoupled in space & time – DDS pub/sub model allows apps to produce/consume information into/from the global data space ISR Processing SCADA Systems Topic Data Reader Domain Participant Subscriber Data Writer Air Traffic Mgmt Aerospace Topic Data Writer Publisher Data Reader Data Reader Subscriber Global Data Space 69 DDS mainly provides distribution middleware & common middleware services
- 70. Promising Elastic CPS Middleware: DDS • DDS controls resource usage, end-to-end data delivery, & data availability via a rich set of QoS policies, e.g.: – Batching – Priority – Deadline – Data Durability – Redundancy – Data History ISR Processing 70 SCADA Systems Air Traffic Mgmt Aerospace
- 71. Promising Elastic CPS Middleware: DDS • DDS controls resource usage, end-to-end data delivery, & data availability via a rich set of QoS policies, e.g.: – Batching – Priority – Deadline – Data Durability – Redundancy – Data History ISR Processing 71 SCADA Systems Air Traffic Mgmt Aerospace
- 72. Promising Elastic CPS Middleware: DDS • DDS controls resource usage, end-to-end data delivery, & data availability via a rich set of QoS policies, e.g.: – Batching – Priority Data Writer – Deadline R – Data Durability – Redundancy Publisher – Data History ISR Processing SCADA Systems HISTORY Air Traffic Mgmt Aerospace RESOURCE LIMITS Topic R S1 Data Reader R S2 S3 S4 S5 Subscriber Subscri S6 S7 X S7 ber LATENCY S7 S6 S5 S4 S3 S2 S1 COHERENCY RELIABILITY 72 www.dre.vanderbilt.edu/~schmidt/PDF/CrossTalk-2008-final.pdf
- 73. Promising Elastic CPS Middleware: DDS • DDS controls resource usage, end-to-end data delivery, & data availability via a rich set of QoS policies, e.g.: – Batching – Priority – Deadline – Data Durability – Redundancy – Data History ISR Processing SCADA Systems Topic Data Reader Air Traffic Mgmt Aerospace Topic Requested Requested Requested QoS QoS QoS Subscriber Domain Participant Offered Data Reader Offered Offered QoS QoS QoS Subscriber Domain Participant 73 DDS’s request/offered (RxO) model matches QoS policies between pub & sub
- 74. Promising Elastic CPS Middleware: DDS • • DDS controls resource usage, end-to-end data delivery, & data availability via a rich set of QoS policies: – Batching – Priority – Deadline – Data Durability – Redundancy – Data History Bridges are available across technologies to expose relevant data to heterogeneous network protocols, without imposing changes into existing legacy systems ISR Processing 74 SCADA Systems Air Traffic Mgmt Aerospace
- 75. Promising Elastic CPS Middleware: DDS • DDS is an OMG standard that itself is based on many associated open standards ISR Processing 75 SCADA Systems Air Traffic Mgmt Aerospace
- 76. Promising Elastic CPS Middleware: DDS • • DDS is an OMG standard that itself is based on many associated open standards Key DDS implementations are now available in opensource form • Many opportunities for researchers to influence DDS standard & implementations ISR Processing SCADA Systems Air Traffic Mgmt Aerospace 76 See www.dre.vanderbilt.edu/~schmidt/PDF/DDS-WAN.pdf for recent paper
- 77. Promising Elastic CPS Middleware: DDS • • • DDS is an OMG standard that itself is based on many associated open standards Key DDS implementations are now available in opensource form • Many opportunities for researchers to influence DDS standard & implementations DDS is used in many CPS research projects & production systems ISR Processing 77 SCADA Systems Air Traffic Mgmt Aerospace
- 78. Promising Elastic CPS Middleware: DDS • • • • DDS is an OMG standard that itself is based on many associated open standards Key DDS implementations are now available in opensource form • Many opportunities for researchers to influence DDS standard & implementations DDS is used in many CPS research projects & production systems portals.omg.org/dds provides more info on DDS activities & projects ISR Processing 78 SCADA Systems Air Traffic Mgmt Aerospace
- 79. Outline of Presentation • Context & terminology • Prior R&D progress • Current R&D trends & challenges • A promising solution • Concluding remarks 79
- 80. Concluding Remarks • Despite advances in elastic hardware, deploying CPSs in cloud environments is hard without adequate support from elastic software infrastructure – It’s unlikely that public clouds will work for mission-critical Industrial Internet applications 80 Key characteristics of computing clouds for CPS are multi-tenancy & elasticity
- 81. Concluding Remarks • • Despite advances in elastic hardware, deploying CPSs in cloud environments is hard without adequate support from elastic software infrastructure Standards-based DDS middleware provides key open-source building-blocks to create a dependable elastic CPS software infrastructure 81 There are many hard research challenges remaining
- 82. Concluding Remarks • • • Despite advances in elastic hardware, deploying CPSs in cloud environments is hard without adequate support from elastic software infrastructure Standards-based DDS middleware provides key open-source building-blocks to create a dependable elastic CPS software infrastructure There are many hard research challenges remaining 82 www.industrialinternet.com/blog/three-qs-professor-douglas-schmidt/
- 83. Concluding Remarks • • • Despite advances in elastic hardware, deploying CPSs in cloud environments is hard without adequate support from elastic software infrastructure Standards-based DDS middleware provides key open-source building-blocks to create a dependable elastic CPS software infrastructure There are many hard research challenges remaining ―Big breakthroughs often happen when what is suddenly possible meets what is desperately necessary‖ – Thomas Friedman 83 www.coursera.org/course/posa
- 84. Additional Information See www.isis.vanderbilt.edu/workshops/cc4cps for info on an NSF workshop on Computing Clouds for Cyber-Physical Systems (CC4CPS) • Attended by ~50 researchers funded by the NSF • Topics of workshop included • Role of computing clouds in data • collection, integration, analysis, & mining for CPS • Roles of computing clouds in CPS control • Stability, safety, security, privacy, & reliability considerations in integrating cloud computing with CPS • Programming models & paradigms for computing clouds that support CPS 84 The NSF CC4CPS workshop report will be available later this year
- 85. Additional Information Ultra-large-scale (ULS) systems are sociotechnical ecosystems comprised of softwarereliant systems, people, policies, cultures, & economics that have unprecedented scale: • # of software & hardware elements • # of connections & interdependencies • # of computational elements • # of purposes & perception of purposes • # of routine processes & ―emergent behaviors‖ • # of (overlapping) policy domains & enforceable mechanisms • # of people involved in some way • Amount of data stored, accessed, & manipulated www.sei.cmu.edu/uls • … etc … 85 See blog.sei.cmu.edu for more discussions of software R&D activities
- 86. Additional Information NRC Report Critical Code: Software Producibility for Defense (2010) The report focuses on ensuring the DoD has the technical capacity & workforce to design, produce, assure, & evolve innovative software-reliant systems in a predictable manner, while effectively managing risk, cost, schedule, & complexity Sponsored by Office of the Secretary of Defense (OSD) with assistance from the National Science Foundation (NSF), & Office of Naval Research (ONR), www.nap.edu/openbook.php?record_id=12979&page=R1 86 See blog.sei.cmu.edu for more discussions of software R&D activities
- 87. Additional Information • The Institute for Software Integrated Systems (ISIS) was established at Vanderbilt in 1998 • Research at ISIS focuses on systems with deeply integrated software that are networked, embedded, & cyber-physical • Key research areas at ISIS: • Model-Integrated Computing • Middleware for distributed real-time & embedded (DRE) systems • Model-based engineering of cyber-physical systems • Wireless sensor networks • Systems security & privacy 87 www.dre.vanderbilt.edu/~schmidt/ISIS-research.pdf has more info on ISIS