Fog Computing - Iot-Inc
Transcript of Fog Computing - Iot-Inc
Fog Computing
Brian Sak, Technical Solutions Architect, Cisco
Salman Asadullah, Distinguished Systems Engineer, Cisco
Stewart Young, OSI Soft
November 20th, 2014 gogoNet Live
What is it and how will it change the way your company implements IoT?
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IoT Rapid Growth
Source: Cisco IBSG projections, UN Economic & Social Affairs http://www.un.org/esa/population/publications/longrange2/WorldPop2300final.pdf
6.307
6.721 6.894 7.347 7.83
0
10
20
30
40
50
2003 2008 2010 2015 2020
Billio
ns o
f Dev
ices
World Population
50 Billion
SmartObjects Rapid adoption rate of digital infrastructure 5 x faster than electricity & telephony
“~6 things online” per person
Inflection Point
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The Data Aggregation Challenge
1.1 Billion Data points generated by sensors daily 500 Gigabytes
Data generated by an offshore oil rig weekly
1000 Gigabytes Data generated by an oil refinery daily 10,000 Gigabytes
Data generated by a jet engine every 30 minutes
2.5 Billion Gigabytes Data generated worldwide daily
90% of the world’s data Has been created in the last 2 years!
10110101100111
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From To
IoT Architectural Philosophy Standardized Networks
(IP Based/ISO Stack)
Distributed Intelligence via Fog Computing (support for IP and non-IP)
Standardized Interfaces (Wireless/Wired)
Protocol Gateways (Inherently complex,
inefficient and fragmented networks)
Closed Systems (Little external interaction)
Proprietary Networks (Usually layer 2 based)
Various Protocols (Modbus, SCADA, BACnet,
LON, HART)
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Source: Cisco Advanced Research & Engineering
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IoT Requires Distributed Computing
ENDPOINT
DATACENTER/CLOUD
Traditional Computing Model (Terminal-mainframe, Client-server, Web)
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IoT Requires Distributed Computing
DEVICE
DATACENTER/CLOUD
IoT Computing Model (Data Volume, Security, Resiliency, Latency)
FOG
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Paradigm Shift with Fog
Unified Platform
Network Compute Storage
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Paradigm Shift with Fog
Unified Platform
Network Compute Storage
CLOUD
STORE ANALYZE ACT NOTIFY
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Paradigm Shift with Fog
Unified Platform
Network Compute Storage
CLOUD CLOUD EDGE
STORE ANALYZE ACT NOTIFY
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§ Edge location, low latency and location & context awareness § Wide-spread geographic distribution
§ Very large number of nodes § Predominant role of wireless access
§ Real time analytics & control close to source
§ Heterogeneity – different form factors, different environments
Fog Computing Defining Characteristics
Extends the Cloud Computing paradigm to the network edge Enables a new breed of applications and services Provides distributed compute, storage and network services
IoTA
pplic
atio
ns
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Hierarchical Fog Architecture
Cloud
Device/Smart Object
North/South Flows East/West Flows Fog
Fog Nodes can be multi-tenant Shared, public or private (like cloud)
Highly virtualised environment Secured & isolated tenants, QoS, workload distribution
Mixed ownership & operation Single entity, federation of agencies
Service Mobility Ability to migrate a running instance from cloud to edge
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Fog Node Architecture
App
Orchestration APIs
Abstraction
Compute Network Storage
App App App App App Fog Applications Various user developed apps on host O/S
Service Orchestration Service management for subscribers, open API to apps, SDN Proximity Engine – redirection to a closer service instance Policy Engine - Implements tenant business policies Matching Engine – Matches capabilities to a service instance
Heterogeneous platform Various form factors, host O/S and service capabilities (storage, RAM….)
Hardware Abstraction Layer Provides uniform interface to compute, network, storage resources Provides resource isolation for different tenants (multi-tenancy) Supports virtualisation (Thin Hypervisor) multiple O/S on physical machine
Matching Engine
Distributed Control
Data
Policy Engine
Proximity Engine
Provisioning Engine
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Fog Computing Example Use Cases
Source: Rodolfo Milito, FogDoc-use-cases 2013
Smart Traffic Lights Real-time (RT) local control loop
Geo-distributed orchestration Multiagency policy co-ordination
Local/Global Analytics
L G C O
Wind Farm RT local control loop In-situ orchestration
Global Big Data
L G C
Connected Rail Two-tier wireless AP
Fast mobility Low latency streaming RT actionable analytics
Global big data
M L G C O
Retailing Video analytics
Interplay between local and Globally process data
L C
M L G C O Mobility Geo-distribution Low/predictable latency Cloud interaction Multi-agent orchestration
Oil & Gas RT actionable analytics
Geo-distributed Orchestration Industrial automation, Big data
M L G C
SCV & Transport RT actionable analytics
Global Big Data (batch processing)
M L C
Military Apps Real-time local control loop
Geo-distributed Orchestration Multiagency policy co-ordination
Local/Global Analytics
M L G C O
Critical attributes
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Introduction • Real-time Operational
Intelligence • Founded in 1980 • 15,000 installs in 110
countries. • Utilities, O&G, Mining,
Manufacturing, Energy, Data Center
• www.osisoft.com • Stewart Young, GAM
Assets
Enterprise Infrastructure
Connect the right data to the right people in the right context for the right decisions in real-time
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IOx
Hardened Edge Platforms: Embedded Storage and Compute
IOS Linux / Other OS
Distributed Applications
IOx SDK
Application Management Application Store
Cisco IOx
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IOx
Hardened Edge Platforms: Embedded Storage and Compute
IOS Distributed Applications
IOx SDK / Linux OS
Application Management Application Store
PI System on Cisco IOx Platform
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PI Server on Cisco UCS
1. Deploy Connectors on CGR/ISR Router
2. Deploy new PI Connectors on CGR/ISR Routers
3. Updates and upgrades to the CGR/ISR Routers
4. Maintenance and
Security through Cisco App Management framework
Cisco IOx On CGR/ISR
What Architectures does Cisco IOx enable?
Cisco IOx On CGR/ISR
Cisco IOx On CGR/ISR
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Value Proposition – OSIsoft & Cisco This technology helps PI interface to be close to the data source (field devices in a rugged environment). Ø Ability to buffer data due to loss of communication.
Ø Ability to filter/throttle data at source and send meaningful data to control center.
Ø Eliminate separate hardware and software (operation system) costs for PI interface node.
Ø Ability to collect data from edge devices, collect data at remote sites which not telemetered, increased visibility in to remote assets.
Ø Higher security due to enclosed case (CGR 1240 model) .
Ø Auto discovery of field devices (points/assets), auto creation of assets (PI AF), auto creation of event frames (PI EF) ability to correlate separate data streams in case of event /disturbance in grid.
Ø Ability to publish data from PI interface to cloud and multiple entities subscribe for data (utility, manufacture for warranty, academia, etc.,.)
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Proactive Network & Asset Management: Mobile Asset
Operator clicks on the Asset on GIS display
Further information is available - single click to access
Real-time information from the PI System for this
Asset is displayed
Cisco IOx On CGR/ISR
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PI Data Collection from Service Area Sources
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§ Multi-Service Field Area Network (FAN)
§ PI Data Connectors pole data on usage and generation for Utility
Utility Example: EV Charging Stations & Solar Arrays
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PI Data Collection from Service Operational Sources
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§ IoT requires rapid processing of significant amounts of data § This capability will be crucial for Operational technologies (OT) § Not necessarily consumer IoT devices (Home weather stations etc…)
§ Close proximity of decision point to IoT devices is essential
§ Cloud infrastructures generally not suitable due to distance § Introduces unacceptable processing latency
§ Fog allows compute, storage and analytics at the network edge § Provides speed, agility, customisation and resiliency
Fog Computing Summary
Thank you.
For more information: http://www.cisco.com/web/tomorrow-starts-here OSIsoft: http://www.osisoft.com