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Douglas C. [email protected]
Elastic Software Infrastructure to
Support the Industrial Internet
Institute for Software Integrated Systems
Vanderbilt University
Nashville, TN
RTI Webinar Series, October 23rd, 2013
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• Context & terminology
• Prior R&D progress
• Current R&D trends & challenges
• A promising solution
• Concluding remarks
Outline of Presentation
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Outline of Presentation
• Context & terminology
• Prior R&D progress
• Current R&D trends & challenges
• A promising solution
• Concluding remarks
4
• The Industrial Internet is a term coined by GE that refers to the integration of complex physical machinery with networked sensors & software
Overview of the Industrial Internet
en.wikipedia.org/wiki/Industrial_Internet
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• 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
Overview of the Industrial Internet
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• 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)
Overview of the Industrial Internet
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• 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
Overview of the Industrial Internet
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• 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)
Overview of the Industrial Internet
9
• 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
Overview of the Industrial Internet
10
• 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
Overview of the Industrial Internet
At the heart of the Industrial Internet are cyber-physical systems & clouds
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• A cyber-physical system (CPS) features a tight coordination between the system’s computational & physical elements
Overview of Cyber-Physical Systems
en.wikipedia.org/wiki/Cyber-physical_system
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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
www.ge.com/stories/industrial-internet has other apt examples
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• 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)
This talk focuses on distributed CPSs rather than standalone CPSs
Overview of Cyber-Physical Systems
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• 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,
storage, applications, & services
Resource pooling
Rapid elasticity
Broad network access
On-demand self-service
Measuredservice
Overview of Cloud Computing
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• 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
Resource pooling
Rapid elasticity
Broad network access
On-demand self-service
Measuredservice
Overview of Cloud Computing
csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf
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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.
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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
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Overview of Cloud Computing
Some implementations of cloud computing may be at odds with CPS needs..
• 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
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Outline of Presentation
• Context & terminology
• Prior R&D progress
• Current R&D trends & challenges
• A promising solution
• Concluding remarks
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The designs of legacy CPSs tend to be:• Stovepiped• Proprietary • Brittle & non-adaptive• Expensive to develop & evolve• Vulnerable
From this design paradigm…
AirFrame
AP
Nav WTS
SPLnner IFF
FLIR
Cyclic Exec
Problem: Small changes can break nearly anything & everything
Prior R&D Progress for Cyber-Physical Systems
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…and this operational paradigm…
Uti
lity
Resources
Utility “Curve”
“Broken” “Works”
“Hard” Requirements
Prior R&D Progress for Cyber-Physical Systems
Problem: Lack of any resource can break nearly everything
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
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…and this operational paradigm…
This is not at all what we think of as a computing cloud!
Prior R&D Progress for Cyber-Physical Systems
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
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Information Backbone
…to this design paradigm…
Event Channel
ReplicationService
PLanner IFF FLIR
AirFrame
AP Nav WTS 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
Result: changing requirements & environments can be handled more flexibly
Prior R&D Progress for Cyber-Physical Systems
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…and this operational paradigm…
Resources
Uti
lity
Desired Utility Curve
“Working Range”
“Softer” Requirements
Result: better support for operations with scarce/contended resources
Prior R&D Progress for Cyber-Physical Systems
• Ensure acceptable end-to-end QoS, e.g.,• Minimize latency, jitter, & footprint• Minimize priority inversions
• Resources are allocated/managed dynamically & can be shared
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…and this operational paradigm…
Some CPS operating platforms have much in common with computing clouds
• Ensure acceptable end-to-end QoS, e.g.,• Minimize latency, jitter, & footprint• Minimize priority inversions
• Resources are allocated/managed dynamically & can be shared
Prior R&D Progress for Cyber-Physical Systems
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…and this operational paradigm…
See www.dre.vanderbilt.edu/~schmidt/JSS-DRM.pdf for more info
• Ensure acceptable end-to-end QoS, e.g.,• Minimize latency, jitter, & footprint• Minimize priority inversions
• Resources are allocated/managed dynamically & can be shared
Prior R&D Progress for Cyber-Physical Systems
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Key solution space challenges• Enormous accidental & inherent
complexities• Continuous evolution & change• Highly heterogeneous platform,
language, & tool environments
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
New Challenge: Ultra-Large-Scale Cyber-Physical Systems
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Key solution space challenges• Enormous accidental & inherent
complexities• Continuous evolution & change• Highly heterogeneous platform,
language, & tool environments
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
Mapping problem space requirements to solution space artifacts is very hard!
New Challenge: Ultra-Large-Scale Cyber-Physical Systems
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Key solution space challenges• Enormous accidental & inherent
complexities• Continuous evolution & change• Highly heterogeneous platform,
language, & tool environments
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
New Challenge: Ultra-Large-Scale Cyber-Physical Systems
See www.dre.vanderbilt.edu/~schmidt/PDF/FOME-HCDS-paper.pdf for more
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Key solution space challenges• Enormous accidental & inherent
complexities• Continuous evolution & change• Highly heterogeneous platform,
language, & tool environments
New Challenge: Ultra-Large-Scale Cyber-Physical Systems
Ultra-Large-Scale CPSs are well beyond scope of today’s computing clouds
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
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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
New Challenge: Ultra-Large-Scale Cyber-Physical Systems
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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
New Challenge: Ultra-Large-Scale Cyber-Physical Systems
“Gentlemen, we have run
out of money. It is time to start
thinking.”
en.wikiquote.org/wiki/Talk:Winston_Churchill
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Outline of Presentation
• Context & terminology
• Prior R&D progress
• Current R&D trends & challenges
• A promising solution
• Concluding remarks
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Convenient Trend: Elastic Hardware Platforms• “Elastic hardware” based on
multi-core & distributed-core architectures now available at reasonable prices
en.wikipedia.org/wiki/Elasticity_(cloud_computing) has more info
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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 & auto-scaling data processing at runtime – Goal is to add/utilize more
hardware without changing application business logic or configurations
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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 & auto-scaling data processing at runtime
• Current focus of elastic hardware is largely on web hosting applications in public cloud environments
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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 & auto-scaling data processing at runtime
• Current focus of elastic hardware is largely on web hosting applications in public cloud environments
Elastic hardware is necessary—but not sufficient—for elastic CPS applications
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Impediments to Applying Elastic Hardware for CPSs
• Inadequate programming models– Complicated & obtrusive APIs– Can’t use hardware predictably
& scalably
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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Impediments to Applying Elastic Hardware for CPSs
• Inadequate programming models
• Inadequate knowledge of real-time, concurrency, & networking – e.g., high probability of race
conditions, deadlocks, priority inversion, & missed deadlines
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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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 SCADA Systems Air Traffic Mgmt Aerospace
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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 – e.g., lack of system-wide control
over key QoS impacting resource usage & end-to-end data deliver semantics
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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Impediments to Applying Elastic Hardware for CPSs
• Inadequate programming models– 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 distributed-core 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 semanticsSome impediments affect many types of systems, some mostly
affect CPSs
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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Key Research Challenges for Elastic CPSs
Meeting these challenges requires rethinking some cloud computing tenets
1. Precise auto-scaling of resources with a system-wide end-to-end focus
2. Flexible optimization algorithms to balance real-time constraints with cost & other goals
3. Improved fault-tolerance fail-over that supports real-time requirements
4. Data provisioning & load balancing algorithms that consider physical properties of computations & storage
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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1. Precise auto-scaling of resources with a system-wide end-to-end focus– State-of-the-art in auto-
scaling algo rithms manage services in isolation• CPSs require auto-
scaling algo rithms to operate on end-to-end task chains
Key Research Challenges for Elastic CPSs
CPU utilization
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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1. Precise auto-scaling of resources with a system-wide end-to-end focus– State-of-the-art in auto-
scaling algo rithms manage services in isolation
– Physical stability & safety properties may require exceedingly complex analyses• e.g., reachability of
hybrid cyber-physical states
Key Research Challenges for Elastic CPSs
CPU utilization
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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2. Flexible optimization algorithms to balance real-time 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
Key Research Challenges for Elastic CPSs
Cos
t
Response Time Scalability
Multi-dimensional Resource Management
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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2. Flexible optimization algorithms to balance real-time 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
Key Research Challenges for Elastic CPSs
Cos
t
Response Time Scalability
Multi-dimensional Resource Management
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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3. Improved fault-tolerance fail-over that supports real-time requirements– Some cloud platforms
tolerate faults for provisioned re sources• This is insufficient for
CPSs where real-time fault-tolerance of end-to-end task chains must be met simultaneously
Key Research Challenges for Elastic CPSsISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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3. Improved fault-tolerance 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
Key Research Challenges for Elastic CPSsISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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4. Data provisioning & load balancing algorithms that consider physical properties of computations & storage– CPSs generate load on
a computing clouddue to physical stimuli
Key Research Challenges for Elastic CPSs
social network linkages
geographic associations
power consumption
cache affinity
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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4. Data provisioning & load balancing algorithms that consider physical properties of computations & storage– CPSs generate load on
a computing clouddue to physical stimuli
– To build more scalable & high-performance CPSs, algorithms & techniques are needed to • Exploit physical
characteristics of data & computation
Key Research Challenges for Elastic CPSs
social network linkages
geographic associations
power consumption
cache affinity
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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4. Data provisioning & load balancing algorithms that consider physical properties of computations & storage– CPSs generate load on
a computing clouddue 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
Key Research Challenges for Elastic CPSs
social network linkages
geographic associations
power consumption
cache affinity
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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4. Data provisioning & load balancing algorithms that consider physical properties of computations & storage– CPSs generate load on
a computing clouddue 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
Key Research Challenges for Elastic CPSs
social network linkages
geographic associations
power consumption
cache affinity
We need a holistic solution that provides an elastic CPS software infrastructure
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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Outline of Presentation
• Context & terminology
• Prior R&D progress
• Current R&D trends & challenges
• A promising solution
• Concluding remarks
55
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
Requirements for Elastic CPS Software Infrastructure
• Flexibility – Loosely coupled components that can be analyzed, replaced, reused, distributed, & parallelized dependably
Middleware
Dynamic Discovery
Load Balancing
Data DistributionLow Latency
Dependability
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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 Middle
ware
Dynamic Discovery
Load Balancing
Data DistributionLow Latency
Dependability
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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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
Middleware
Dynamic Discovery
Load Balancing
Data DistributionLow Latency
Dependability
ISR Processing SCADA Systems Air Traffic Mgmt 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
• Uniformity – Seamless (ideally standards-based) support for multi-core & distributed-core
• Scalability – Static & dynamic load balancing ensures best & dependable utilization of available elastic hardware resources
Middleware
Dynamic Discovery
Load Balancing
Data DistributionLow Latency
Dependability
ISR Processing SCADA Systems Air Traffic Mgmt 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
• Scalability – Static & dynamic load balancing ensures best & dependable utilization of available elastic hardware resources
Middleware
Dynamic Discovery
Load Balancing
Data DistributionLow Latency
Dependability
Middleware is a key element of elastic CPS software infrastructure
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
60
Key Layers of CPS Software Infrastructure
Provide mechanisms to manage end-system resources, e.g., CPU scheduling, inter-process communication, memory management, & file systems
Domain-SpecificServices
CommonMiddleware
ServicesDistributionMiddleware
Host InfrastructureMiddleware
Operating Systems &
Protocols
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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Domain-SpecificServices
CommonMiddleware
ServicesDistributionMiddleware
Host InfrastructureMiddleware
Operating Systems &
Protocols
Encapsulates & enhances native OS mechanisms to create reusable network programming components
Key Layers of CPS Software InfrastructureISR Processing SCADA Systems Air Traffic Mgmt Aerospace
62
Domain-SpecificServices
CommonMiddleware
ServicesDistributionMiddleware
Host InfrastructureMiddleware
Operating Systems &
Protocols
Defines higher-level programming models whose reusable APIs & components automate & extend native OS capabilities across distribution boundaries
Key Layers of CPS Software InfrastructureISR Processing SCADA Systems Air Traffic Mgmt Aerospace
63
Domain-SpecificServices
CommonMiddleware
ServicesDistributionMiddleware
Host InfrastructureMiddleware
Operating Systems &
Protocols
Augment distribution middleware by defining higher-level domain-independent services that focus on programming “business logic”
Key Layers of CPS Software InfrastructureISR Processing SCADA Systems Air Traffic Mgmt Aerospace
64
Domain-SpecificServices
CommonMiddleware
ServicesDistributionMiddleware
Host InfrastructureMiddleware
Operating Systems &
Protocols
Tailored to requirements of particular domains, such as SCADA, avionics, aerospace, vehtronics, C4ISR, air traffic management, integrated healthcare, etc.
Key Layers of CPS Software InfrastructureISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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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
en.wikipedia.org/wiki/Data_Distribution_Service has a good DDS overview
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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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• Data-Centric Publish-
Subscribe (DCPS) & Data Local Reconstruction Layer (DLRL)
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
67
Global Data Space
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
Data Reader
Data Writer
Data Writer
Data Reader
Data Reader
Subscriber Publisher Subscriber
Domain Participant
Topic Topic
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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Subscriber Publisher Subscriber
Global Data Space
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
Topic Topic
Data Reader
Data Writer
Data Writer
Data Reader
Data Reader
Domain Participant
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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
DDS mainly provides distribution middleware & common middleware services
Global Data Space
Topic Topic
Subscriber Publisher Subscriber
Data Reader
Data Writer
Data Writer
Data Reader
Data Reader
Domain Participant
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
70
• 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
Promising Elastic CPS Middleware: DDSISR Processing SCADA Systems Air Traffic Mgmt Aerospace
71
• 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
Promising Elastic CPS Middleware: DDSISR Processing SCADA Systems Air Traffic Mgmt Aerospace
72
Subscriber
• 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
Data Reader
R
Data Writer
R
S1
S2
S3
S4
S5
S6
S7
S6 S5 S4 S3 S2 S1S7 S7X
HISTORY
RELIABILITYCOHERENCY
RESOURCE LIMITS
LATENCY
TopicR
Publisher
Promising Elastic CPS Middleware: DDS
www.dre.vanderbilt.edu/~schmidt/PDF/CrossTalk-2008-final.pdf
Subscriber
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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
DDS’s request/offered (RxO) model matches QoS policies between pub & sub
Subscriber
Data Reader
Topic
Domain Participant
RequestedQoS
RequestedQoS
RequestedQoS
OfferedQoS
OfferedQoS
Subscriber
Data Reader
Topic
Domain Participant
OfferedQoS
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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 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 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 open-source form• Many opportunities
for researchers to influence DDS standard & implementations
See www.dre.vanderbilt.edu/~schmidt/PDF/DDS-WAN.pdf for recent paper
ISR Processing SCADA Systems Air Traffic Mgmt Aerospace
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 open-source form• Many opportunities
for researchers to influence DDS standard & implementations
• DDS is used in many CPS research projects & production systems
ISR Processing 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 open-source 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 SCADA Systems Air Traffic Mgmt Aerospace
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Outline of Presentation
• Context & terminology
• Prior R&D progress
• Current R&D trends & challenges
• A promising solution
• Concluding remarks
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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
Key characteristics of computing clouds for CPS are multi-tenancy & elasticity
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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
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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
www.industrialinternet.com/blog/three-qs-professor-douglas-schmidt/
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Concluding Remarks
www.coursera.org/course/posa
“Big breakthroughs often happen when
what is suddenly possible meets what is
desperately necessary”
– Thomas Friedman
• 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
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• 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 & paradigmsfor computing clouds that support CPS
The NSF CC4CPS workshop report will be available later this year
Additional Information
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Ultra-large-scale (ULS) systems are socio-technical ecosystems comprised of software-reliant 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
• … etc …
www.sei.cmu.edu/uls
See blog.sei.cmu.edu for more discussions of software R&D activities
Additional Information
86
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
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
NRC Report Critical Code: Software Producibility for Defense (2010)
See blog.sei.cmu.edu for more discussions of software R&D activities
Additional Information
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
www.dre.vanderbilt.edu/~schmidt/ISIS-research.pdf has more info on ISIS