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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19

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