elastic software infrastructure to support the industrial internet

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

mailto:[email protected]

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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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• 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

http://en.wikipedia.org/wiki/Industrial_Internet

http://en.wikipedia.org/wiki/Industrial_Internet

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• It combines fields such as machine learning, big data, the Internet of things, & machine-to-machine communication to


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• Connect machines embedded with sensors to other machines (& end users)


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• Enable access & control of mechanical devices


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• Extract data from these devices, make sense of it, & deliver the right information to the right people at the right time (& in real-time)


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• Derive some form of value in terms of improved utility, & cost savings


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• Derive some form of value in terms of improved utility, & cost savings


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

http://en.wikipedia.org/wiki/Cyber-physical_system

http://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

http://www.ge.com/stories/industrial-internet

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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


csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf

http://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


• 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


• 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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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


• 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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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


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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This is not at all what we think of as a computing cloud!


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

http://www.takeourword.com/images/persistence-of-memory.jpg

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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


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Resources

Uti

lity

Desired Utility Curve

“Working Range”

“Softer” Requirements

Result: better support for operations with scarce/contended resources


• 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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Some CPS operating platforms have much in common with computing clouds




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See www.dre.vanderbilt.edu/~schmidt/JSS-DRM.pdf for more info




http://www.dre.vanderbilt.edu/~schmidt/JSS-DRM.pdf

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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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(sub)systems

Mapping problem space requirements to solution space artifacts is very hard!


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(sub)systems


See www.dre.vanderbilt.edu/~schmidt/PDF/FOME-HCDS-paper.pdf for more

http://www.dre.vanderbilt.edu/~schmidt/PDF/FOME-HCDS-paper.pdf



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Ultra-Large-Scale CPSs are well beyond scope of today’s computing clouds



(sub)systems

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(sub)systems





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(sub)systems





“Gentlemen, we have run

out of money. It is time to start

thinking.”

en.wikiquote.org/wiki/Talk:Winston_Churchill

http://en.wikiquote.org/wiki/Talk:Winston_Churchill

http://en.wikiquote.org/wiki/Talk:Winston_Churchill

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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

http://en.wikipedia.org/wiki/Elasticity_(cloud_computing)



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• 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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• 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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• 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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• Inadequate programming models

• Inadequate knowledge of real-time, concurrency, & networking – e.g., high probability of race

conditions, deadlocks, priority inversion, & missed deadlines


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• Inadequate knowledge of real-time, concurrency, & networking

• Inadequate mechanisms to transition seamlessly from multi- to distributed-core environments


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• Inadequate knowledge of real-time, concurrency, & networking


• 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


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• 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 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


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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


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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


CPU utilization


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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


CPU utilization


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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


Cos

t

Response Time Scalability

Multi-dimensional Resource Management


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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


Cos

t

Response Time Scalability

Multi-dimensional Resource Management


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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


social network linkages

geographic associations

power consumption

cache affinity


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– To build more scalable & high-performance CPSs, algorithms & techniques are needed to • Exploit physical

characteristics of data & computation




power consumption

cache affinity


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• Improve the distribution of work in a computing cloud




power consumption

cache affinity


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• Improve the distribution of work in a computing cloud




power consumption

cache affinity

We need a holistic solution that provides an elastic CPS software infrastructure


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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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• Adaptability – Provide APIs that adapt to existing code, rather than always having to adapt code to an API Middle

ware

Dynamic Discovery

Load Balancing


Dependability


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• 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


Dependability


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• Scalability – Static & dynamic load balancing ensures best & dependable utilization of available elastic hardware resources

Middleware

Dynamic Discovery

Load Balancing


Dependability


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• Scalability – Static & dynamic load balancing ensures best & dependable utilization of available elastic hardware resources

Middleware

Dynamic Discovery

Load Balancing


Dependability

Middleware is a key element of elastic CPS software infrastructure


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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


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CommonMiddleware



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

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CommonMiddleware



Operating Systems &

Protocols

Defines higher-level programming models whose reusable APIs & components automate & extend native OS capabilities across distribution boundaries


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CommonMiddleware



Operating Systems &

Protocols

Augment distribution middleware by defining higher-level domain-independent services that focus on programming “business logic”


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CommonMiddleware



Operating Systems &

Protocols

Tailored to requirements of particular domains, such as SCADA, avionics, aerospace, vehtronics, C4ISR, air traffic management, integrated healthcare, etc.


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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


http://en.wikipedia.org/wiki/Data_Distribution_Service

http://en.wikipedia.org/wiki/Data_Distribution_Service

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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)


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Global Data Space


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


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Global Data Space





– 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


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– 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


Data Reader

Data Writer

Data Writer

Data Reader

Data Reader

Domain Participant


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• 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

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Promising Elastic CPS Middleware: DDSISR Processing SCADA Systems Air Traffic Mgmt Aerospace

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Subscriber


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


http://www.dre.vanderbilt.edu/~schmidt/PDF/CrossTalk-2008-final.pdf

http://www.dre.vanderbilt.edu/~schmidt/PDF/CrossTalk-2008-final.pdf

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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


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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


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Promising Elastic CPS Middleware: DDS• DDS is an OMG standard

that itself is based on many associated open standards


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• 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


http://www.dre.vanderbilt.edu/~schmidt/PDF/DDS-WAN.pdf

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• DDS is used in many CPS research projects & production systems


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• DDS is used in many CPS research projects & production systems

• portals.omg.org/dds provides more info on DDS activities & projects


http://portals.omg.org/dds

http://portals.omg.org/dds

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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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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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CPSs in cloud environments is hard without adequate support from elastic software infrastructure


• There are many hard research challenges remaining

www.industrialinternet.com/blog/three-qs-professor-douglas-schmidt/

http://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


• There are many hard research challenges remaining

http://www.coursera.org/course/posa

http://www.coursera.org/course/posa

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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

http://www.isis.vanderbilt.edu/workshops/cc4cps

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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


http://blog.sei.cmu.edu/

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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


http://blog.sei.cmu.edu/

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• 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

http://www.dre.vanderbilt.edu/~schmidt/ISIS-research.pdf



elastic software infrastructure to support the industrial internet

Technology

internet of things

industrial internet

machine learning

cyberphysical systems

cyberphysical system

machine communication

big data

right people